diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..5d11dd945a5d811af0f0b6bf04bfe436d7fc28f4 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.df filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..1bb9b2d08905b446e4cbbcc1a088e5b30c7d5b6c
--- /dev/null
+++ b/README.md
@@ -0,0 +1,65 @@
+# iLoRA
+
+#### Preparation
+
+1. Prepare the environment:
+
+```python
+git clone
+cd iLoRA
+pip install -r requirements.txt
+```
+
+2. Prepare the pre-trained huggingface model of Llama2-7B (https://huggingface.co/meta-llama/Llama-2-7b-hf).
+3. Modify the paths inside the .sh file.
+
+
+#### Train iLoRA
+
+Train iLoRA with a single A100 GPU on MovieLens dataset:
+
+```python
+sh train_movielens.sh
+```
+
+Train iLoRA with a single A100 GPU on Steam dataset:
+
+```
+sh train_steam.sh
+```
+
+Train iLoRA with a single A100 GPU on LastFM dataset:
+
+```
+sh train_lastfm.sh
+```
+
+Note that: set the `llm_path` argument with your own directory path of the Llama2 model.
+
+##### For the environmental issues mentioned by everyone during the reproduction process, we have attempted to help resolve them and have listed some solutions:
+
+If you encounter an error with your transformers/generation/utils.py file, please replace it with the debug/utils.py file we have provided in your environment.
+
+If you encounter an error with your transformers/models/llama/modeling_llama.py file, please replace it with the debug/modeling_llama.py file.
+
+Thank you all for your attention to our work! Wishing you success in your research.
+
+##### Evaluate iLoRA
+
+Test iLoRA with a single A100 GPU on MovieLens dataset:
+
+```
+sh test_movielens.sh
+```
+
+Test iLoRA with a single A100 GPU on Steam dataset:
+
+```
+sh test_steam.sh
+```
+
+Test iLoRA with a single A100 GPU on LastFM dataset:
+
+```
+sh test_lastfm.sh
+```
diff --git a/SASRecModules_ori.py b/SASRecModules_ori.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ac3c975a58d43ee866b637ee5d4f99534974a7b
--- /dev/null
+++ b/SASRecModules_ori.py
@@ -0,0 +1,95 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+
+class PositionwiseFeedForward(nn.Module):
+    def __init__(self, d_in, d_hid, dropout=0.1):
+        super().__init__()
+        self.w_1 = nn.Conv1d(d_in, d_hid, 1)
+        self.w_2 = nn.Conv1d(d_hid, d_in, 1)
+        self.layer_norm = nn.LayerNorm(d_in)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        residual = x
+        output = x.transpose(1, 2)
+        output = self.w_2(F.relu(self.w_1(output)))
+        output = output.transpose(1, 2)
+        output = self.dropout(output)
+        output = self.layer_norm(output + residual)
+        return output
+
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, hidden_size, num_units, num_heads, dropout_rate):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        assert hidden_size % num_heads == 0
+        
+        self.linear_q = nn.Linear(hidden_size, num_units)
+        self.linear_k = nn.Linear(hidden_size, num_units)
+        self.linear_v = nn.Linear(hidden_size, num_units)
+        self.dropout = nn.Dropout(dropout_rate)
+        self.softmax = nn.Softmax(dim=-1)
+
+
+    def forward(self, queries, keys):
+        """
+        :param queries: A 3d tensor with shape of [N, T_q, C_q]
+        :param keys: A 3d tensor with shape of [N, T_k, C_k]
+        
+        :return: A 3d tensor with shape of (N, T_q, C)
+        
+        """
+        Q = self.linear_q(queries)  # (N, T_q, C)
+        K = self.linear_k(keys)  # (N, T_k, C)
+        V = self.linear_v(keys)  # (N, T_k, C)
+        
+        # Split and Concat
+        split_size = self.hidden_size // self.num_heads
+        Q_ = torch.cat(torch.split(Q, split_size, dim=2), dim=0)  # (h*N, T_q, C/h)
+        K_ = torch.cat(torch.split(K, split_size, dim=2), dim=0)  # (h*N, T_k, C/h)
+        V_ = torch.cat(torch.split(V, split_size, dim=2), dim=0)  # (h*N, T_k, C/h)
+        
+        # Multiplication
+        matmul_output = torch.bmm(Q_, K_.transpose(1, 2)) / self.hidden_size ** 0.5  # (h*N, T_q, T_k)
+        
+        # Key Masking
+        key_mask = torch.sign(torch.abs(keys.sum(dim=-1))).repeat(self.num_heads, 1)  # (h*N, T_k)
+        key_mask_reshaped = key_mask.unsqueeze(1).repeat(1, queries.shape[1], 1)  # (h*N, T_q, T_k)
+        key_paddings = torch.ones_like(matmul_output) * (-2 ** 32 + 1)
+        matmul_output_m1 = torch.where(torch.eq(key_mask_reshaped, 0), key_paddings, matmul_output)  # (h*N, T_q, T_k)
+        
+        # Causality - Future Blinding
+        diag_vals = torch.ones_like(matmul_output[0, :, :])   # (T_q, T_k)
+        tril = torch.tril(diag_vals)  # (T_q, T_k)
+        causality_mask = tril.unsqueeze(0).repeat(matmul_output.shape[0], 1, 1)  # (h*N, T_q, T_k)
+        causality_paddings = torch.ones_like(causality_mask) * (-2 ** 32 + 1)
+        matmul_output_m2 = torch.where(torch.eq(causality_mask, 0), causality_paddings, matmul_output_m1)  # (h*N, T_q, T_k)
+        
+        # Activation
+        matmul_output_sm = self.softmax(matmul_output_m2)  # (h*N, T_q, T_k)
+        
+        # Query Masking
+        query_mask = torch.sign(torch.abs(queries.sum(dim=-1))).repeat(self.num_heads, 1)  # (h*N, T_q)
+        query_mask = query_mask.unsqueeze(-1).repeat(1, 1, keys.shape[1])  # (h*N, T_q, T_k)
+        matmul_output_qm = matmul_output_sm * query_mask
+        
+        # Dropout
+        matmul_output_dropout = self.dropout(matmul_output_qm)
+        
+        # Weighted Sum
+        output_ws = torch.bmm(matmul_output_dropout, V_)  # ( h*N, T_q, C/h)
+        
+        # Restore Shape
+        output = torch.cat(torch.split(output_ws, output_ws.shape[0] // self.num_heads, dim=0), dim=2)  # (N, T_q, C)
+        
+        # Residual Connection
+        output_res = output + queries
+        
+        return output_res
+        
\ No newline at end of file
diff --git a/data/_init_.py b/data/_init_.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/data/_init_.py
@@ -0,0 +1 @@
+
diff --git a/data/data_interface.py b/data/data_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f822c628751e5bbef8857a350e5812a4cc510e1
--- /dev/null
+++ b/data/data_interface.py
@@ -0,0 +1,190 @@
+import inspect
+import importlib
+import pickle as pkl
+import pytorch_lightning as pl
+from torch.utils.data import DataLoader
+from torch.utils.data.sampler import WeightedRandomSampler
+
+import random
+import torch
+import argparse
+from transformers import LlamaForCausalLM, LlamaTokenizer
+import os
+
+
+
+class TrainCollater:
+    def __init__(self,
+                 prompt_list=None,
+                 llm_tokenizer=None,
+                 train=False,
+                 terminator="\n",
+                 max_step=1):
+        self.prompt_list = prompt_list
+        self.llm_tokenizer = llm_tokenizer
+        self.train=train
+        self.terminator = terminator
+        self.max_step = max_step
+        self.cur_step = 1
+
+    def __call__(self, batch):
+        if isinstance(self.prompt_list,list):
+            instruction = random.choice(self.prompt_list)
+            inputs_text = instruction if isinstance(instruction, list) else [instruction] * len(batch)
+        else:
+            instruction = sample["instruction_input"] if "instruction_input" in sample else None
+            inputs_text = instruction if isinstance(instruction, list) else [instruction] * len(batch)
+ 
+        thresh_hold = self.cur_step/self.max_step
+        p = random.random()
+        if p < thresh_hold or not self.train:
+            for i, sample in enumerate(batch):
+                input_text=inputs_text[i]
+                if '[HistoryHere]' in input_text:
+                    insert_prompt=", ".join([seq_title+' [HistoryEmb]' for seq_title in sample['seq_name']])
+                    input_text=input_text.replace('[HistoryHere]',insert_prompt)
+                if '[CansHere]' in input_text:
+                    insert_prompt=", ".join([can_title+' [CansEmb]' for can_title in sample['cans_name']])
+                    input_text=input_text.replace('[CansHere]',insert_prompt)   
+                if '[TargetHere]' in input_text:
+                    insert_prompt=insert_prompt=", ".join([sample['correct_answer']+' [ItemEmb]'])
+                    input_text=input_text.replace('[TargetHere]',insert_prompt)  
+                
+                inputs_text[i]=input_text
+            flag = False
+        else:
+            for i, sample in enumerate(batch):
+                input_text=inputs_text[i]
+                if '[HistoryHere]' in input_text:
+                    insert_prompt=", ".join([seq_title+' [PH]' for seq_title in sample['seq_name']])
+                    input_text=input_text.replace('[HistoryHere]',insert_prompt)
+                if '[CansHere]' in input_text:
+                    insert_prompt=", ".join([can_title+' [PH]' for can_title in sample['cans_name']])
+                    input_text=input_text.replace('[CansHere]',insert_prompt)   
+                
+                inputs_text[i]=input_text
+            flag = True
+        self.cur_step += 1
+        
+        targets_text = [sample['correct_answer'] for sample in batch]
+
+        if self.train:
+            targets_text=[target_text+self.terminator for target_text in targets_text]
+            inputs_pair = [[p, t] for p, t in zip(inputs_text, targets_text)]
+
+            batch_tokens = self.llm_tokenizer(
+                inputs_pair,
+                return_tensors="pt",
+                padding="longest",
+                truncation=False,
+                add_special_tokens=True,
+                return_attention_mask=True,
+                return_token_type_ids=True)
+            new_batch={"tokens":batch_tokens,
+                       "seq":torch.stack([torch.tensor(sample['seq']) for sample in batch], dim=0),
+                       "cans":torch.stack([torch.tensor(sample['cans']) for sample in batch], dim=0),
+                       "len_seq":torch.stack([torch.tensor(sample['len_seq']) for sample in batch], dim=0),
+                       "len_cans":torch.stack([torch.tensor(sample['len_cans']) for sample in batch], dim=0),
+                       "item_id": torch.stack([torch.tensor(sample['item_id']) for sample in batch], dim=0),
+                       "flag":flag,
+                       }
+            
+            
+        else:
+            batch_tokens = self.llm_tokenizer(
+                inputs_text,
+                return_tensors="pt",
+                padding="longest",
+                truncation=False,
+                add_special_tokens=True,
+                return_token_type_ids=True)
+            cans_name=[sample['cans_name'] for sample in batch]
+            new_batch={"tokens":batch_tokens,
+                       "seq":torch.stack([torch.tensor(sample['seq']) for sample in batch], dim=0),
+                       "cans":torch.stack([torch.tensor(sample['cans']) for sample in batch], dim=0),
+                       "len_seq":torch.stack([torch.tensor(sample['len_seq']) for sample in batch], dim=0),
+                       "len_cans":torch.stack([torch.tensor(sample['len_cans']) for sample in batch], dim=0),
+                       "item_id": torch.stack([torch.tensor(sample['item_id']) for sample in batch], dim=0),
+                       "correct_answer": targets_text,
+                       "cans_name": cans_name,
+            
+                       }
+            
+        return new_batch
+
+class DInterface(pl.LightningDataModule):
+
+    def __init__(self, 
+                 llm_tokenizer=None,
+                 num_workers=8,
+                 dataset='',
+                 **kwargs):
+        super().__init__()
+        self.num_workers = num_workers
+        self.llm_tokenizer=llm_tokenizer
+        self.dataset = dataset
+        self.kwargs = kwargs
+        self.batch_size = kwargs['batch_size']
+        self.max_epochs = kwargs['max_epochs']
+        self.load_data_module()
+        self.load_prompt(kwargs['prompt_path'])
+
+        self.trainset = self.instancialize(stage='train')
+        self.valset = self.instancialize(stage='val')
+        self.testset = self.instancialize(stage='test')
+        self.max_steps = self.max_epochs*(len(self.trainset)//self.batch_size)//self.num_workers
+
+    def train_dataloader(self):
+        return DataLoader(self.trainset,
+                          batch_size=self.batch_size, 
+                          num_workers=self.num_workers, 
+                          shuffle=True,
+                          drop_last=True,
+                          collate_fn=TrainCollater(prompt_list=self.prompt_list,llm_tokenizer=self.llm_tokenizer,train=True, max_step=self.max_steps))
+
+    def val_dataloader(self):
+        return DataLoader(self.valset, 
+                          batch_size=self.batch_size, 
+                          num_workers=self.num_workers, 
+                          shuffle=False,
+                          collate_fn=TrainCollater(prompt_list=self.prompt_list,llm_tokenizer=self.llm_tokenizer,train=False))
+
+    def test_dataloader(self):
+        return DataLoader(self.testset, 
+                          batch_size=self.batch_size, 
+                          num_workers=self.num_workers, 
+                          shuffle=False,
+                          
+                          drop_last=True,
+                          
+                          collate_fn=TrainCollater(prompt_list=self.prompt_list,llm_tokenizer=self.llm_tokenizer,train=False))
+
+    def load_data_module(self):
+        name = self.dataset
+        camel_name = ''.join([i.capitalize() for i in name.split('_')])
+        try:
+            self.data_module = getattr(importlib.import_module(
+                '.'+name, package=__package__), camel_name)
+        except:
+            raise ValueError(
+                f'Invalid Dataset File Name or Invalid Class Name data.{name}.{camel_name}')
+
+    def instancialize(self, **other_args):
+        class_args = inspect.getargspec(self.data_module.__init__).args[1:]
+        inkeys = self.kwargs.keys()
+        args1 = {}
+        for arg in class_args:
+            if arg in inkeys:
+                args1[arg] = self.kwargs[arg]
+        args1.update(other_args)
+        return self.data_module(**args1)
+    
+    def load_prompt(self,prompt_path):
+        if os.path.isfile(prompt_path):
+            with open(prompt_path, 'r') as f:
+                raw_prompts = f.read().splitlines()
+            self.prompt_list = [p.strip() for p in raw_prompts]
+            print('Load {} training prompts'.format(len(self.prompt_list)))
+            print('Prompt Example \n{}'.format(random.choice(self.prompt_list)))
+        else:
+            self.prompt_list = []
diff --git a/data/lastfm_data.py b/data/lastfm_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..54a7518dc223ff4ee6d7e9b84ab86a43a4d9ebd1
--- /dev/null
+++ b/data/lastfm_data.py
@@ -0,0 +1,87 @@
+import torch
+import os.path as op
+import numpy as np
+import pickle as pkl
+import torch.utils.data as data
+
+import pandas as pd
+import random
+
+class LastfmData(data.Dataset):
+    def __init__(self, data_dir=r'data/ref/lastfm_ctr',
+                 stage=None,
+                 cans_num=10,
+                 sep=", ",
+                 no_augment=True):
+        self.__dict__.update(locals())
+        self.aug = (stage=='train') and not no_augment
+        self.padding_item_id=4606
+        self.check_files()
+
+    def __len__(self):
+        return len(self.session_data['seq'])
+
+    def __getitem__(self, i):
+        temp = self.session_data.iloc[i]
+        candidates = self.negative_sampling(temp['seq_unpad'],temp['next'])
+        cans_name=[self.item_id2name[can] for can in candidates]
+        sample = {
+            'seq': temp['seq'],
+            'seq_name': temp['seq_title'],
+            'len_seq': temp['len_seq'],
+            'seq_str': self.sep.join(temp['seq_title']),
+            'cans': candidates,
+            'cans_name': cans_name,
+            'cans_str': self.sep.join(cans_name),
+            'len_cans': self.cans_num,
+            'item_id': temp['next'],
+            'item_name': temp['next_item_name'],
+            'correct_answer': temp['next_item_name']
+        }
+        return sample
+
+    def negative_sampling(self,seq_unpad,next_item):
+        canset=[i for i in list(self.item_id2name.keys()) if i not in seq_unpad and i!=next_item]
+        candidates=random.sample(canset, 1)
+        return candidates
+
+    def check_files(self):
+        self.item_id2name=self.get_music_id2name()
+        if self.stage=='train':
+            filename="train_data.df"
+        elif self.stage=='val':
+            filename="Val_data.df"
+        elif self.stage=='test':
+            filename="Test_data.df"
+        data_path=op.join(self.data_dir, filename)
+        self.session_data = self.session_data4frame(data_path, self.item_id2name)  
+
+
+    def get_music_id2name(self):
+        music_id2name = dict()
+        item_path=op.join(self.data_dir, 'id2name.txt')
+        with open(item_path, 'r') as f:
+            for l in f.readlines():
+                ll = l.strip('\n').split('::')
+                music_id2name[int(ll[0])] = ll[1].strip()
+        return music_id2name
+
+    def session_data4frame(self, datapath, music_id2name):
+        train_data = pd.read_pickle(datapath)
+        train_data = train_data[train_data['len_seq'] >= 3]
+        def remove_padding(xx):
+            x = xx[:]
+            for i in range(10):
+                try:
+                    x.remove(self.padding_item_id)
+                except:
+                    break
+            return x
+        train_data['seq_unpad'] = train_data['seq'].apply(remove_padding)
+        def seq_to_title(x): 
+            return [music_id2name[x_i] for x_i in x]
+        train_data['seq_title'] = train_data['seq_unpad'].apply(seq_to_title)
+        def next_item_title(x): 
+            return music_id2name[x]
+        train_data['next_item_name'] = train_data['next'].apply(next_item_title)
+        return train_data
\ No newline at end of file
diff --git a/data/movielens_data.py b/data/movielens_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bf0e156a215b16f0d6328a1c12fcb30a0848252
--- /dev/null
+++ b/data/movielens_data.py
@@ -0,0 +1,102 @@
+import torch
+import os.path as op
+import numpy as np
+import pickle as pkl
+import torch.utils.data as data
+
+import pandas as pd
+import random
+
+class MovielensData(data.Dataset):
+    def __init__(self, data_dir=r'data/ref/movielens',
+                 stage=None,
+                 cans_num=10,
+                 sep=", ",
+                 no_augment=True):
+        self.__dict__.update(locals())
+        self.aug = (stage=='train') and not no_augment
+        self.padding_item_id=1682
+        self.padding_rating=0
+        self.check_files()
+
+    def __len__(self):
+        return len(self.session_data['seq'])
+
+    def __getitem__(self, i):
+        temp = self.session_data.iloc[i]
+        candidates = self.negative_sampling(temp['seq_unpad'],temp['next'])
+        cans_name=[self.item_id2name[can] for can in candidates]
+        sample = {
+            'seq': temp['seq'],
+            'seq_name': temp['seq_title'],
+            'len_seq': temp['len_seq'],
+            'seq_str': self.sep.join(temp['seq_title']),
+            'cans': candidates,
+            'cans_name': cans_name,
+            'cans_str': self.sep.join(cans_name),
+            'len_cans': self.cans_num,
+            'item_id': temp['next'],
+            'item_name': temp['next_item_name'],
+            'correct_answer': temp['next_item_name']
+        }
+        return sample
+    
+    def negative_sampling(self,seq_unpad,next_item):
+        canset=[i for i in list(self.item_id2name.keys()) if i not in seq_unpad and i!=next_item]
+        candidates=random.sample(canset, self.cans_num-1)+[next_item]
+        random.shuffle(candidates)
+        return candidates  
+
+    def check_files(self):
+        self.item_id2name=self.get_movie_id2name()
+        if self.stage=='train':
+            filename="train_data.df"
+        elif self.stage=='val':
+            filename="Val_data.df"
+        elif self.stage=='test':
+            filename="Test_data.df"
+        data_path=op.join(self.data_dir, filename)
+        self.session_data = self.session_data4frame(data_path, self.item_id2name)  
+    
+    def get_mv_title(self,s):
+        sub_list=[", The", ", A", ", An"]
+        for sub_s in sub_list:
+            if sub_s in s:
+                return sub_s[2:]+" "+s.replace(sub_s,"")
+        return s
+
+    def get_movie_id2name(self):
+        movie_id2name = dict()
+        item_path=op.join(self.data_dir, 'u.item')
+        with open(item_path, 'r', encoding = "ISO-8859-1") as f:
+            for l in f.readlines():
+                ll = l.strip('\n').split('|')
+                movie_id2name[int(ll[0]) - 1] = self.get_mv_title(ll[1][:-7])
+        return movie_id2name
+    
+    def session_data4frame(self, datapath, movie_id2name):
+        train_data = pd.read_pickle(datapath)
+        train_data = train_data[train_data['len_seq'] >= 3]
+        def remove_padding(xx):
+            x = xx[:]
+            for i in range(10):
+                try:
+                    x.remove((self.padding_item_id,self.padding_rating))
+                except:
+                    break
+            return x
+        train_data['seq_unpad'] = train_data['seq'].apply(remove_padding)
+        def seq_to_title(x): 
+            return [movie_id2name[x_i[0]] for x_i in x]
+        train_data['seq_title'] = train_data['seq_unpad'].apply(seq_to_title)
+        def next_item_title(x): 
+            return movie_id2name[x[0]]
+        train_data['next_item_name'] = train_data['next'].apply(next_item_title)
+        def get_id_from_tumple(x):
+            return x[0]
+        def get_id_from_list(x):
+            return [i[0] for i in x]
+        train_data['next'] = train_data['next'].apply(get_id_from_tumple)
+        train_data['seq'] = train_data['seq'].apply(get_id_from_list)
+        train_data['seq_unpad']=train_data['seq_unpad'].apply(get_id_from_list)
+        return train_data
\ No newline at end of file
diff --git a/data/ref/lastfm/Test_data.df b/data/ref/lastfm/Test_data.df
new file mode 100644
index 0000000000000000000000000000000000000000..e4a9572f55d2284fc99b973fee2f36158de5c815
--- /dev/null
+++ b/data/ref/lastfm/Test_data.df
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cac058a2842b2a3ed4d39ed1fa0a742f5034a23c9851a2ffc157b4845d703570
+size 6673
diff --git a/data/ref/lastfm/Val_data.df b/data/ref/lastfm/Val_data.df
new file mode 100644
index 0000000000000000000000000000000000000000..9947214a5ba6e8b05024a059dc2668de2de1aa10
--- /dev/null
+++ b/data/ref/lastfm/Val_data.df
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92a1ddb65ebdeff999f910b65660eb3d8c7cd85e6a19308d80c429481839f8b3
+size 6690
diff --git a/data/ref/lastfm/id2name.txt b/data/ref/lastfm/id2name.txt
new file mode 100644
index 0000000000000000000000000000000000000000..51c03fbb6ce96ceebaf8357d82b0b9402be1d4cc
--- /dev/null
+++ b/data/ref/lastfm/id2name.txt
@@ -0,0 +1,4606 @@
+0::Morcheeba
+1::Enigma
+2::Café Del Mar
+3::Fleetwood Mac
+4::China Crisis
+5::Loscil
+6::Chicane
+7::Sigue Sigue Sputnik
+8::Duran Duran
+9::Air
+10::Röyksopp
+11::Moby
+12::Depeche Mode
+13::Groove Armada
+14::INXS
+15::Deep Forest
+16::Porcupine Tree
+17::De/Vision
+18::Radiohead
+19::VAST
+20::Michael Jackson
+21::God Is an Astronaut
+22::Pink Floyd
+23::Planet Funk
+24::Scissor Sisters
+25::Mew
+26::Stereophonics
+27::Placebo
+28::Infected Mushroom
+29::Delerium
+30::Roxette
+31::Paradise Lost
+32::Jamiroquai
+33::James Blunt
+34::Reamonn
+35::Opeth
+36::Elton John
+37::Tears for Fears
+38::Death Cab for Cutie
+39::Savage Garden
+40::Darren Hayes
+41::Explosions in the Sky
+42::AC/DC
+43::Apocalyptica
+44::Megadeth
+45::Dream Theater
+46::Tiësto
+47::The Darkness
+48::VNV Nation
+49::Thirteen Senses
+50::Dave Gahan
+51::Apparat
+52::Def Leppard
+53::Zero 7
+54::Dope
+55::Audioslave
+56::Charon
+57::Amorphis
+58::Pain
+59::Sentenced
+60::Blank & Jones
+61::Mesh
+62::Keane
+63::Muse
+64::Bright Eyes
+65::Tom Waits
+66::Gogol Bordello
+67::CAKE
+68::Jeff Buckley
+69::Sparklehorse
+70::Modest Mouse
+71::Sunset Rubdown
+72::Nine Inch Nails
+73::Clap Your Hands Say Yeah
+74::Manu Chao
+75::Belle and Sebastian
+76::John Lennon
+77::Primal Scream
+78::The Velvet Underground
+79::Bon Jovi
+80::Johann Sebastian Bach
+81::Weezer
+82::Imogen Heap
+83::Janis Joplin
+84::Oh No Oh My
+85::The Cramps
+86::Panda Bear
+87::Gavin DeGraw
+88::Wilco
+89::Ben Kweller
+90::Everlast
+91::Menomena
+92::Turin Brakes
+93::DIR EN GREY
+94::My Chemical Romance
+95::Travis
+96::The Killers
+97::Tina Turner
+98::Katatonia
+99::Skillet
+100::宇多田ヒカル
+101::浜崎あゆみ
+102::UVERworld
+103::Linkin Park
+104::中島美嘉
+105::GACKT
+106::L'Arc~en~Ciel
+107::雅-MIYAVI-
+108::The All-American Rejects
+109::The Rasmus
+110::HYDE
+111::Simple Plan
+112::30 Seconds to Mars
+113::Three Days Grace
+114::Seether
+115::Lostprophets
+116::HIM
+117::Korn
+118::Within Temptation
+119::Shakira
+120::Epica
+121::Nickelback
+122::Noir Désir
+123::Tool
+124::Hawthorne Heights
+125::Snow Patrol
+126::Jimmy Eat World
+127::Ricky Martin
+128::Ryan Adams
+129::Shayne Ward
+130::Sting
+131::Sex Pistols
+132::Hoobastank
+133::They Might Be Giants
+134::Rie fu
+135::ギルガメッシュ
+136::ムック
+137::Il Divo
+138::Josh Groban
+139::倖田來未
+140::Type O Negative
+141::Lonestar
+142::KT Tunstall
+143::Vanessa Carlton
+144::Hinder
+145::Phil Collins
+146::Melt-Banana
+147::Goo Goo Dolls
+148::Krypteria
+149::Paula DeAnda
+150::Saliva
+151::Seether (Feat. Amy Lee)
+152::Puddle of Mudd
+153::Staind
+154::Royal Hunt
+155::Dream Evil
+156::Olivia
+157::Juno Reactor
+158::Polysics
+159::O-Zone
+160::Mark Lanegan
+161::12012
+162::Sadie
+163::Sami Yusuf
+164::Massari
+165::Saybia
+166::Beirut
+167::Arcade Fire
+168::Babyshambles
+169::The Decemberists
+170::The National
+171::Sigur Rós
+172::The Libertines
+173::Kate Nash
+174::Wolf Parade
+175::Animal Collective
+176::The Fray
+177::The Shins
+178::Lady Gaga
+179::Green Day
+180::Nirvana
+181::Mariah Carey
+182::JoJo
+183::Backstreet Boys
+184::Jesse McCartney
+185::Justin Bieber
+186::Usher
+187::Nick Carter
+188::Metro Station
+189::Jeremih
+190::Fall Out Boy
+191::Kristinia DeBarge
+192::Paris Hilton
+193::50 Cent
+194::New Kids on the Block
+195::Дима Билан
+196::London After Midnight
+197::Psyclon Nine
+198::The Crüxshadows
+199::KMFDM
+200::Mindless Self Indulgence
+201::Daft Punk
+202::Goldfrapp
+203::Madonna
+204::Dido
+205::Gorillaz
+206::The Cure
+207::Poets of the Fall
+208::OneRepublic
+209::System of a Down
+210::The Beatles
+211::Thom Yorke
+212::Massive Attack
+213::2Pac
+214::Rihanna
+215::Britney Spears
+216::Jennifer Lopez
+217::Katy Perry
+218::P!nk
+219::Black Eyed Peas
+220::Hilary Duff
+221::Fergie
+222::Ashley Tisdale
+223::Chris Brown
+224::Kanye West
+225::Avril Lavigne
+226::Taylor Swift
+227::The Pussycat Dolls
+228::Evanescence
+229::Akira Yamaoka
+230::blink-182
+231::Bat for Lashes
+232::The Smashing Pumpkins
+233::3OH!3
+234::Ke$ha
+235::Craig David
+236::Eminem
+237::Ne-Yo
+238::The Birthday Massacre
+239::Limp Bizkit
+240::Fort Minor
+241::Funeral for a Friend
+242::Finch
+243::Cold
+244::Papa Roach
+245::Paramore
+246::Flyleaf
+247::Bullet for My Valentine
+248::Angels & Airwaves
+249::LaFee
+250::In Flames
+251::Anathema
+252::Saosin
+253::Tracktor Bowling
+254::Pitchshifter
+255::P.O.D.
+256::U2
+257::Deftones
+258::Ill Niño
+259::Clan of Xymox
+260::Celldweller
+261::Static-X
+262::Fluke
+263::happysad
+264::Lacrimosa
+265::Lindsay Lohan
+266::Good Charlotte
+267::The Used
+268::Maroon 5
+269::Adam Lambert
+270::Kid Cudi
+271::Owl City
+272::The Pretty Reckless
+273::Metallica
+274::#####
+275::Fear Factory
+276::Rise Against
+277::Killswitch Engage
+278::Jane's Addiction
+279::Tiamat
+280::Nightwish
+281::Sixpence None the Richer
+282::t.A.T.u.
+283::Disturbed
+284::Jem
+285::Kerli
+286::Bruno Mars
+287::Slipknot
+288::Myslovitz
+289::Björk
+290::Rammstein
+291::A Perfect Circle
+292::Joe Satriani
+293::Stone Sour
+294::Editors
+295::Billy Talent
+296::Silverstein
+297::The Red Jumpsuit Apparatus
+298::Alesana
+299::Senses Fail
+300::Story of the Year
+301::Tactical Sekt
+302::Sepultura
+303::The Offspring
+304::Akon
+305::B.o.B
+306::Black Rebel Motorcycle Club
+307::Lykke Li
+308::Jay-Z
+309::Black Veil Brides
+310::Tokio Hotel
+311::Oomph!
+312::Guano Apes
+313::Red
+314::Breaking Benjamin
+315::Thousand Foot Krutch
+316::Jay-Z and Linkin Park
+317::Hollywood Undead
+318::Cypress Hill
+319::Lacuna Coil
+320::September
+321::Gregorian
+322::OK Go
+323::Fightstar
+324::Godsmack
+325::Keith Urban
+326::Rascal Flatts
+327::Brooks & Dunn
+328::Scooter
+329::Thursday
+330::Bloodhound Gang
+331::Robert Miles
+332::Automatic Loveletter
+333::Underoath
+334::Lupe Fiasco
+335::Dead by April
+336::Adema
+337::Dead by Sunrise
+338::Faktion
+339::We Are the Fallen
+340::Coma
+341::Drowning Pool
+342::Drake
+343::Sky Ferreira
+344::The Magic Numbers
+345::Mary Elizabeth McGlynn
+346::Brand New
+347::Will Smith
+348::Daughtry
+349::12 Stones
+350::Ferry Corsten
+351::Silversun Pickups
+352::Scary Kids Scaring Kids
+353::Xandria
+354::Kill Hannah
+355::Taking Back Sunday
+356::Hayden Panettiere
+357::Линда
+358::Smile Empty Soul
+359::Jason Derülo
+360::O.N.A.
+361::Chylińska
+362::Alexandre Desplat
+363::Emilie Autumn
+364::Fuel
+365::Stateless
+366::Robert Pattinson
+367::Trading Yesterday
+368::Armor for Sleep
+369::Bear McCreary
+370::I Am Ghost
+371::Xzibit
+372::Helios
+373::Emery
+374::Alexi Murdoch
+375::Anya Marina
+376::Mudvayne
+377::Chingy
+378::Plumb
+379::Taproot
+380::Jason Walker
+381::Mark Ronson
+382::Gavin Rossdale
+383::Blindside
+384::Amy Lee
+385::James Newton Howard
+386::Band of Skulls
+387::Sea Wolf
+388::The Letter Black
+389::Ja Rule
+390::Holly Brook
+391::Hurricane Bells
+392::Kylie Minogue
+393::Ashlee Simpson
+394::Lily Allen
+395::Heidi Montag
+396::New Order
+397::Japan
+398::The Smiths
+399::Joy Division
+400::The Sonics
+401::Southern Culture on the Skids
+402::Small Faces
+403::The Detroit Cobras
+404::The Pretty Things
+405::Talking Heads
+406::The Stranglers
+407::The Clash
+408::Roxy Music
+409::Alphaville
+410::Blondie
+411::Frank Sinatra
+412::The Human League
+413::Information Society
+414::Pet Shop Boys
+415::Ultravox
+416::Yazoo
+417::Heaven 17
+418::Siouxsie and the Banshees
+419::Misfits
+420::Dean Martin
+421::Dick Dale
+422::Motörhead
+423::Devo
+424::Split Enz
+425::The Cars
+426::Dead or Alive
+427::Oingo Boingo
+428::Eurythmics
+429::Visage
+430::Magazine
+431::Madness
+432::Ska-P
+433::The Specials
+434::Culture Club
+435::Elvis Costello
+436::Sublime
+437::A Flock of Seagulls
+438::Echo & The Bunnymen
+439::Cab Calloway
+440::The B-52's
+441::Butthole Surfers
+442::Nekromantix
+443::The Byrds
+444::The Mighty Mighty Bosstones
+445::Peggy Lee
+446::Corona
+447::The Jam
+448::Joe Jackson
+449::Messer Chups
+450::The Meteors
+451::Adam and the Ants
+452::The Zombies
+453::Barón Rojo
+454::Missing Persons
+455::Squeeze
+456::Akurat
+457::The Brian Setzer Orchestra
+458::Stray Cats
+459::Goldfinger
+460::Mecano
+461::Men at Work
+462::Squirrel Nut Zippers
+463::Harry Connick, Jr.
+464::Dick Dale and His Del-Tones
+465::The Kings of Nuthin'
+466::B-Movie
+467::Anne Clark
+468::Mad Sin
+469::Calabrese
+470::The Seeds
+471::The Litter
+472::Big Bad Voodoo Daddy
+473::13th Floor Elevators
+474::Holly Golightly
+475::IRA
+476::The Troggs
+477::Duffy
+478::Allison Iraheta
+479::Lena
+480::Jónsi
+481::Kalomoira
+482::Άννα Βισση
+483::Inna
+484::Jewel
+485::Fresno
+486::Hurts
+487::Miranda Cosgrove
+488::Wanessa
+489::The Script
+490::Alexander Rybak
+491::Cinema Bizarre
+492::Edyta Górniak
+493::ZAZ
+494::Cary Brothers
+495::Karl Wolf
+496::Boris
+497::Fever Ray
+498::Fennesz
+499::I Set My Friends On Fire
+500::Blue
+501::Jessica Simpson
+502::RBD
+503::Mandy Moore
+504::Paulina Rubio
+505::Fey
+506::Dannii Minogue
+507::Thalía
+508::A*Teens
+509::Geri Halliwell
+510::Atomic Kitten
+511::Andrés Calamaro
+512::Laibach
+513::David Bowie
+514::Einstürzende Neubauten
+515::Jesu
+516::Soundgarden
+517::Stone Temple Pilots
+518::Killing Joke
+519::Bauhaus
+520::Nitzer Ebb
+521::Ladytron
+522::Suede
+523::My Bloody Valentine
+524::Fugazi
+525::Godflesh
+526::Prong
+527::Filter
+528::Lard
+529::Noisettes
+530::VV Brown
+531::Sonique
+532::Robbie Williams
+533::Christina Aguilera
+534::Leona Lewis
+535::Beyoncé
+536::Justin Timberlake
+537::Monrose
+538::Cheryl Cole
+539::Sarah Connor
+540::M. Pokora
+541::Taio Cruz
+542::Girls Aloud
+543::Rachel Stevens
+544::Take That
+545::Iron Maiden
+546::Savatage
+547::Bruce Dickinson
+548::Children of Bodom
+549::Andre Matos
+550::Viper
+551::Matanza
+552::Nevermore
+553::Enya
+554::The Ting Tings
+555::Pearl Jam
+556::Isis
+557::Tori Amos
+558::The Mars Volta
+559::Panic! At the Disco
+560::The Fall of Troy
+561::Dżem
+562::Hard-Fi
+563::Bond
+564::Sophie Ellis-Bextor
+565::Rick Astley
+566::Solar Stone
+567::Ian Van Dahl
+568::Guru Josh Project
+569::Morrissey
+570::Frank Zappa
+571::Plaid
+572::Underworld
+573::Thomas Newman
+574::Junkie XL
+575::Sasha
+576::Glee Cast
+577::All Time Low
+578::Runner Runner
+579::And One
+580::Crystal Castles
+581::Anything Box
+582::Recoil
+583::Hubert Kah
+584::Sally Shapiro
+585::Ayria
+586::Red Flag
+587::The Frozen Autumn
+588::Cause & Effect
+589::Wham!
+590::Billy Idol
+591::Provision
+592::Peter Murphy
+593::Kaizers Orchestra
+594::Kent
+595::Iggy Pop
+596::Yann Tiersen
+597::Rush
+598::Frédéric Chopin
+599::Incubus
+600::Claude Debussy
+601::Faith No More
+602::Wolfgang Amadeus Mozart
+603::John Petrucci & Jordan Rudess
+604::Ludwig van Beethoven
+605::The String Quartet
+606::Yes
+607::Mike Oldfield
+608::Zu
+609::Temple of the Dog
+610::Bob Dylan
+611::Miles Davis
+612::John Coltrane
+613::Thelonious Monk
+614::Florence + the Machine
+615::The Cinematic Orchestra
+616::Yeasayer
+617::Tera Melos
+618::Bill Evans
+619::Animals as Leaders
+620::Arcturus
+621::Clutch
+622::Chet Baker
+623::Madeleine Peyroux
+624::Charlie Parker
+625::Charles Mingus
+626::Herbie Hancock
+627::Pat Metheny
+628::Bring Me The Horizon
+629::Escape The Fate
+630::MALICE MIZER
+631::Marilyn Manson
+632::Coldplay
+633::Nas
+634::Mos Def
+635::Gang Starr
+636::The Roots
+637::A Tribe Called Quest
+638::Sufjan Stevens
+639::Saul Williams
+640::Sunn O)))
+641::Vampire Weekend
+642::Sonic Youth
+643::Masta Ace
+644::De La Soul
+645::Jedi Mind Tricks
+646::ASIAN KUNG-FU GENERATION
+647::MF DOOM
+648::N.W.A
+649::Mike Patton
+650::Mr. Bungle
+651::Public Enemy
+652::Kool G Rap
+653::Talib Kweli
+654::Wu-Tang Clan
+655::Black Kids
+656::Merzbow
+657::Why?
+658::Method Man
+659::Drudkh
+660::Bubba Sparxxx
+661::Del tha Funkee Homosapien
+662::Wolves in the Throne Room
+663::Orgy
+664::Foo Fighters
+665::Combichrist
+666::Grendel
+667::Agonoize
+668::Hocico
+669::Feindflug
+670::Amduscia
+671::Dawn of Ashes
+672::She Wants Revenge
+673::Headscan
+674::Rotersand
+675::Blutengel
+676::Wynardtage
+677::Unheilig
+678::[:SITD:]
+679::L'Âme Immortelle
+680::Terminal Choice
+681::Suicidal Romance
+682::Liquid Divine
+683::Melotron
+684::Evil's Toy
+685::Vibrasphere
+686::Bamboo Forest
+687::Miss Construction
+688::Samsas Traum
+689::Zombie Girl
+690::Nurzery [Rhymes]
+691::Nachtmahr
+692::ASP
+693::Subway to Sally
+694::Caustic
+695::Diary of Dreams
+696::xotox
+697::Tamtrum
+698::Covenant
+699::Icon of Coil
+700::Funker Vogt
+701::God Module
+702::Noisuf-X
+703::Reaper
+704::Portishead
+705::David Guetta
+706::Cascada
+707::Paul van Dyk
+708::ATB
+709::Skeletal Family
+710::Dragonette
+711::Armin van Buuren
+712::Yelle
+713::Kyau vs. Albert
+714::Novaspace
+715::OceanLab
+716::Groove Coverage
+717::Fragma
+718::Sylver
+719::Markus Schulz
+720::IAMX
+721::Hot Chip
+722::Cryo
+723::Front Line Assembly
+724::Haujobb
+725::mind.in.a.box
+726::Absurd Minds
+727::Seabound
+728::Colony 5
+729::Apoptygma Berzerk
+730::Suicide Commando
+731::Neuroticfish
+732::Assemblage 23
+733::Edge of Dawn
+734::Cosmic Gate
+735::Above & Beyond
+736::Pride and Fall
+737::E-Craft
+738::Code 64
+739::Angels & Agony
+740::4 Strings
+741::XP8
+742::Bruderschaft
+743::Syrian
+744::Digitalism
+745::Justice
+746::Armand van Helden
+747::Simian Mobile Disco
+748::MSTRKRFT
+749::The Sisters of Mercy
+750::Holly Valance
+751::The Knife
+752::Porcelain and the Tramps
+753::Dope Stars Inc.
+754::Ashbury Heights
+755::Vive la Fête
+756::Diorama
+757::Belanova
+758::Alice in Videoland
+759::Theatre of Tragedy
+760::ADULT.
+761::Annie
+762::Front 242
+763::Robots in Disguise
+764::Alien Sex Fiend
+765::Christian Death
+766::Helalyn Flowers
+767::Shiny Toy Guns
+768::Claire Voyant
+769::Hungry Lucy
+770::Minerve
+771::Client
+772::Frozen Plasma
+773::Sohodolls
+774::Tiga
+775::Vitalic
+776::Fischerspooner
+777::Sebastian
+778::Project Pitchfork
+779::Deine Lakaien
+780::Panzer AG
+781::Imperative Reaction
+782::In Strict Confidence
+783::Decoded Feedback
+784::Le Tigre
+785::Paul Oakenfold
+786::Cinema Strange
+787::Uffie
+788::Aesthetic Perfection
+789::Safri Duo
+790::Peaches
+791::Blaqk Audio
+792::Alien Vampires
+793::Heimataerde
+794::David Vendetta
+795::Rank 1
+796::Faderhead
+797::Destroid
+798::Blind Passengers
+799::Beborn Beton
+800::Faith and the Muse
+801::Miss Kittin
+802::Chicks on Speed
+803::BT
+804::Darude
+805::Switchblade Symphony
+806::Lasgo
+807::Kittie
+808::Michigan
+809::Felix da Housecat
+810::Pulsedriver
+811::Lo-Fi-Fnk
+812::Welle:Erdball
+813::T.O.Y.
+814::Electrocute
+815::Pzychobitch
+816::Hallucinogen
+817::Astral Projection
+818::Razed in Black
+819::Dismantled
+820::Amon Amarth
+821::Kreator
+822::Queen
+823::Sodom
+824::Flotsam and Jetsam
+825::Sabaton
+826::Hirax
+827::Venom
+828::Biomechanical
+829::Turisas
+830::Warbringer
+831::Dismember
+832::Cut Copy
+833::Katie Melua
+834::Garbage
+835::Scorpions
+836::a-ha
+837::Blur
+838::Arctic Monkeys
+839::Bloc Party
+840::Interpol
+841::Toni Braxton
+842::Bob Marley
+843::Whitney Houston
+844::Mary J. Blige
+845::Brandy
+846::Monica
+847::Kelly Clarkson
+848::Alanis Morissette
+849::Cobra Starship
+850::Cher
+851::Wir sind Helden
+852::Tokyo Police Club
+853::The Kills
+854::The Strokes
+855::Yeah Yeah Yeahs
+856::She & Him
+857::Ra Ra Riot
+858::Local Natives
+859::3 Doors Down
+860::Gwen Stefani
+861::La Roux
+862::Alphabeat
+863::Oasis
+864::Space Cowboy
+865::Pitty
+866::Ellie Goulding
+867::The Kinks
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+2585::King Crimson
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+2587::Dethklok
+2588::Styx
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+2591::Finntroll
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+2594::Bad Brains
+2595::The Gathering
+2596::Labyrinth
+2597::Elvenking
+2598::Candlemass
+2599::Persuader
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+2601::Cameo
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+2604::Andre 3000
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+2607::Alcatrazz
+2608::Mountain
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+2613::Mustard Plug
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+2615::Nitin Sawhney
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+2618::Emilie Simon
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+2620::After Forever
+2621::Devil Doll
+2622::Violeta Parra
+2623::Porter
+2624::Gerry Rafferty
+2625::Steel Pulse
+2626::Bowling for Soup
+2627::Chevelle
+2628::Wheatus
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+2630::Westlife
+2631::Five
+2632::Jamal
+2633::Eva Simons
+2634::Kelly Key
+2635::Mr. President
+2636::Whigfield
+2637::Ice MC
+2638::Arash
+2639::Daniela Mercury
+2640::Lawrence
+2641::LS Jack
+2642::Steps
+2643::E-Type
+2644::Between the Buried and Me
+2645::Born of Osiris
+2646::Periphery
+2647::Veil of Maya
+2648::The Contortionist
+2649::Within The Ruins
+2650::After the Burial
+2651::Moving Mountains
+2652::Real Life
+2653::John Taylor
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+2655::Orianthi
+2656::Heaven Shall Burn
+2657::Austrian Death Machine
+2658::Stray From the Path
+2659::Ana Cañas
+2660::Silbermond
+2661::Eluveitie
+2662::Die Toten Hosen
+2663::Hot Water Music
+2664::Caliban
+2665::It Prevails
+2666::I Killed the Prom Queen
+2667::Lisa Marie Presley
+2668::Abandon All Ships
+2669::The Crimson Armada
+2670::Architects
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+2672::Kutless
+2673::Maylene and the Sons of Disaster
+2674::Confide
+2675::Demon Hunter
+2676::Disarmonia Mundi
+2677::Kyte
+2678::Nightrage
+2679::Misery Signals
+2680::House vs. Hurricane
+2681::One Morning Left
+2682::Kids In Glass Houses
+2683::FM Static
+2684::Head
+2685::Threat Signal
+2686::Becoming the Archetype
+2687::Against Me!
+2688::Lucero
+2689::Mooncake
+2690::Of Mice & Men
+2691::Sabrepulse
+2692::Norma Jean
+2693::Blockhead
+2694::Emancipator
+2695::Мои Ракеты Вверх
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+2697::Caspian
+2698::NEVERSMILE
+2699::Xe-NONE
+2700::Asobi Seksu
+2701::Lydia
+2702::Bassnectar
+2703::The Amity Affliction
+2704::Maria Mena
+2705::Gorgoroth
+2706::Marduk
+2707::Immortal
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+2709::Entombed
+2710::Borknagar
+2711::Nocturnal Depression
+2712::Zемфира
+2713::Adriano Celentano
+2714::Alex Gaudino
+2715::Shaggy
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+2717::Tortoise
+2718::Lights
+2719::Nadja
+2720::PJ Harvey
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+2724::Starlight Mints
+2725::Mercyful Fate
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+2727::Isaac Hayes
+2728::The 69 Eyes
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+2730::Deerhoof
+2731::Apostle of Hustle
+2732::Primus
+2733::Xiu Xiu
+2734::Built to Spill
+2735::The Fiery Furnaces
+2736::Peeping Tom
+2737::Public Image Ltd.
+2738::Eagles of Death Metal
+2739::Charlotte Gainsbourg
+2740::John Cage
+2741::The Sugarcubes
+2742::Danzig
+2743::John Zorn
+2744::Nurse With Wound
+2745::Scarling.
+2746::Caribou
+2747::Liars
+2748::GWAR
+2749::Sun City Girls
+2750::Secret Chiefs 3
+2751::Celtic Frost
+2752::Into Eternity
+2753::Funki Porcini
+2754::Leæther Strip
+2755::Auf der Maur
+2756::Living Colour
+2757::Lovage
+2758::Esthero
+2759::Faust
+2760::The Go! Team
+2761::Camera Obscura
+2762::The 5.6.7.8's
+2763::Exciter
+2764::Don Caballero
+2765::Helmet
+2766::Eluvium
+2767::Pinback
+2768::Angelspit
+2769::Bif Naked
+2770::John Frusciante
+2771::Harold Budd
+2772::Sloan
+2773::Daniel Johnston
+2774::Meat Puppets
+2775::Beulah
+2776::The Creepshow
+2777::Matthew Herbert
+2778::Tosca
+2779::Afterlife
+2780::El Perro del Mar
+2781::Gravity Kills
+2782::The Postmarks
+2783::Minor Threat
+2784::Hella
+2785::Veruca Salt
+2786::Love Is All
+2787::Buckethead
+2788::Richard Cheese
+2789::Mouse on Mars
+2790::MC5
+2791::My Ruin
+2792::Hanzel und Gretyl
+2793::Gang Gang Dance
+2794::The Concretes
+2795::The Long Blondes
+2796::OOIOO
+2797::Shannon Wright
+2798::Marcomé
+2799::Terry Riley
+2800::Tristan Feldbauer
+2801::Warren Zevon
+2802::Daniel Lanois
+2803::The Tragically Hip
+2804::Karlheinz Stockhausen
+2805::Hello Saferide
+2806::Unexpect
+2807::This Heat
+2808::Bill Laswell
+2809::Art Ensemble of Chicago
+2810::Milivoj Culibrk
+2811::Estradasphere
+2812::The Boy Least Likely To
+2813::The Wimshurst's Machine
+2814::Wintersleep
+2815::Ministry
+2816::New Model Army
+2817::Johnny Hates Jazz
+2818::Robert Palmer
+2819::The Skatalites
+2820::Lush
+2821::Dead Can Dance
+2822::Pavement
+2823::Suicide
+2824::Elvis Costello & The Attractions
+2825::The Beat
+2826::The Rapture
+2827::Suzanne Vega
+2828::The Sundays
+2829::Neil Diamond
+2830::Crass
+2831::Clock DVA
+2832::Toad the Wet Sprocket
+2833::GBH
+2834::Slowdive
+2835::Mazzy Star
+2836::The Rutles
+2837::Brendan Benson
+2838::Badly Drawn Boy
+2839::The Undertones
+2840::The Band
+2841::The La's
+2842::The Modern Lovers
+2843::Mr. Mister
+2844::Autograph
+2845::Scandal
+2846::Falco
+2847::Soft Machine
+2848::Black Tape for a Blue Girl
+2849::The Rembrandts
+2850::Buzzcocks
+2851::Aimee Mann
+2852::The Comsat Angels
+2853::The Danse Society
+2854::The Sounds
+2855::Sebadoh
+2856::Klaus Nomi
+2857::The Lemonheads
+2858::Laura Branigan
+2859::Book of Love
+2860::Tim Finn
+2861::Camper Van Beethoven
+2862::Pete Shelley
+2863::The Icicle Works
+2864::Buffalo Springfield
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+2868::The House of Love
+2869::France Gall
+2870::And Also the Trees
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+2872::.38 Special
+2873::X-Ray Spex
+2874::Prince Buster
+2875::Teenage Fanclub
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+2879::Severed Heads
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+2882::Sham 69
+2883::Television Personalities
+2884::The Adverts
+2885::X-Mal Deutschland
+2886::The Replacements
+2887::IMA Robot
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+2889::Youth Group
+2890::Love Spirals Downwards
+2891::Low
+2892::Todd Rundgren
+2893::Stereo Total
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+2896::Hothouse Flowers
+2897::Kirsty MacColl
+2898::Billy Bragg
+2899::Josef K
+2900::Sid Vicious
+2901::The Lightning Seeds
+2902::UK Subs
+2903::YACHT
+2904::Young Marble Giants
+2905::Talulah Gosh
+2906::Faces
+2907::The Teardrop Explodes
+2908::Pere Ubu
+2909::The Boomtown Rats
+2910::Pop Will Eat Itself
+2911::Das Ich
+2912::Stromae
+2913::Social Distortion
+2914::Salem
+2915::The Black Dahlia Murder
+2916::Sonic Syndicate
+2917::Emmure
+2918::The Dillinger Escape Plan
+2919::Dance Gavin Dance
+2920::Agoraphobic Nosebleed
+2921::Mastodon
+2922::Naked City
+2923::Rhapsody of Fire
+2924::Exodus
+2925::Primal Fear
+2926::Tankard
+2927::Artillery
+2928::Toxic Holocaust
+2929::Municipal Waste
+2930::Napalm Death
+2931::Bleeding Through
+2932::Waking The Cadaver
+2933::Impending Doom
+2934::See You Next Tuesday
+2935::Megaherz
+2936::Architect
+2937::ВFI
+2938::Blind Witness
+2939::We Butter The Bread With Butter
+2940::It Dies Today
+2941::Danko Jones
+2942::Best Coast
+2943::Protest The Hero
+2944::Ляпис Трубецкой
+2945::1349
+2946::Vader
+2947::Dark Funeral
+2948::Joe Bonamassa
+2949::Machinae Supremacy
+2950::Hypocrisy
+2951::Converge
+2952::The Ghost Inside
+2953::Comeback Kid
+2954::Have Heart
+2955::New Years Day
+2956::iwrestledabearonce
+2957::Ektomorf
+2958::Blood Red Shoes
+2959::Misery Index
+2960::The Faceless
+2961::The Chariot
+2962::Asesino
+2963::H2O
+2964::Possessed
+2965::Dissection
+2966::Altaria
+2967::The Acacia Strain
+2968::Terror
+2969::In Fear and Faith
+2970::Necrophagist
+2971::Adept
+2972::Anal Cunt
+2973::I Declare War
+2974::Dr. Acula
+2975::And Hell Followed With
+2976::Attila
+2977::War From a Harlots Mouth
+2978::Molotov Solution
+2979::The Tony Danza Tapdance Extravaganza
+2980::To/Die/For
+2981::Throwdown
+2982::Sadus
+2983::Agnostic Front
+2984::Emigrate
+2985::Dungeon Elite [**]
+2986::iamerror
+2987::Arsonists Get All The Girls
+2988::Greeley Estates
+2989::Coroner
+2990::I:Scintilla
+2991::Conjure One
+2992::De-Phazz
+2993::Corrosion of Conformity
+2994::Virgin Black
+2995::Onslaught
+2996::The Derek Trucks Band
+2997::For the Fallen Dreams
+2998::Sumatra
+2999::Every Time I Die
+3000::Memphis May Fire
+3001::Xentrix
+3002::Times of Grace
+3003::Charlie Musselwhite
+3004::Death Before Dishonor
+3005::Kylesa
+3006::The Locust
+3007::Spectra Paris
+3008::Raunchy
+3009::Element Eighty
+3010::Your Demise
+3011::Spineshank
+3012::Oceana
+3013::Razor
+3014::Beseech
+3015::Imperanon
+3016::An Albatross
+3017::The Human Abstract
+3018::Circle Jerks
+3019::Envy
+3020::United Nations
+3021::Sybreed
+3022::Prodigy
+3023::Sick of It All
+3024::Trap Them
+3025::Walter Trout
+3026::Dead Poetic
+3027::Meg & Dia
+3028::Cephalic Carnage
+3029::Tesseract
+3030::Bonded By Blood
+3031::Casey Jones
+3032::Silent Civilian
+3033::Through the Eyes of the Dead
+3034::HEARTSREVOLUTION
+3035::Amanda Blank
+3036::T.S.O.L.
+3037::AKADO
+3038::Arkaea
+3039::Scanners
+3040::Super Junior
+3041::SHINee
+3042::소녀시대
+3043::BoA
+3044::M. Ward
+3045::Devendra Banhart
+3046::Nick Drake
+3047::Tracy Chapman
+3048::Tilly and the Wall
+3049::The Magnetic Fields
+3050::Jens Lekman
+3051::Neutral Milk Hotel
+3052::The Microphones
+3053::Augustus Pablo
+3054::King Tubby
+3055::Lee "Scratch" Perry
+3056::The Gun Club
+3057::Spiritualized
+3058::The Flying Burrito Brothers
+3059::Loretta Lynn
+3060::Hawkwind
+3061::Leadbelly
+3062::Grandaddy
+3063::Guided by Voices
+3064::Townes Van Zandt
+3065::Red House Painters
+3066::Madredeus
+3067::Mediæval Bæbes
+3068::Her Space Holiday
+3069::Hood
+3070::Ane Brun
+3071::Son House
+3072::Sopor Aeternus & The Ensemble of Shadows
+3073::Parkway Drive
+3074::Avantasia
+3075::You Me At Six
+3076::Mis-Teeq
+3077::Adam Green
+3078::Nina Simone
+3079::Stacie Orrico
+3080::Emma Shapplin
+3081::4minute
+3082::Eazy-E
+3083::Mobb Deep
+3084::Chayanne
+3085::Royce da 5'9"
+3086::Busta Rhymes
+3087::Sara Bareilles
+3088::Lenka
+3089::A Static Lullaby
+3090::Girl Talk
+3091::Spinnerette
+3092::Will Young
+3093::Macy Gray
+3094::This Mortal Coil
+3095::Cold War Kids
+3096::Marit Larsen
+3097::Every Avenue
+3098::Edenbridge
+3099::Lisa Miskovsky
+3100::Krystal Meyers
+3101::Kirlian Camera
+3102::The Organ
+3103::Clannad
+3104::Lisa "Left Eye" Lopes
+3105::Millionaires
+3106::The Casualties
+3107::Evile
+3108::Cadaveria
+3109::Astarte
+3110::Elis
+3111::Jonathan Davis
+3112::Android Lust
+3113::Hillsong United
+3114::The Toy Dolls
+3115::A Place to Bury Strangers
+3116::Crash Test Dummies
+3117::The Wreckers
+3118::Lunatica
+3119::Draconian
+3120::Trail of Tears
+3121::Uh Huh Her
+3122::Planetshakers
+3123::Coal Chamber
+3124::The Submarines
+3125::Mortal Love
+3126::M2M
+3127::Danity Kane
+3128::Doda
+3129::Brodka
+3130::GZA/Genius
+3131::Ghostface Killah
+3132::Natasha Bedingfield
+3133::Aretha Franklin
+3134::James Brown
+3135::Alexandra Burke
+3136::Ewa Farna
+3137::Lady Sovereign
+3138::Dan Balan
+3139::Eve
+3140::Carla Bruni
+3141::Minnie Riperton
+3142::Czesław Śpiewa
+3143::Etta James
+3144::Michael Bublé
+3145::Beady Eye
+3146::Eric B. & Rakim
+3147::Shontelle
+3148::Hey
+3149::Édith Piaf
+3150::Peter Doherty
+3151::Angus & Julia Stone
+3152::Strachy Na Lachy
+3153::Yolanda Be Cool & DCUP
+3154::Paloma Faith
+3155::Stanfour
+3156::Hellogoodbye
+3157::Nosowska
+3158::Ania
+3159::Bow Wow
+3160::Aura Dione
+3161::Cody Simpson
+3162::Natalia Kills
+3163::James Horner
+3164::Lea Michele
+3165::Nneka
+3166::Alexis Jordan
+3167::Tinchy Stryder
+3168::Lauryn Hill
+3169::Mark Ronson & The Business Intl
+3170::Box Car Racer
+3171::Flipsyde
+3172::Soulja Boy
+3173::En Vogue
+3174::Clipse
+3175::Yael Naim
+3176::Flogging Molly
+3177::Smolik
+3178::Ana Johnsson
+3179::Sinéad O'Connor
+3180::Clare Maguire
+3181::Mike Posner
+3182::Justin Nozuka
+3183::T-Pain
+3184::Kaki King
+3185::Vanessa Paradis
+3186::Tom Petty
+3187::Rachael Yamagata
+3188::Måns Zelmerlöw
+3189::Moulin Rouge
+3190::Hope
+3191::Run-D.M.C.
+3192::The Supremes
+3193::Diana Ross and The Supremes
+3194::Hurt
+3195::Pretty Ricky
+3196::Gotan Project
+3197::The Last Goodnight
+3198::Zabili Mi Żółwia
+3199::Cee-Lo
+3200::Cee Lo Green
+3201::Lil Mama
+3202::Anita Baker
+3203::Julian Casablancas
+3204::Poisonblack
+3205::Black Stone Cherry
+3206::Queensberry
+3207::The Blackout
+3208::deadmau5
+3209::Marianas Trench
+3210::Aiden
+3211::Sugarcult
+3212::CKY
+3213::Halestorm
+3214::Emerson, Lake & Palmer
+3215::Kix
+3216::The Saturdays
+3217::Jessie J
+3218::Rebecca Black
+3219::Самое Большое Простое Число
+3220::Red Snapper
+3221::Animal ДжаZ
+3222::Laika
+3223::Killwhitneydead
+3224::Texas in July
+3225::Elio e le Storie Tese
+3226::TV on the Radio
+3227::Blackfield
+3228::Helena Paparizou
+3229::Midge Ure
+3230::Exposé
+3231::X-Dream
+3232::Lights of Euphoria
+3233::Astrud Gilberto
+3234::Athlete
+3235::The Ark
+3236::Alizée
+3237::Huey Lewis & The News
+3238::Morten Harket
+3239::Bad Boys Blue
+3240::Silent Circle
+3241::Fancy
+3242::Astrix
+3243::Chris Spheeris
+3244::عمر دياب
+3245::Cesária Évora
+3246::Supreme Beings of Leisure
+3247::Bread
+3248::Frou Frou
+3249::Ace Ventura
+3250::Guillemots
+3251::Gus Gus
+3252::Boy George
+3253::Jay-Jay Johanson
+3254::Aqualung
+3255::Tahiti 80
+3256::Offer Nissim
+3257::Orient Expressions
+3258::Elegant Machinery
+3259::Mel & Kim
+3260::Bebel Gilberto
+3261::Jody Watley
+3262::Ghosts
+3263::Mumm-Ra
+3264::Bitter:Sweet
+3265::Angtoria
+3266::Terence Trent D'Arby
+3267::Nicola Conte
+3268::Alter Ego
+3269::Samantha Fox
+3270::Anouar Brahem
+3271::Liza Minnelli
+3272::Blue Stone
+3273::Peter Schilling
+3274::Omar Faruk Tekbilek
+3275::Team Sleep
+3276::Up, Bustle and Out
+3277::Parov Stelar
+3278::Asian Dub Foundation
+3279::Chase & Status
+3280::KOTOKO
+3281::安室奈美恵
+3282::モーニング娘。
+3283::大塚愛
+3284::水樹奈々
+3285::林原めぐみ
+3286::北出菜奈
+3287::Zola Jesus
+3288::Blackmore's Night
+3289::Stevie Ray Vaughan
+3290::Rick Wakeman
+3291::Geddy Lee
+3292::Roger Waters
+3293::Van der Graaf Generator
+3294::Focus
+3295::Bachman-Turner Overdrive
+3296::Slade
+3297::Traffic
+3298::Premiata Forneria Marconi
+3299::B.B. King & Eric Clapton
+3300::Neil Young & Crazy Horse
+3301::Inti-Illimani
+3302::Thievery Corporation
+3303::Ornette Coleman
+3304::Michael Gray
+3305::Madvillain
+3306::Sneaker Pimps
+3307::Skye
+3308::Dangerous Muse
+3309::Grizzly Bear
+3310::Martina Topley-Bird
+3311::Smoke City
+3312::Halou
+3313::H.U.V.A. Network
+3314::Aes Dana
+3315::Anja Garbarek
+3316::dZihan & Kamien
+3317::Days of the New
+3318::Shirley Bassey
+3319::Patti LaBelle
+3320::The Field
+3321::Daughter Darling
+3322::Montefiori Cocktail
+3323::Laurent Garnier
+3324::Waldeck
+3325::Rue du Soleil
+3326::Kruder & Dorfmeister
+3327::DeVotchKa
+3328::O Teatro Mágico
+3329::The Tallest Man on Earth
+3330::New Young Pony Club
+3331::Pizzicato Five
+3332::The Dresden Dolls
+3333::Those Dancing Days
+3334::Laura Marling
+3335::Super Furry Animals
+3336::Nightmare
+3337::Rory Gallagher
+3338::Duke Ellington
+3339::St. Vincent
+3340::Brian Wilson
+3341::Owen Pallett
+3342::Jorge Drexler
+3343::The Raveonettes
+3344::Tim Maia
+3345::Janelle Monáe
+3346::Tujiko Noriko
+3347::Vashti Bunyan
+3348::Sons and Daughters
+3349::Nico
+3350::Rasputina
+3351::The Zutons
+3352::Harry and the Potters
+3353::The Ditty Bops
+3354::VHS or Beta
+3355::The Seatbelts
+3356::Louise Attaque
+3357::Au Revoir Simone
+3358::Emmy the Great
+3359::Juli
+3360::Hope Sandoval & The Warm Inventions
+3361::Dum Dum Girls
+3362::Blue Man Group
+3363::Louis XIV
+3364::The Grates
+3365::The Cribs
+3366::The Maccabees
+3367::Envydust
+3368::Head Automatica
+3369::Boy Kill Boy
+3370::The View
+3371::The Futureheads
+3372::Moptop
+3373::Madina Lake
+3374::Juliette and The Licks
+3375::Sirenia
+3376::Kristine Elezaj
+3377::Ticon
+3378::Asura
+3379::Brigitte Bardot
+3380::Hybrid
+3381::Lützenkirchen
+3382::Kiara Rocks
+3383::嵐
+3384::玉置成実
+3385::Grave Digger
+3386::Bonfire
+3387::3 Inches of Blood
+3388::Nazareth
+3389::Sandy Leah
+3390::Mat Kearney
+3391::Nonpoint
+3392::Turbonegro
+3393::Scott Walker
+3394::Gianna Nannini
+3395::Velvet Acid Christ
+3396::David Gray
+3397::Rancid
+3398::The Wallflowers
+3399::Teitur
+3400::Psapp
+3401::Natalie Walker
+3402::Deathstars
+3403::My Life with the Thrill Kill Kult
+3404::Hank Williams Jr.
+3405::Hootie & the Blowfish
+3406::Gov't Mule
+3407::Lene Marlin
+3408::Toadies
+3409::Joy Williams
+3410::Mr. Scruff
+3411::Anna Nalick
+3412::Olivia Ruiz
+3413::Thomas Dybdahl
+3414::Vermillion Lies
+3415::Jerry Lee Lewis
+3416::Charlie Daniels Band
+3417::Blues Traveler
+3418::Jimmy Cliff
+3419::Better Than Ezra
+3420::Vienna Teng
+3421::Bel Canto
+3422::Jonny Lang
+3423::ohGr
+3424::Pigface
+3425::Crosby, Stills & Nash
+3426::Celestial Aeon Project
+3427::Powerman 5000
+3428::Ace Frehley
+3429::Various Artists
+3430::Alcest
+3431::Heaven & Hell
+3432::Tor Lundvall
+3433::All My Faith Lost ...
+3434::Magna Canta
+3435::32Crash
+3436::Jota Quest
+3437::Scars on Broadway
+3438::Upcdowncleftcrightcabc+start
+3439::Banco de Gaia
+3440::I Hear Sirens
+3441::Yonderboi
+3442::Kwoon
+3443::Fuck Buttons
+3444::Destroyalldreamers
+3445::Пелагея
+3446::Rachel's
+3447::Tunturia
+3448::Peter Tosh
+3449::Damian Marley
+3450::Vavamuffin
+3451::Ziggy Marley
+3452::Gentleman
+3453::He Is Legend
+3454::Dance Club Massacre
+3455::Pogo
+3456::Circa Survive
+3457::Kill Paradise
+3458::Tyler, The Creator
+3459::Anna Calvi
+3460::Rev Theory
+3461::The Perishers
+3462::José González
+3463::The Wombats
+3464::Madlib
+3465::Paul Kalkbrenner
+3466::James Blake
+3467::Télépopmusik
+3468::Tesla Boy
+3469::Trentemøller
+3470::Yoav
+3471::Bent
+3472::Sad Lovers and Giants
+3473::The Chameleons
+3474::HEALTH
+3475::Toro y Moi
+3476::SCSI-9
+3477::Zeigeist
+3478::Beach Fossils
+3479::Bad Lieutenant
+3480::Gui Boratto
+3481::Chairlift
+3482::Nathan Fake
+3483::Thieves Like Us
+3484::Kollektiv Turmstrasse
+3485::Extrawelt
+3486::Stephan Bodzin
+3487::Minilogue
+3488::Sascha Funke
+3489::A Camp
+3490::Anthony Rother
+3491::Matthew Dear
+3492::The Mary Onettes
+3493::Erik Satie
+3494::Weather Report
+3495::Nujabes
+3496::Nick Cave & Warren Ellis
+3497::Night Ranger
+3498::Girlicious
+3499::Blitz
+3500::Worm Is Green
+3501::Broken Social Scene
+3502::Great Lake Swimmers
+3503::Grateful Dead
+3504::Anathallo
+3505::Piano Magic
+3506::Do Make Say Think
+3507::Cartola
+3508::The Mountain Goats
+3509::Okkervil River
+3510::Amália Rodrigues
+3511::Celtic Woman
+3512::João Gilberto
+3513::Woods
+3514::Ray LaMontagne
+3515::The Brian Jonestown Massacre
+3516::Streetlight Manifesto
+3517::Ash
+3518::Elis Regina
+3519::Bang Gang
+3520::Baden Powell
+3521::The Radio Dept.
+3522::Stafrænn Hákon
+3523::Owen
+3524::My Morning Jacket
+3525::Amusement Parks on Fire
+3526::Efterklang
+3527::Gray Strawberries
+3528::Omnia
+3529::September Malevolence
+3530::The Meters
+3531::Chapterhouse
+3532::Nara Leão
+3533::Borko
+3534::Electrelane
+3535::Novos Baianos
+3536::Carissa's Wierd
+3537::Under Byen
+3538::Huun-Huur-Tu
+3539::Stan Getz
+3540::Andrei Machado
+3541::Say Hi to Your Mom
+3542::Kitchens of Distinction
+3543::Sun Kil Moon
+3544::Casiotone for the Painfully Alone
+3545::Drive-By Truckers
+3546::Love
+3547::Hermeto Pascoal
+3548::Roger Miller
+3549::Horse Feathers
+3550::Akron/Family
+3551::Tristeza
+3552::A Hawk and a Hacksaw
+3553::Delays
+3554::Rilo Kiley
+3555::Infernal
+3556::dredg
+3557::Trapt
+3558::Billy Currington
+3559::Richard Ashcroft
+3560::From Autumn to Ashes
+3561::Ronan Keating
+3562::Cat Stevens
+3563::Train
+3564::Richie Sambora
+3565::Air Traffic
+3566::Entwine
+3567::Brian McKnight
+3568::Superchic[k]
+3569::Globus
+3570::Semisonic
+3571::Skazi
+3572::Shadow Gallery
+3573::Dark Moor
+3574::Nocturnal Rites
+3575::Lost Horizon
+3576::Evergrey
+3577::TV-2
+3578::Common
+3579::Faith Evans
+3580::Otis Redding
+3581::The Game
+3582::Musiq
+3583::Dizzee Rascal
+3584::Redman
+3585::Utada
+3586::Akufen
+3587::Black Star
+3588::Third Eye Blind
+3589::Jennifer Love Hewitt
+3590::Example
+3591::Coconut Records
+3592::Ginuwine
+3593::Jamie T
+3594::Mario
+3595::112
+3596::Ben Woods
+3597::John Lee Hooker
+3598::Missy Higgins
+3599::Christina Milian
+3600::Lisa Gerrard
+3601::Black Lab
+3602::Pete Yorn
+3603::Ol' Dirty Bastard
+3604::Alkaline Trio
+3605::Pennywise
+3606::Battlelore
+3607::Lucinda Williams
+3608::RZA
+3609::Amber Pacific
+3610::植松伸夫
+3611::Lemon Jelly
+3612::The Blues Brothers
+3613::The Living End
+3614::The J. Geils Band
+3615::Beatsteaks
+3616::Meat Loaf
+3617::Jagged Edge
+3618::Marcy Playground
+3619::Stealers Wheel
+3620::The Click Five
+3621::One Night Only
+3622::Scouting for Girls
+3623::The Crowns
+3624::The Bird and The Bee
+3625::Chiodos
+3626::The Get Up Kids
+3627::The Coasters
+3628::Overkill
+3629::Down
+3630::Nek
+3631::Gipsy Kings
+3632::Ibrahim Ferrer
+3633::Paco de Lucía
+3634::Alejandro Fernández
+3635::Moenia
+3636::Karsh Kale
+3637::Oliver Shanti
+3638::Graham Coxon
+3639::Maino
+3640::Keyshia Cole
+3641::Винтаж
+3642::Booty Luv
+3643::Three 6 Mafia
+3644::Jordan Rudess
+3645::Lunatic Soul
+3646::David Gilmour
+3647::Liquid Tension Experiment
+3648::Al Di Meola
+3649::John Petrucci
+3650::Michael Giacchino
+3651::Django Reinhardt
+3652::Donovan
+3653::Fish
+3654::Fates Warning
+3655::Obscura
+3656::Camel
+3657::Atheist
+3658::Gentle Giant
+3659::David Sylvian
+3660::Nine Horses
+3661::Harry Gregson-Williams
+3662::Beardfish
+3663::Phideaux
+3664::Eloy
+3665::Pestilence
+3666::Kenny G
+3667::Rain Tree Crow
+3668::Goblin
+3669::maudlin of the Well
+3670::Caravan
+3671::Gryphon
+3672::Chroma Key
+3673::Robin Trower
+3674::Chick Corea
+3675::Return to Forever
+3676::Renaissance
+3677::Trey Gunn
+3678::The Rakes
+3679::Dirty Pretty Things
+3680::Milburn
+3681::The Bravery
+3682::Quietdrive
+3683::The Automatic
+3684::The Hoosiers
+3685::Obie Trice
+3686::Bizarre
+3687::Cassiane
+3688::Kim Carnes
+3689::Brenda Lee
+3690::Jack's Mannequin
+3691::Motion City Soundtrack
+3692::The John Butler Trio
+3693::The Cloud Room
+3694::Mark Knopfler
+3695::Haircut 100
+3696::Olivia Newton-John
+3697::Idlewild
+3698::Edwyn Collins
+3699::Orange Juice
+3700::Trio
+3701::Jane Wiedlin
+3702::Passengers
+3703::Andy Taylor
+3704::Nuclear Assault
+3705::Doves
+3706::Hot Hot Heat
+3707::Klaus Badelt
+3708::Kidneythieves
+3709::Grave
+3710::D'espairsRay
+3711::Benjamin Biolay
+3712::Ian Brown
+3713::Eddie Cochran
+3714::Link Wray
+3715::Gary Jules
+3716::Immortal Technique
+3717::Jurassic 5
+3718::Blackalicious
+3719::Bone Thugs-N-Harmony
+3720::Ice Cube
+3721::Cam'ron
+3722::Notorious B.I.G.
+3723::Naughty by Nature
+3724::Proof
+3725::Big L
+3726::Taj Mahal
+3727::Andrew W.K.
+3728::Joe
+3729::Vanilla Ice
+3730::Cap'n Jazz
+3731::Less Than Jake
+3732::Saves the Day
+3733::American Football
+3734::Matt Pond PA
+3735::Karunesh
+3736::Ravi Shankar
+3737::Philip Glass
+3738::Frozen Silence
+3739::Current 93
+3740::Psychic TV
+3741::Ryoji Ikeda
+3742::Zoviet France
+3743::Lustmord
+3744::Alva Noto
+3745::Sergei Rachmaninoff
+3746::Lusine
+3747::Kaya Project
+3748::Tangerine Dream
+3749::Atrium Carceri
+3750::Kammarheit
+3751::Kronos Quartet
+3752::Niyaz
+3753::Steve Roach
+3754::Muslimgauze
+3755::Yat-Kha
+3756::Steve Reich
+3757::Luc Ferrari
+3758::Baskyl
+3759::Modus
+3760::大谷幸
+3761::DJ Food
+3762::Jon Hopkins
+3763::Mythos
+3764::THEreminGIRL
+3765::The Teenagers
+3766::Solar Fields
+3767::The Tear Garden
+3768::Goran Bregović
+3769::Michael Andrews
+3770::Patrick Doyle
+3771::Eric Serra
+3772::Jan Hammer
+3773::Swans
+3774::John Barry
+3775::Dario Marianelli
+3776::Side Liner
+3777::Revolting Cocks
+3778::American Head Charge
+3779::Lords of Acid
+3780::Zeromancer
+3781::Zombina and the Skeletones
+3782::Dog Fashion Disco
+3783::Mortiis
+3784::Sevendust
+3785::Stabbing Westward
+3786::Godhead
+3787::Fun Lovin' Criminals
+3788::Pagoda
+3789::Moonspell
+3790::Gojira
+3791::Balmorhea
+3792::Glen Hansard & Markéta Irglová
+3793::Peace Orchestra
+3794::dEUS
+3795::Diante do Trono
+3796::Carter Burwell
+3797::Dusty Kid
+3798::Modjo
+3799::Nada Surf
+3800::Matia Bazar
+3801::The Glove
+3802::Sex Gang Children
+3803::Altered Images
+3804::The Tornados
+3805::Them Crooked Vultures
+3806::Ólafur Arnalds
+3807::Remy Zero
+3808::Inkubus Sukkubus
+3809::Monty Python
+3810::Steely Dan
+3811::MUM
+3812::Traveling Wilburys
+3813::The Nitty Gritty Dirt Band
+3814::Carly Simon
+3815::This Will Destroy You
+3816::Big Brother & The Holding Company
+3817::Cage the Elephant
+3818::Bill Hicks
+3819::K's Choice
+3820::Little Feat
+3821::Sam & Dave
+3822::Three Dog Night
+3823::Dan Fogelberg
+3824::Poe
+3825::Indigo Girls
+3826::Nikka Costa
+3827::Crosby, Stills, Nash & Young
+3828::And So I Watch You From Afar
+3829::Elysian Fields
+3830::pg.lost
+3831::Maserati
+3832::Dan Auerbach
+3833::Eddie Izzard
+3834::Firefall
+3835::Zwan
+3836::Cine
+3837::Copacabana Club
+3838::E.S. Posthumus
+3839::Of the Wand and the Moon
+3840::Rodrigo y Gabriela
+3841::Bell X1
+3842::Les Savy Fav
+3843::Steve Earle
+3844::Cibo Matto
+3845::Jonny Greenwood
+3846::Susumu Yokota
+3847::Get Cape. Wear Cape. Fly
+3848::Fred Astaire
+3849::Kay Starr
+3850::Jamie Lidell
+3851::Gustavo Santaolalla
+3852::Gnawa Diffusion
+3853::Michelle Branch
+3854::Stephanie McIntosh
+3855::Queen + Paul Rodgers
+3856::Mägo de Oz
+3857::Jill Scott
+3858::The Easybeats
+3859::El Cuarteto de Nos
+3860::Horace Andy
+3861::Os Paralamas do Sucesso
+3862::Plastiscines
+3863::Laurel Aitken
+3864::Chico Science & Nação Zumbi
+3865::The Wailers
+3866::The Warlocks
+3867::Spacemen 3
+3868::Mala Rodríguez
+3869::Aesop Rock
+3870::The Field Mice
+3871::Gustavo Cerati
+3872::Buju Banton
+3873::Glasvegas
+3874::Kinky
+3875::The Rifles
+3876::Gil Scott-Heron
+3877::Elastica
+3878::Paul Weller
+3879::The English Beat
+3880::Estelle
+3881::Toots and the Maytals
+3882::Mansun
+3883::The Feelies
+3884::The Duke Spirit
+3885::Kula Shaker
+3886::Gregory Isaacs
+3887::Burning Spear
+3888::Barrington Levy
+3889::Ike & Tina Turner
+3890::The Tears
+3891::Easy Star All-Stars
+3892::The Thrills
+3893::Bernard Butler
+3894::Free the Robots
+3895::John Mayall & The Bluesbreakers
+3896::Sleeper
+3897::Pharrell
+3898::The Telescopes
+3899::Marianne Faithfull
+3900::The Lucksmiths
+3901::Tindersticks
+3902::Julian Cope
+3903::Pernice Brothers
+3904::You Say Party! We Say Die!
+3905::VETO
+3906::Shout Out Louds
+3907::Curve
+3908::Art Brut
+3909::Love and Rockets
+3910::Fela Kuti
+3911::Windy & Carl
+3912::Grouper
+3913::Dictaphone
+3914::Porn Sword Tobacco
+3915::Ada
+3916::The Herbaliser
+3917::Secos & Molhados
+3918::Neu!
+3919::Pentagram
+3920::Le Orme
+3921::Manfred Mann's Earth Band
+3922::Blue Cheer
+3923::Saint Vitus
+3924::Miike Snow
+3925::Atmosphere
+3926::Roy Ayers
+3927::Sarah Vaughan
+3928::Deerhunter
+3929::Fink
+3930::The Temper Trap
+3931::Maria Gadú
+3932::Seu Jorge
+3933::Zeca Baleiro
+3934::Roberta Sá
+3935::Yuksek
+3936::Djavan
+3937::Wax Poetic
+3938::Elsiane
+3939::Jorge Ben Jor
+3940::Logh
+3941::Donavon Frankenreiter
+3942::The Naked and Famous
+3943::Urge Overkill
+3944::Paulinho Moska
+3945::Mariana Aydar
+3946::3-11 Porter
+3947::Alessandro Safina
+3948::Shaman
+3949::Republica
+3950::Canned Heat
+3951::Natiruts
+3952::Blaze
+3953::Gong
+3954::Derek and the Dominos
+3955::Collective Soul
+3956::Richard Wright
+3957::The Servant
+3958::Ananda Shake
+3959::Library Tapes
+3960::Divine Heresy
+3961::Afghan Whigs
+3962::Bayside
+3963::The Courteeners
+3964::Someone Still Loves You Boris Yeltsin
+3965::The Apples in Stereo
+3966::Final Fantasy
+3967::Delorean
+3968::Wild Nothing
+3969::Set Your Goals
+3970::The Bloody Beetroots
+3971::The Feeling
+3972::Pure Reason Revolution
+3973::Atlas Sound
+3974::Portugal. The Man
+3975::Pnau
+3976::Rotting Christ
+3977::Midnight Juggernauts
+3978::Ampop
+3979::Saltillo
+3980::Bane
+3981::Acceptance
+3982::NOFX
+3983::No Use for a Name
+3984::Tears Run Rings
+3985::Noah and the Whale
+3986::...And You Will Know Us by the Trail of Dead
+3987::Jack Peñate
+3988::Catherine Wheel
+3989::Mute Math
+3990::Goose
+3991::Labradford
+3992::Cloud Cult
+3993::The Secret Handshake
+3994::PlayRadioPlay!
+3995::The Unicorns
+3996::Spleen United
+3997::Polvo
+3998::Scratch Acid
+3999::The Whip
+4000::Zoot Woman
+4001::Go:Audio
+4002::Great Northern
+4003::The Dears
+4004::Hawk Nelson
+4005::Destroy The Runner
+4006::Frankmusik
+4007::Van She
+4008::No Age
+4009::Voxtrot
+4010::Lights Out Asia
+4011::Sleater-Kinney
+4012::We Have Band
+4013::The Monochrome Set
+4014::Mission of Burma
+4015::Bishop Allen
+4016::The Dodos
+4017::Swervedriver
+4018::Edward Sharpe & the Magnetic Zeros
+4019::Steve Aoki
+4020::Thurston Moore
+4021::Plastilina Mosh
+4022::Sleeping at Last
+4023::French Teen Idol
+4024::Hate Eternal
+4025::Sugar Ray
+4026::Film School
+4027::Secret Shine
+4028::Surfer Blood
+4029::The Republic Tigers
+4030::We Smoke Fags
+4031::The Big Pink
+4032::Under the Influence of Giants
+4033::Soundpool
+4034::Paper Route
+4035::Sick Puppies
+4036::BarlowGirl
+4037::Ryan Cabrera
+4038::Akcent
+4039::Vanilla Sky
+4040::Carolina Liar
+4041::Casting Crowns
+4042::Vertical Horizon
+4043::Falling Up
+4044::Orson
+4045::T.M.Revolution
+4046::Pain Confessor
+4047::The Blood Brothers
+4048::Diablo Swing Orchestra
+4049::Satyricon
+4050::Karnivool
+4051::Winds of Plague
+4052::Deathspell Omega
+4053::Anata
+4054::Blut aus Nord
+4055::Closure In Moscow
+4056::近藤浩治
+4057::Koop
+4058::PMMP
+4059::Boys Noize
+4060::Orphaned Land
+4061::Andromeda
+4062::Hanni Kohl
+4063::Steve Vai
+4064::Haggard
+4065::Mors Principium Est
+4066::Café Tacuba
+4067::Jarabe de Palo
+4068::Ark
+4069::Marty Friedman
+4070::Maná
+4071::Ojos de Brujo
+4072::Cacophony
+4073::I'm from Barcelona
+4074::Diablo
+4075::Paul Anka
+4076::Damn Yankees
+4077::Diabolical Masquerade
+4078::Jumbo
+4079::Banco del Mutuo Soccorso
+4080::Sacred Reich
+4081::Septic Flesh
+4082::Legion of the Damned
+4083::Rosetta Stone
+4084::Jane Birkin & Serge Gainsbourg
+4085::Bill Haley
+4086::Bella Morte
+4087::Museum
+4088::The Airborne Toxic Event
+4089::Legowelt
+4090::Robert Plant
+4091::Elmore James
+4092::Sonny Boy Williamson
+4093::Freddie King
+4094::Albert King
+4095::Citizen Cope
+4096::Dr. John
+4097::Otis Rush
+4098::Grant Green
+4099::Junior Wells
+4100::Lightnin' Hopkins
+4101::Koko Taylor
+4102::Cursive
+4103::Ted Leo and The Pharmacists
+4104::Laura Veirs
+4105::La Oreja de Van Gogh
+4106::Fernanda Takai
+4107::As Blood Runs Black
+4108::akissforjersey
+4109::Unearth
+4110::Bury Your Dead
+4111::Sonny Rollins
+4112::Cauterize
+4113::Rufio
+4114::My American Heart
+4115::The Hush Sound
+4116::Andy McKee
+4117::Breathe Electric
+4118::The Audition
+4119::Moneen
+4120::The Bled
+4121::Broadway
+4122::LoveHateHero
+4123::División Minúscula
+4124::Daphne Loves Derby
+4125::KSU
+4126::Four Year Strong
+4127::For Today
+4128::Maroon
+4129::The Agony Scene
+4130::Earth Crisis
+4131::Emarosa
+4132::Morningwood
+4133::Hird
+4134::The Dave Brubeck Quartet
+4135::Gustav Mahler
+4136::Franz Joseph Haydn
+4137::Farid Farjad
+4138::梶浦由記
+4139::Marco Borsato
+4140::Joe Walsh
+4141::Sailor Moon
+4142::Montrose
+4143::Nephew
+4144::Volbeat
+4145::Colin Hay
+4146::John Cale
+4147::Beth Gibbons & Rustin Man
+4148::Bay City Rollers
+4149::Free
+4150::Booker T. & The MG's
+4151::Eddie Money
+4152::Bob Seger
+4153::Gene Vincent
+4154::Peter & Gordon
+4155::John Mellencamp
+4156::Bill Haley and the Comets
+4157::Wanda Jackson
+4158::The Swinging Blue Jeans
+4159::The Cowsills
+4160::Foghat
+4161::Fats Domino
+4162::Harry Belafonte
+4163::Ricky Nelson
+4164::Lesley Gore
+4165::The Jeff Healey Band
+4166::Tommy James & The Shondells
+4167::Little River Band
+4168::The Marshall Tucker Band
+4169::The Lively Ones
+4170::Dion & The Belmonts
+4171::The Music Machine
+4172::Kenny Rogers
+4173::Waylon Jennings
+4174::The Beautiful South
+4175::The Left Banke
+4176::Carl Perkins
+4177::Tennessee Ernie Ford
+4178::Bo Diddley
+4179::Blackfoot
+4180::Heathen
+4181::Forbidden
+4182::Exumer
+4183::Our Lady Peace
+4184::Dishwalla
+4185::Gama Bomb
+4186::Dark Angel
+4187::Joseph Arthur
+4188::Saving Abel
+4189::All About Eve
+4190::The Breeders
+4191::Letzte Instanz
+4192::British Sea Power
+4193::Dead Fish
+4194::Fear Before the March of Flames
+4195::Téléphone
+4196::White Rose Movement
+4197::Milla Jovovich
+4198::Renaud
+4199::Georges Brassens
+4200::Alain Bashung
+4201::Prince & The Revolution
+4202::Prince and the New Power Generation
+4203::Mariza
+4204::Gal Costa
+4205::Ry Cooder
+4206::Francis Cabrel
+4207::2002
+4208::Jane Birkin
+4209::Phil Ochs
+4210::Mitchel Musso
+4211::Esmée Denters
+4212::The Ready Set
+4213::New Boyz
+4214::Stereolab
+4215::Throwing Muses
+4216::Cranes
+4217::Deadlock
+4218::Innerpartysystem
+4219::川井憲次
+4220::Empyrium
+4221::Javier Navarrete
+4222::Rick James
+4223::Bobby Brown
+4224::The Polyphonic Spree
+4225::Chew Lips
+4226::Soulwax
+4227::Scarlett Johansson
+4228::DatA
+4229::2 Many DJ's
+4230::Danger
+4231::Filthy Dukes
+4232::Danny
+4233::Zac Efron
+4234::Polly Scattergood
+4235::Marié Digby
+4236::Ben Lee
+4237::Bethany Joy Lenz
+4238::Tyler Hilton
+4239::Grace Potter and the Nocturnals
+4240::Heather Nova
+4241::Die Antwoord
+4242::Muchy
+4243::Reverend and The Makers
+4244::Wild Beasts
+4245::Dying Fetus
+4246::Trouble
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+4474::Georges Bizet
+4475::Yasmin Levy
+4476::Peter Brötzmann
+4477::Mulatu Astatke
+4478::Ali Farka Touré & Toumani Diabaté
+4479::Erkan Oğur
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+4482::Coleman Hawkins
+4483::Wayne Shorter
+4484::Zoë Keating
+4485::Serkan Süleymaniye
+4486::Mercan Dede
+4487::Mark Owen
+4488::Jesus Jones
+4489::White Town
+4490::Françoise Hardy
+4491::Tim Urban
+4492::Gladys Knight & The Pips
+4493::Ben E. King
+4494::Eve 6
+4495::Edwin McCain
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+4537::Joe McElderry
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+4543::Luis Fonsi
+4544::Dionne Bromfield
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+4549::Fernanda Brum
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+4570::György Ligeti
+4571::Zbigniew Preisner
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+4577::Nileppez
+4578::Keith Moon
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+4584::Bülent Ortaçgil
+4585::Feridun Düzağaç
+4586::Be Your Own Pet
+4587::A.C. Newman
+4588::Zooey Deschanel
+4589::Shantel
+4590::Global Deejays
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+4592::Brazilian Girls
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+4594::Coeur de Pirate
+4595::SoKo
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+4597::The Sunshine Underground
+4598::Gridlock
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+4600::Elefant
+4601::Fantasia
+4602::Fikret Kızılok
+4603::Astrobrite
+4604::Darren Criss
+4605::Abney Park
diff --git a/data/ref/lastfm/train_data.df b/data/ref/lastfm/train_data.df
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diff --git a/data/ref/movielens/Test_data.df b/data/ref/movielens/Test_data.df
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+1|Toy Story (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)|0|0|0|1|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+2|GoldenEye (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?GoldenEye%20(1995)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+3|Four Rooms (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Four%20Rooms%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+4|Get Shorty (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Get%20Shorty%20(1995)|0|1|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+5|Copycat (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Copycat%20(1995)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|1|0|0
+6|Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)|01-Jan-1995||http://us.imdb.com/Title?Yao+a+yao+yao+dao+waipo+qiao+(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+7|Twelve Monkeys (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Twelve%20Monkeys%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0
+8|Babe (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Babe%20(1995)|0|0|0|0|1|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+9|Dead Man Walking (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Dead%20Man%20Walking%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+10|Richard III (1995)|22-Jan-1996||http://us.imdb.com/M/title-exact?Richard%20III%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+11|Seven (Se7en) (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Se7en%20(1995)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0
+12|Usual Suspects, The (1995)|14-Aug-1995||http://us.imdb.com/M/title-exact?Usual%20Suspects,%20The%20(1995)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0
+13|Mighty Aphrodite (1995)|30-Oct-1995||http://us.imdb.com/M/title-exact?Mighty%20Aphrodite%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+14|Postino, Il (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Postino,%20Il%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+15|Mr. Holland's Opus (1995)|29-Jan-1996||http://us.imdb.com/M/title-exact?Mr.%20Holland's%20Opus%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+16|French Twist (Gazon maudit) (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Gazon%20maudit%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+17|From Dusk Till Dawn (1996)|05-Feb-1996||http://us.imdb.com/M/title-exact?From%20Dusk%20Till%20Dawn%20(1996)|0|1|0|0|0|1|1|0|0|0|0|1|0|0|0|0|1|0|0
+18|White Balloon, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Badkonake%20Sefid%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+19|Antonia's Line (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Antonia%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+20|Angels and Insects (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Angels%20and%20Insects%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+21|Muppet Treasure Island (1996)|16-Feb-1996||http://us.imdb.com/M/title-exact?Muppet%20Treasure%20Island%20(1996)|0|1|1|0|0|1|0|0|0|0|0|0|1|0|0|0|1|0|0
+22|Braveheart (1995)|16-Feb-1996||http://us.imdb.com/M/title-exact?Braveheart%20(1995)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+23|Taxi Driver (1976)|16-Feb-1996||http://us.imdb.com/M/title-exact?Taxi%20Driver%20(1976)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+24|Rumble in the Bronx (1995)|23-Feb-1996||http://us.imdb.com/M/title-exact?Hong%20Faan%20Kui%20(1995)|0|1|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+25|Birdcage, The (1996)|08-Mar-1996||http://us.imdb.com/M/title-exact?Birdcage,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+26|Brothers McMullen, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Brothers%20McMullen,%20The%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+27|Bad Boys (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Bad%20Boys%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+28|Apollo 13 (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Apollo%2013%20(1995)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+29|Batman Forever (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Batman%20Forever%20(1995)|0|1|1|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0
+30|Belle de jour (1967)|01-Jan-1967||http://us.imdb.com/M/title-exact?Belle%20de%20jour%20(1967)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+31|Crimson Tide (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Crimson%20Tide%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0
+32|Crumb (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Crumb%20(1994)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+33|Desperado (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Desperado%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0
+34|Doom Generation, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Doom%20Generation,%20The%20(1995)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+35|Free Willy 2: The Adventure Home (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Free%20Willy%202:%20The%20Adventure%20Home%20(1995)|0|0|1|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+36|Mad Love (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Mad%20Love%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+37|Nadja (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Nadja%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+38|Net, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Net,%20The%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+39|Strange Days (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Strange%20Days%20(1995)|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0
+40|To Wong Foo, Thanks for Everything! Julie Newmar (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?To%20Wong%20Foo,%20Thanks%20for%20Everything!%20Julie%20Newmar%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+41|Billy Madison (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Billy%20Madison%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+42|Clerks (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Clerks%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+43|Disclosure (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Disclosure%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+44|Dolores Claiborne (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Dolores%20Claiborne%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+45|Eat Drink Man Woman (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Yinshi%20Nan%20Nu%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+46|Exotica (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Exotica%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+47|Ed Wood (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Ed%20Wood%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+48|Hoop Dreams (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Hoop%20Dreams%20(1994)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
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+51|Legends of the Fall (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Legends%20of%20the%20Fall%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|1|1
+52|Madness of King George, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Madness%20of%20King%20George,%20The%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+53|Natural Born Killers (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Natural%20Born%20Killers%20(1994)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+54|Outbreak (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Outbreak%20(1995)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
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+56|Pulp Fiction (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Pulp%20Fiction%20(1994)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
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+58|Quiz Show (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Quiz%20Show%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+59|Three Colors: Red (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Trzy%20kolory:%20Czerwony%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+63|Santa Clause, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Santa%20Clause,%20The%20(1994)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+64|Shawshank Redemption, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Shawshank%20Redemption,%20The%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+65|What's Eating Gilbert Grape (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?What's%20Eating%20Gilbert%20Grape%20(1993)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+66|While You Were Sleeping (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?While%20You%20Were%20Sleeping%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+67|Ace Ventura: Pet Detective (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Ace%20Ventura:%20Pet%20Detective%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+68|Crow, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Crow,%20The%20(1994)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0
+69|Forrest Gump (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Forrest%20Gump%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|1|0
+70|Four Weddings and a Funeral (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Four%20Weddings%20and%20a%20Funeral%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+71|Lion King, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Lion%20King,%20The%20(1994)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+72|Mask, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Mask,%20The%20(1994)|0|0|0|0|0|1|1|0|0|1|0|0|0|0|0|0|0|0|0
+73|Maverick (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Maverick%20(1994)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1
+74|Faster Pussycat! Kill! Kill! (1965)|01-Jan-1965||http://us.imdb.com/M/title-exact?Faster%20Pussycat!%20Kill!%20Kill!%20(1965)|0|1|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+75|Brother Minister: The Assassination of Malcolm X (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Brother%20Minister:%20The%20Assassination%20of%20Malcolm%20X%20(1994)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+76|Carlito's Way (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Carlito's%20Way%20(1993)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+77|Firm, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Firm,%20The%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+78|Free Willy (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Free%20Willy%20(1993)|0|0|1|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+79|Fugitive, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Fugitive,%20The%20(1993)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
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+81|Hudsucker Proxy, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Hudsucker%20Proxy,%20The%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+82|Jurassic Park (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Jurassic%20Park%20(1993)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+83|Much Ado About Nothing (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Much%20Ado%20About%20Nothing%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+84|Robert A. Heinlein's The Puppet Masters (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Robert%20A.%20Heinlein's%20The%20Puppet%20Masters%20(1994)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0
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+86|Remains of the Day, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Remains%20of%20the%20Day,%20The%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+87|Searching for Bobby Fischer (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Searching%20for%20Bobby%20Fischer%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+88|Sleepless in Seattle (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Sleepless%20in%20Seattle%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+89|Blade Runner (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Blade%20Runner%20(1982)|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0|0
+90|So I Married an Axe Murderer (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?So%20I%20Married%20an%20Axe%20Murderer%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|1|0|0
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+92|True Romance (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?True%20Romance%20(1993)|0|1|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0|0|0
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+96|Terminator 2: Judgment Day (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Terminator%202:%20Judgment%20Day%20(1991)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+97|Dances with Wolves (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Dances%20with%20Wolves%20(1990)|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1
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+101|Heavy Metal (1981)|08-Mar-1981||http://us.imdb.com/M/title-exact?Heavy%20Metal%20(1981)|0|1|1|1|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0
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+103|All Dogs Go to Heaven 2 (1996)|29-Mar-1996||http://us.imdb.com/M/title-exact?All%20Dogs%20Go%20to%20Heaven%202%20(1996)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
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+105|Sgt. Bilko (1996)|29-Mar-1996||http://us.imdb.com/M/title-exact?Sgt.%20Bilko%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+106|Diabolique (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?Diabolique%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
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+108|Kids in the Hall: Brain Candy (1996)|12-Apr-1996||http://us.imdb.com/M/title-exact?Kids%20in%20the%20Hall:%20Brain%20Candy%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+111|Truth About Cats & Dogs, The (1996)|26-Apr-1996||http://us.imdb.com/M/title-exact?Truth%20About%20Cats%20&%20Dogs,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
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+114|Wallace & Gromit: The Best of Aardman Animation (1996)|05-Apr-1996||http://us.imdb.com/Title?Wallace+%26+Gromit%3A+The+Best+of+Aardman+Animation+(1996)|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+115|Haunted World of Edward D. Wood Jr., The (1995)|26-Apr-1996||http://us.imdb.com/Title?Haunted+World+of+Edward+D.+Wood+Jr.,+The+(1995)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+116|Cold Comfort Farm (1995)|23-Apr-1996||http://us.imdb.com/M/title-exact?Cold%20Comfort%20Farm%20(1995)%20(TV)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+117|Rock, The (1996)|07-Jun-1996||http://us.imdb.com/M/title-exact?Rock,%20The%20(1996)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
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+119|Maya Lin: A Strong Clear Vision (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Maya%20Lin:%20A%20Strong%20Clear%20Vision%20(1994)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+120|Striptease (1996)|28-Jun-1996||http://us.imdb.com/M/title-exact?Striptease%20(1996)|0|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0
+121|Independence Day (ID4) (1996)|03-Jul-1996||http://us.imdb.com/M/title-exact?Independence%20Day%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0
+122|Cable Guy, The (1996)|14-Jun-1996||http://us.imdb.com/M/title-exact?Cable%20Guy,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+123|Frighteners, The (1996)|19-Jul-1996||http://us.imdb.com/M/title-exact?Frighteners,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0
+124|Lone Star (1996)|21-Jun-1996||http://us.imdb.com/M/title-exact?Lone%20Star%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0|0|0|0
+125|Phenomenon (1996)|29-Jun-1996||http://us.imdb.com/M/title-exact?Phenomenon%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+126|Spitfire Grill, The (1996)|06-Sep-1996||http://us.imdb.com/M/title-exact?Spitfire%20Grill,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+127|Godfather, The (1972)|01-Jan-1972||http://us.imdb.com/M/title-exact?Godfather,%20The%20(1972)|0|1|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+128|Supercop (1992)|26-Jul-1996||http://us.imdb.com/M/title-exact?Police%20Story%20III:%20Supercop%20(1992)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+129|Bound (1996)|04-Oct-1996||http://us.imdb.com/M/title-exact?Bound%20(1996)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|1|0|1|0|0
+130|Kansas City (1996)|16-Aug-1996||http://us.imdb.com/M/title-exact?Kansas%20City%20(1996)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+131|Breakfast at Tiffany's (1961)|01-Jan-1961||http://us.imdb.com/M/title-exact?Breakfast%20at%20Tiffany's%20(1961)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
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+133|Gone with the Wind (1939)|01-Jan-1939||http://us.imdb.com/M/title-exact?Gone%20with%20the%20Wind%20(1939)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|1|0
+134|Citizen Kane (1941)|01-Jan-1941||http://us.imdb.com/M/title-exact?Citizen%20Kane%20(1941)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+135|2001: A Space Odyssey (1968)|01-Jan-1968||http://us.imdb.com/M/title-exact?2001:%20A%20Space%20Odyssey%20(1968)|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|1|1|0|0
+136|Mr. Smith Goes to Washington (1939)|01-Jan-1939||http://us.imdb.com/M/title-exact?Mr.%20Smith%20Goes%20to%20Washington%20(1939)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+138|D3: The Mighty Ducks (1996)|04-Oct-1996||http://us.imdb.com/M/title-exact?D3:%20The%20Mighty%20Ducks%20(1996)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+139|Love Bug, The (1969)|01-Jan-1969||http://us.imdb.com/M/title-exact?Love%20Bug,%20The%20(1969)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+140|Homeward Bound: The Incredible Journey (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Homeward%20Bound:%20The%20Incredible%20Journey%20(1993)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+141|20,000 Leagues Under the Sea (1954)|01-Jan-1954||http://us.imdb.com/M/title-exact?20,000%20Leagues%20Under%20the%20Sea%20(1954)|0|0|1|0|1|0|0|0|0|1|0|0|0|0|0|1|0|0|0
+142|Bedknobs and Broomsticks (1971)|01-Jan-1971||http://us.imdb.com/M/title-exact?Bedknobs%20and%20Broomsticks%20(1971)|0|0|1|0|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+143|Sound of Music, The (1965)|01-Jan-1965||http://us.imdb.com/M/title-exact?Sound%20of%20Music,%20The%20(1965)|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+144|Die Hard (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Die%20Hard%20(1988)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+145|Lawnmower Man, The (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Lawnmower%20Man,%20The%20(1992)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+146|Unhook the Stars (1996)|30-Oct-1996||http://us.imdb.com/M/title-exact?Unhook%20the%20Stars%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+147|Long Kiss Goodnight, The (1996)|05-Oct-1996||http://us.imdb.com/M/title-exact?Long%20Kiss%20Goodnight,%20The%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+148|Ghost and the Darkness, The (1996)|11-Oct-1996||http://us.imdb.com/M/title-exact?Ghost%20and%20the%20Darkness,%20The%20(1996)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+149|Jude (1996)|01-Nov-1996||http://us.imdb.com/M/title-exact?Jude%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+150|Swingers (1996)|18-Oct-1996||http://us.imdb.com/M/title-exact?Swingers%20(1996)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+151|Willy Wonka and the Chocolate Factory (1971)|01-Jan-1971||http://us.imdb.com/M/title-exact?Willy%20Wonka%20and%20the%20Chocolate%20Factory%20(1971)|0|0|1|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+152|Sleeper (1973)|01-Jan-1973||http://us.imdb.com/M/title-exact?Sleeper%20(1973)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0
+153|Fish Called Wanda, A (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Fish%20Called%20Wanda,%20A%20(1988)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+154|Monty Python's Life of Brian (1979)|01-Jan-1979||http://us.imdb.com/M/title-exact?Life%20of%20Brian%20(1979)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+155|Dirty Dancing (1987)|01-Jan-1987||http://us.imdb.com/M/title-exact?Dirty%20Dancing%20(1987)|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0|0|0
+156|Reservoir Dogs (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Reservoir%20Dogs%20(1992)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0
+157|Platoon (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Platoon%20(1986)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+158|Weekend at Bernie's (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Weekend%20at%20Bernie's%20(1989)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+159|Basic Instinct (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Basic%20Instinct%20(1992)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+160|Glengarry Glen Ross (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Glengarry%20Glen%20Ross%20(1992)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+161|Top Gun (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Top%20Gun%20(1986)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+162|On Golden Pond (1981)|01-Jan-1981||http://us.imdb.com/M/title-exact?On%20Golden%20Pond%20(1981)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+163|Return of the Pink Panther, The (1974)|01-Jan-1974||http://us.imdb.com/M/title-exact?Return%20of%20the%20Pink%20Panther,%20The%20(1974)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+164|Abyss, The (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Abyss,%20The%20(1989)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+165|Jean de Florette (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Jean%20de%20Florette%20(1986)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+166|Manon of the Spring (Manon des sources) (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Manon%20des%20sources%20(1986)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+167|Private Benjamin (1980)|01-Jan-1980||http://us.imdb.com/M/title-exact?Private%20Benjamin%20(1980)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+168|Monty Python and the Holy Grail (1974)|01-Jan-1974||http://us.imdb.com/M/title-exact?Monty%20Python%20and%20the%20Holy%20Grail%20(1974)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+169|Wrong Trousers, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Wrong%20Trousers,%20The%20(1993)|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+170|Cinema Paradiso (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Nuovo%20cinema%20Paradiso%20(1988)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|1|0|0|0|0
+171|Delicatessen (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Delicatessen%20(1991)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0
+172|Empire Strikes Back, The (1980)|01-Jan-1980||http://us.imdb.com/M/title-exact?Empire%20Strikes%20Back,%20The%20(1980)|0|1|1|0|0|0|0|0|1|0|0|0|0|0|1|1|0|1|0
+173|Princess Bride, The (1987)|01-Jan-1987||http://us.imdb.com/M/title-exact?Princess%20Bride,%20The%20(1987)|0|1|1|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+174|Raiders of the Lost Ark (1981)|01-Jan-1981||http://us.imdb.com/M/title-exact?Raiders%20of%20the%20Lost%20Ark%20(1981)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+175|Brazil (1985)|01-Jan-1985||http://us.imdb.com/M/title-exact?Brazil%20(1985)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+176|Aliens (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Aliens%20(1986)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|1|0
+177|Good, The Bad and The Ugly, The (1966)|01-Jan-1966||http://us.imdb.com/M/title-exact?Buono,%20il%20brutto,%20il%20cattivo,%20Il%20(1966)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+178|12 Angry Men (1957)|01-Jan-1957||http://us.imdb.com/M/title-exact?12%20Angry%20Men%20(1957)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+179|Clockwork Orange, A (1971)|01-Jan-1971||http://us.imdb.com/M/title-exact?Clockwork%20Orange,%20A%20(1971)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+180|Apocalypse Now (1979)|01-Jan-1979||http://us.imdb.com/M/title-exact?Apocalypse%20Now%20(1979)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+181|Return of the Jedi (1983)|14-Mar-1997||http://us.imdb.com/M/title-exact?Return%20of%20the%20Jedi%20(1983)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|1|1|0|1|0
+182|GoodFellas (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?GoodFellas%20(1990)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+183|Alien (1979)|01-Jan-1979||http://us.imdb.com/M/title-exact?Alien%20(1979)|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0|1|1|0|0
+184|Army of Darkness (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Army%20of%20Darkness%20(1993)|0|1|1|0|0|1|0|0|0|0|0|1|0|0|0|1|0|0|0
+185|Psycho (1960)|01-Jan-1960||http://us.imdb.com/M/title-exact?Psycho%20(1960)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|1|0|0
+186|Blues Brothers, The (1980)|01-Jan-1980||http://us.imdb.com/M/title-exact?Blues%20Brothers,%20The%20(1980)|0|1|0|0|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0
+187|Godfather: Part II, The (1974)|01-Jan-1974||http://us.imdb.com/M/title-exact?Godfather:%20Part%20II,%20The%20(1974)|0|1|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+188|Full Metal Jacket (1987)|01-Jan-1987||http://us.imdb.com/M/title-exact?Full%20Metal%20Jacket%20(1987)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+189|Grand Day Out, A (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Grand%20Day%20Out,%20A%20(1992)|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+190|Henry V (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Henry%20V%20(1989)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+191|Amadeus (1984)|01-Jan-1984||http://us.imdb.com/M/title-exact?Amadeus%20(1984)|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0|0|0|0
+192|Raging Bull (1980)|01-Jan-1980||http://us.imdb.com/M/title-exact?Raging%20Bull%20(1980)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+193|Right Stuff, The (1983)|01-Jan-1983||http://us.imdb.com/M/title-exact?Right%20Stuff,%20The%20(1983)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+194|Sting, The (1973)|01-Jan-1973||http://us.imdb.com/M/title-exact?Sting,%20The%20(1973)|0|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0
+195|Terminator, The (1984)|01-Jan-1984||http://us.imdb.com/M/title-exact?Terminator,%20The%20(1984)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+196|Dead Poets Society (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Dead%20Poets%20Society%20(1989)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+197|Graduate, The (1967)|01-Jan-1967||http://us.imdb.com/M/title-exact?Graduate,%20The%20(1967)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+198|Nikita (La Femme Nikita) (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Nikita%20(1990)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+199|Bridge on the River Kwai, The (1957)|01-Jan-1957||http://us.imdb.com/M/title-exact?Bridge%20on%20the%20River%20Kwai,%20The%20(1957)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+200|Shining, The (1980)|01-Jan-1980||http://us.imdb.com/M/title-exact?Shining,%20The%20(1980)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+201|Evil Dead II (1987)|01-Jan-1987||http://us.imdb.com/M/title-exact?Evil%20Dead%20II%20(1987)|0|1|1|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0
+202|Groundhog Day (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Groundhog%20Day%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+203|Unforgiven (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Unforgiven%20(1992)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+204|Back to the Future (1985)|01-Jan-1985||http://us.imdb.com/M/title-exact?Back%20to%20the%20Future%20(1985)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0
+205|Patton (1970)|01-Jan-1970||http://us.imdb.com/M/title-exact?Patton%20(1970)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+206|Akira (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Akira%20(1988)|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+207|Cyrano de Bergerac (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Cyrano%20de%20Bergerac%20(1990)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+208|Young Frankenstein (1974)|01-Jan-1974||http://us.imdb.com/M/title-exact?Young%20Frankenstein%20(1974)|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0
+209|This Is Spinal Tap (1984)|01-Jan-1984||http://us.imdb.com/M/title-exact?This%20Is%20Spinal%20Tap%20(1984)|0|0|0|0|0|1|0|0|1|0|0|0|1|0|0|0|0|0|0
+210|Indiana Jones and the Last Crusade (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Indiana%20Jones%20and%20the%20Last%20Crusade%20(1989)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+211|M*A*S*H (1970)|01-Jan-1970||http://us.imdb.com/M/title-exact?MASH%20(1970)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0
+212|Unbearable Lightness of Being, The (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Unbearable%20Lightness%20of%20Being,%20The%20(1988)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+213|Room with a View, A (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Room%20with%20a%20View,%20A%20(1986)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+214|Pink Floyd - The Wall (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Pink%20Floyd%20-%20The%20Wall%20(1982)|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|1|0
+215|Field of Dreams (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Field%20of%20Dreams%20(1989)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+216|When Harry Met Sally... (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?When%20Harry%20Met%20Sally...%20(1989)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+217|Bram Stoker's Dracula (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Bram%20Stoker's%20Dracula%20(1992)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0|0|0
+218|Cape Fear (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Cape%20Fear%20(1991)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+219|Nightmare on Elm Street, A (1984)|01-Jan-1984||http://us.imdb.com/M/title-exact?Nightmare%20on%20Elm%20Street,%20A%20(1984)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+220|Mirror Has Two Faces, The (1996)|15-Nov-1996||http://us.imdb.com/M/title-exact?Mirror%20Has%20Two%20Faces,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+221|Breaking the Waves (1996)|15-Nov-1996||http://us.imdb.com/M/title-exact?Breaking%20the%20Waves%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+222|Star Trek: First Contact (1996)|22-Nov-1996||http://us.imdb.com/M/title-exact?Star%20Trek:%20First%20Contact%20(1996)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+223|Sling Blade (1996)|22-Nov-1996||http://us.imdb.com/M/title-exact?Sling%20Blade%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+224|Ridicule (1996)|27-Nov-1996||http://us.imdb.com/M/title-exact?Ridicule%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+225|101 Dalmatians (1996)|27-Nov-1996||http://us.imdb.com/M/title-exact?101%20Dalmatians%20(1996)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+226|Die Hard 2 (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Die%20Hard%202%20(1990)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+227|Star Trek VI: The Undiscovered Country (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Star%20Trek%20VI:%20The%20Undiscovered%20Country%20(1991)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+228|Star Trek: The Wrath of Khan (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Star%20Trek:%20The%20Wrath%20of%20Khan%20(1982)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+229|Star Trek III: The Search for Spock (1984)|01-Jan-1984||http://us.imdb.com/M/title-exact?Star%20Trek%20III:%20The%20Search%20for%20Spock%20(1984)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+230|Star Trek IV: The Voyage Home (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Star%20Trek%20IV:%20The%20Voyage%20Home%20(1986)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+231|Batman Returns (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Batman%20Returns%20(1992)|0|1|1|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0
+232|Young Guns (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Young%20Guns%20(1988)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1
+233|Under Siege (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Under%20Siege%20(1992)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
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+235|Mars Attacks! (1996)|13-Dec-1996||http://us.imdb.com/M/title-exact?Mars%20Attacks!%20(1996)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|1|0
+236|Citizen Ruth (1996)|13-Dec-1996||http://us.imdb.com/M/title-exact?Citizen%20Ruth%20(1996)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+237|Jerry Maguire (1996)|13-Dec-1996||http://us.imdb.com/M/title-exact?Jerry%20Maguire%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+238|Raising Arizona (1987)|01-Jan-1987||http://us.imdb.com/M/title-exact?Raising%20Arizona%20(1987)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+239|Sneakers (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Sneakers%20(1992)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|1|0|0|0
+240|Beavis and Butt-head Do America (1996)|20-Dec-1996||http://us.imdb.com/M/title-exact?Beavis%20and%20Butt-head%20Do%20America%20(1996)|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+242|Kolya (1996)|24-Jan-1997||http://us.imdb.com/M/title-exact?Kolya%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+244|Smilla's Sense of Snow (1997)|14-Mar-1997||http://us.imdb.com/M/title-exact?Smilla%27s%20Sense%20of%20Snow%20(1997)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+245|Devil's Own, The (1997)|26-Mar-1997||http://us.imdb.com/M/title-exact?Devil%27s%20Own%2C%20The%20(1997)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0
+246|Chasing Amy (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Chasing+Amy+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+247|Turbo: A Power Rangers Movie (1997)|28-Mar-1997||http://us.imdb.com/M/title-exact?Turbo%3A%20A%20Power%20Rangers%20Movie%20%281997%29|0|1|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+248|Grosse Pointe Blank (1997)|11-Apr-1997||http://us.imdb.com/M/title-exact?Grosse%20Pointe%20Blank%20%281997%29|0|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0
+249|Austin Powers: International Man of Mystery (1997)|02-May-1997||http://us.imdb.com/M/title-exact?Austin%20Powers%3A%20International%20Man%20of%20Mystery%20%281997%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+251|Shall We Dance? (1996)|11-Jul-1997||http://us.imdb.com/M/title-exact?Shall%20we%20DANSU%3F%20%281996%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+252|Lost World: Jurassic Park, The (1997)|23-May-1997||http://us.imdb.com/M/title-exact?Lost%20World%3A%20Jurassic%20Park%2C%20The%20%281997%29|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
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+254|Batman & Robin (1997)|20-Jun-1997||http://us.imdb.com/M/title-exact?Batman+%26+Robin+(1997)|0|1|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+255|My Best Friend's Wedding (1997)|20-Jun-1997||http://us.imdb.com/M/title-exact?My+Best+Friend%27s+Wedding+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
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+257|Men in Black (1997)|04-Jul-1997||http://us.imdb.com/M/title-exact?Men+in+Black+(1997)|0|1|1|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0
+258|Contact (1997)|11-Jul-1997||http://us.imdb.com/Title?Contact+(1997/I)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0
+259|George of the Jungle (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?George+of+the+Jungle+(1997)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+260|Event Horizon (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Event+Horizon+(1997)|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0|1|1|0|0
+261|Air Bud (1997)|01-Aug-1997||http://us.imdb.com/M/title-exact?Air+Bud+(1997)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+262|In the Company of Men (1997)|01-Aug-1997||http://us.imdb.com/M/title-exact?In+the+Company+of+Men+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+268|Chasing Amy (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Chasing+Amy+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+269|Full Monty, The (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Full+Monty%2C+The+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+270|Gattaca (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Gattaca+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|1|1|0|0
+271|Starship Troopers (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Starship+Troopers+(1997)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0
+272|Good Will Hunting (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?imdb-title-119217|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+273|Heat (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Heat%20(1995)|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0
+274|Sabrina (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Sabrina%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+275|Sense and Sensibility (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Sense%20and%20Sensibility%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+276|Leaving Las Vegas (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Leaving%20Las%20Vegas%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+277|Restoration (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Restoration%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+278|Bed of Roses (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?Bed%20of%20Roses%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+279|Once Upon a Time... When We Were Colored (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Once%20Upon%20a%20Time... When%20We%20Were%20Colored%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+280|Up Close and Personal (1996)|01-Mar-1996||http://us.imdb.com/M/title-exact?Up%20Close%20and%20Personal%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+281|River Wild, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?River%20Wild,%20The%20(1994)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
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+284|Tin Cup (1996)|16-Aug-1996||http://us.imdb.com/M/title-exact?Tin%20Cup%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+285|Secrets & Lies (1996)|04-Oct-1996||http://us.imdb.com/M/title-exact?Secrets%20&%20Lies%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+286|English Patient, The (1996)|15-Nov-1996||http://us.imdb.com/M/title-exact?English%20Patient,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|1|0
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+288|Scream (1996)|20-Dec-1996||http://us.imdb.com/M/title-exact?Scream%20(1996)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0
+289|Evita (1996)|25-Dec-1996||http://us.imdb.com/M/title-exact?Evita%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0
+290|Fierce Creatures (1997)|10-Jan-1997||http://us.imdb.com/M/title-exact?Fierce%20Creatures%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+291|Absolute Power (1997)|14-Feb-1997||http://us.imdb.com/M/title-exact?Absolute%20Power%20(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+292|Rosewood (1997)|21-Feb-1997||http://us.imdb.com/M/title-exact?Rosewood%20(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+295|Breakdown (1997)|02-May-1997||http://us.imdb.com/M/title-exact?Breakdown%20%281997%29|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+296|Promesse, La (1996)|16-May-1997||http://us.imdb.com/M/title-exact?Promesse%2C%20La%20%281996%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+298|Face/Off (1997)|27-Jun-1997||http://us.imdb.com/M/title-exact?Face/Off+(1997)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
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+304|Fly Away Home (1996)|13-Sep-1996||http://us.imdb.com/M/title-exact?Fly%20Away%20Home%20(1996)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
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+314|3 Ninjas: High Noon At Mega Mountain (1998)|01-Jan-1997||http://us.imdb.com/M/title-exact?imdb-title-118539|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+315|Apt Pupil (1998)|23-Oct-1998||http://us.imdb.com/Title?Apt+Pupil+(1998)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
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+320|Paradise Lost: The Child Murders at Robin Hood Hills (1996)|06-Dec-1996||http://us.imdb.com/M/title-exact?Paradise%20Lost%3a%20The%20Child%20Murders%20at%20Robin%20Hood%20Hills%20(1996)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
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+325|Crash (1996)|21-Mar-1997||http://us.imdb.com/M/title-exact?Crash%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+326|G.I. Jane (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?G%2EI%2E+Jane+(1997)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
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+377|Heavyweights (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Heavyweights%20(1994)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+426|Transformers: The Movie, The (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Transformers:%20The%20Movie,%20The%20(1986)|0|1|0|1|1|0|0|0|0|0|0|0|0|0|0|1|1|1|0
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+428|Harold and Maude (1971)|01-Jan-1971||http://us.imdb.com/M/title-exact?Harold%20and%20Maude%20(1971)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+429|Day the Earth Stood Still, The (1951)|01-Jan-1951||http://us.imdb.com/M/title-exact?Day%20the%20Earth%20Stood%20Still,%20The%20(1951)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0
+430|Duck Soup (1933)|01-Jan-1933||http://us.imdb.com/M/title-exact?Duck%20Soup%20(1933)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0
+431|Highlander (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Highlander%20(1986)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+432|Fantasia (1940)|01-Jan-1940||http://us.imdb.com/M/title-exact?Fantasia%20(1940)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+433|Heathers (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Heathers%20(1989)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+434|Forbidden Planet (1956)|01-Jan-1956||http://us.imdb.com/M/title-exact?Forbidden%20Planet%20(1956)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+435|Butch Cassidy and the Sundance Kid (1969)|01-Jan-1969||http://us.imdb.com/M/title-exact?Butch%20Cassidy%20and%20the%20Sundance%20Kid%20(1969)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1
+436|American Werewolf in London, An (1981)|01-Jan-1981||http://us.imdb.com/M/title-exact?American%20Werewolf%20in%20London,%20An%20(1981)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+437|Amityville 1992: It's About Time (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Amityville%201992:%20It's%20About%20Time%20(1992)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+438|Amityville 3-D (1983)|01-Jan-1983||http://us.imdb.com/M/title-exact?Amityville%203-D%20(1983)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+439|Amityville: A New Generation (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Amityville:%20A%20New%20Generation%20(1993)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+440|Amityville II: The Possession (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Amityville%20II:%20The%20Possession%20(1982)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+441|Amityville Horror, The (1979)|01-Jan-1979||http://us.imdb.com/M/title-exact?Amityville%20Horror,%20The%20(1979)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+442|Amityville Curse, The (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Amityville%20Curse,%20The%20(1990)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+443|Birds, The (1963)|01-Jan-1963||http://us.imdb.com/M/title-exact?Birds,%20The%20(1963)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+444|Blob, The (1958)|01-Jan-1958||http://us.imdb.com/M/title-exact?Blob,%20The%20(1958)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0
+445|Body Snatcher, The (1945)|01-Jan-1945||http://us.imdb.com/M/title-exact?Body%20Snatcher,%20The%20(1945)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+446|Burnt Offerings (1976)|01-Jan-1976||http://us.imdb.com/M/title-exact?Burnt%20Offerings%20(1976)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+447|Carrie (1976)|01-Jan-1976||http://us.imdb.com/M/title-exact?Carrie%20(1976)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+448|Omen, The (1976)|01-Jan-1976||http://us.imdb.com/M/title-exact?Omen,%20The%20(1976)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+449|Star Trek: The Motion Picture (1979)|01-Jan-1979||http://us.imdb.com/M/title-exact?Star%20Trek:%20The%20Motion%20Picture%20(1979)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+450|Star Trek V: The Final Frontier (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Star%20Trek%20V:%20The%20Final%20Frontier%20(1989)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+451|Grease (1978)|01-Jan-1978||http://us.imdb.com/M/title-exact?Grease%20(1978)|0|0|0|0|0|1|0|0|0|0|0|0|1|0|1|0|0|0|0
+452|Jaws 2 (1978)|01-Jan-1978||http://us.imdb.com/M/title-exact?Jaws%202%20(1978)|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+453|Jaws 3-D (1983)|01-Jan-1983||http://us.imdb.com/M/title-exact?Jaws%203-D%20(1983)|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+454|Bastard Out of Carolina (1996)|15-Dec-1996||http://us.imdb.com/M/title-exact?Bastard%20Out%20of%20Carolina%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+455|Jackie Chan's First Strike (1996)|10-Jan-1997||http://us.imdb.com/M/title-exact?Police%20Story%204:%20First%20Strike%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+456|Beverly Hills Ninja (1997)|17-Jan-1997||http://us.imdb.com/M/title-exact?Beverly%20Hills%20Ninja%20(1997)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+457|Free Willy 3: The Rescue (1997)|08-Aug-1997||http://us.imdb.com/M/title-exact?Free+Willy+3%3A+The+Rescue+(1997)|0|0|1|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+458|Nixon (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Nixon%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+459|Cry, the Beloved Country (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Cry,%20the%20Beloved%20Country%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+460|Crossing Guard, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Crossing%20Guard,%20The%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+461|Smoke (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Smoke%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+462|Like Water For Chocolate (Como agua para chocolate) (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Como%20agua%20para%20chocolate%20(1992)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+463|Secret of Roan Inish, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Secret%20of%20Roan%20Inish,%20The%20(1994)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+464|Vanya on 42nd Street (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Vanya%20on%2042nd%20Street%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+465|Jungle Book, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Jungle%20Book,%20The%20(1994)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+466|Red Rock West (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Red%20Rock%20West%20(1992)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+467|Bronx Tale, A (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Bronx%20Tale,%20A%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+468|Rudy (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Rudy%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+469|Short Cuts (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Short%20Cuts%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+470|Tombstone (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Tombstone%20(1993)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+471|Courage Under Fire (1996)|08-Mar-1996||http://us.imdb.com/M/title-exact?Courage%20Under%20Fire%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+472|Dragonheart (1996)|31-May-1996||http://us.imdb.com/M/title-exact?Dragonheart%20(1996)|0|1|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0
+473|James and the Giant Peach (1996)|12-Apr-1996||http://us.imdb.com/M/title-exact?James%20and%20the%20Giant%20Peach%20(1996)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+474|Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)|01-Jan-1963||http://us.imdb.com/M/title-exact?Dr.%20Strangelove%20or:%20How%20I%20Learned%20to%20Stop%20Worrying%20and%20Love%20the%20Bomb%20(1963)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0
+475|Trainspotting (1996)|19-Jul-1996||http://us.imdb.com/Title?Trainspotting+(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+476|First Wives Club, The (1996)|14-Sep-1996||http://us.imdb.com/M/title-exact?First%20Wives%20Club,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+477|Matilda (1996)|02-Aug-1996||http://us.imdb.com/M/title-exact?Matilda%20(1996)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+478|Philadelphia Story, The (1940)|01-Jan-1940||http://us.imdb.com/M/title-exact?Philadelphia%20Story,%20The%20(1940)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+479|Vertigo (1958)|01-Jan-1958||http://us.imdb.com/M/title-exact?Vertigo%20(1958)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+480|North by Northwest (1959)|01-Jan-1959||http://us.imdb.com/M/title-exact?North%20by%20Northwest%20(1959)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|1|0|0
+481|Apartment, The (1960)|01-Jan-1960||http://us.imdb.com/M/title-exact?Apartment,%20The%20(1960)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+482|Some Like It Hot (1959)|01-Jan-1959||http://us.imdb.com/M/title-exact?Some%20Like%20It%20Hot%20(1959)|0|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0
+483|Casablanca (1942)|01-Jan-1942||http://us.imdb.com/M/title-exact?Casablanca%20(1942)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|1|0
+484|Maltese Falcon, The (1941)|01-Jan-1941||http://us.imdb.com/M/title-exact?Maltese%20Falcon,%20The%20(1941)|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0|0|0|0
+485|My Fair Lady (1964)|01-Jan-1964||http://us.imdb.com/M/title-exact?My%20Fair%20Lady%20(1964)|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0|0|0
+486|Sabrina (1954)|01-Jan-1954||http://us.imdb.com/M/title-exact?Sabrina%20(1954)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+487|Roman Holiday (1953)|01-Jan-1953||http://us.imdb.com/M/title-exact?Roman%20Holiday%20(1953)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+488|Sunset Blvd. (1950)|01-Jan-1950||http://us.imdb.com/M/title-exact?Sunset%20Boulevard%20(1950)|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0
+489|Notorious (1946)|01-Jan-1946||http://us.imdb.com/M/title-exact?Notorious%20(1946)|0|0|0|0|0|0|0|0|0|0|1|0|0|0|1|0|1|0|0
+490|To Catch a Thief (1955)|01-Jan-1955||http://us.imdb.com/M/title-exact?To%20Catch%20a%20Thief%20(1955)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|1|0|0
+491|Adventures of Robin Hood, The (1938)|01-Jan-1938||http://us.imdb.com/M/title-exact?Adventures%20of%20Robin%20Hood,%20The%20(1938)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+492|East of Eden (1955)|01-Jan-1955||http://us.imdb.com/M/title-exact?East%20of%20Eden%20(1955)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+493|Thin Man, The (1934)|01-Jan-1934||http://us.imdb.com/M/title-exact?Thin%20Man,%20The%20(1934)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0
+494|His Girl Friday (1940)|01-Jan-1940||http://us.imdb.com/M/title-exact?His%20Girl%20Friday%20(1940)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+495|Around the World in 80 Days (1956)|01-Jan-1956||http://us.imdb.com/M/title-exact?Around%20the%20World%20in%2080%20Days%20(1956)|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+496|It's a Wonderful Life (1946)|01-Jan-1946||http://us.imdb.com/M/title-exact?It's%20a%20Wonderful%20Life%20(1946)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+497|Bringing Up Baby (1938)|01-Jan-1938||http://us.imdb.com/M/title-exact?Bringing%20Up%20Baby%20(1938)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+498|African Queen, The (1951)|01-Jan-1951||http://us.imdb.com/M/title-exact?African%20Queen,%20The%20(1951)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0
+499|Cat on a Hot Tin Roof (1958)|01-Jan-1958||http://us.imdb.com/M/title-exact?Cat%20on%20a%20Hot%20Tin%20Roof%20(1958)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+500|Fly Away Home (1996)|13-Sep-1996||http://us.imdb.com/M/title-exact?Fly%20Away%20Home%20(1996)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+501|Dumbo (1941)|01-Jan-1941||http://us.imdb.com/M/title-exact?Dumbo%20(1941)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+502|Bananas (1971)|01-Jan-1971||http://us.imdb.com/M/title-exact?Bananas%20(1971)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0
+503|Candidate, The (1972)|01-Jan-1972||http://us.imdb.com/M/title-exact?Candidate,%20The%20(1972)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+504|Bonnie and Clyde (1967)|01-Jan-1967||http://us.imdb.com/M/title-exact?Bonnie%20and%20Clyde%20(1967)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+505|Dial M for Murder (1954)|01-Jan-1954||http://us.imdb.com/M/title-exact?Dial%20M%20for%20Murder%20(1954)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+506|Rebel Without a Cause (1955)|01-Jan-1955||http://us.imdb.com/M/title-exact?Rebel%20Without%20a%20Cause%20(1955)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+507|Streetcar Named Desire, A (1951)|01-Jan-1951||http://us.imdb.com/M/title-exact?Streetcar%20Named%20Desire,%20A%20(1951)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+508|People vs. Larry Flynt, The (1996)|27-Dec-1996||http://us.imdb.com/M/title-exact?People%20vs.%20Larry%20Flynt,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+509|My Left Foot (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?My%20Left%20Foot%20(1989)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+510|Magnificent Seven, The (1954)|01-Jan-1954||http://us.imdb.com/M/title-exact?Shichinin%20no%20samurai%20(1954)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1
+511|Lawrence of Arabia (1962)|01-Jan-1962||http://us.imdb.com/M/title-exact?Lawrence%20of%20Arabia%20(1962)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0
+512|Wings of Desire (1987)|01-Jan-1987||http://us.imdb.com/Title?Himmel+%FCber+Berlin,+Der+(1987)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|1|0|0|0|0
+513|Third Man, The (1949)|01-Jan-1949||http://us.imdb.com/M/title-exact?Third%20Man,%20The%20(1949)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+514|Annie Hall (1977)|01-Jan-1977||http://us.imdb.com/M/title-exact?Annie%20Hall%20(1977)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+515|Boot, Das (1981)|04-Apr-1997||http://us.imdb.com/M/title-exact?Boot,%20Das%20(1981)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+516|Local Hero (1983)|01-Jan-1983||http://us.imdb.com/M/title-exact?Local%20Hero%20(1983)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+517|Manhattan (1979)|01-Jan-1979||http://us.imdb.com/M/title-exact?Manhattan%20(1979)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|1|0|0|0|0
+518|Miller's Crossing (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Miller's%20Crossing%20(1990)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+519|Treasure of the Sierra Madre, The (1948)|01-Jan-1948||http://us.imdb.com/M/title-exact?Treasure%20of%20the%20Sierra%20Madre,%20The%20(1948)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+520|Great Escape, The (1963)|01-Jan-1963||http://us.imdb.com/M/title-exact?Great%20Escape,%20The%20(1963)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0
+521|Deer Hunter, The (1978)|01-Jan-1978||http://us.imdb.com/M/title-exact?Deer%20Hunter,%20The%20(1978)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+522|Down by Law (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Down%20by%20Law%20(1986)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+523|Cool Hand Luke (1967)|01-Jan-1967||http://us.imdb.com/M/title-exact?Cool%20Hand%20Luke%20(1967)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+524|Great Dictator, The (1940)|01-Jan-1940||http://us.imdb.com/M/title-exact?Great%20Dictator,%20The%20(1940)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+525|Big Sleep, The (1946)|01-Jan-1946||http://us.imdb.com/M/title-exact?Big%20Sleep,%20The%20(1946)|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0|0|0|0
+526|Ben-Hur (1959)|01-Jan-1959||http://us.imdb.com/M/title-exact?Ben-Hur%20(1959)|0|1|1|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+527|Gandhi (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Gandhi%20(1982)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+528|Killing Fields, The (1984)|01-Jan-1984||http://us.imdb.com/M/title-exact?Killing%20Fields,%20The%20(1984)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+529|My Life as a Dog (Mitt liv som hund) (1985)|01-Jan-1985||http://us.imdb.com/M/title-exact?Mitt%20liv%20som%20hund%20(1985)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+530|Man Who Would Be King, The (1975)|01-Jan-1975||http://us.imdb.com/M/title-exact?Man%20Who%20Would%20Be%20King,%20The%20(1975)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+531|Shine (1996)|22-Nov-1996||http://us.imdb.com/M/title-exact?Shine%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+532|Kama Sutra: A Tale of Love (1996)|07-Mar-1997||http://us.imdb.com/M/title-exact?Kama%20Sutra%20(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+533|Daytrippers, The (1996)|21-Mar-1997||http://us.imdb.com/M/title-exact?Daytrippers%2C%20The%20(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0
+534|Traveller (1997)|18-Apr-1997||http://us.imdb.com/M/title-exact?Traveller%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+535|Addicted to Love (1997)|23-May-1997||http://us.imdb.com/M/title-exact?Addicted%20to%20Love%20%281997%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+536|Ponette (1996)|23-May-1997||http://us.imdb.com/M/title-exact?Ponette%20%281996%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+537|My Own Private Idaho (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?My+Own+Private+Idaho+(1991)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+538|Anastasia (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Anastasia+(1997)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+539|Mouse Hunt (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?imdb-title-119715|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+540|Money Train (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Money%20Train%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+541|Mortal Kombat (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Mortal%20Kombat%20(1995)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+542|Pocahontas (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Pocahontas%20(1995)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|1|0|0|0|0
+543|Mis�rables, Les (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Mis%E9rables%2C%20Les%20%281995%29|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0
+544|Things to Do in Denver when You're Dead (1995)|02-Feb-1996||http://us.imdb.com/M/title-exact?Things%20to%20Do%20in%20Denver%20when%20You're%20Dead%20(1995)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|1|0|0|0|0
+545|Vampire in Brooklyn (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Vampire%20in%20Brooklyn%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+546|Broken Arrow (1996)|09-Feb-1996||http://us.imdb.com/M/title-exact?Broken%20Arrow%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+547|Young Poisoner's Handbook, The (1995)|23-Feb-1996||http://us.imdb.com/M/title-exact?Young%20Poisoner's%20Handbook,%20The%20(1995)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+548|NeverEnding Story III, The (1994)|02-Feb-1996||http://us.imdb.com/M/title-exact?NeverEnding%20Story%20III,%20The%20(1994)|0|0|0|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0
+549|Rob Roy (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Rob%20Roy%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|1|0
+550|Die Hard: With a Vengeance (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Die%20Hard:%20With%20a%20Vengeance%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+551|Lord of Illusions (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Lord%20of%20Illusions%20(1995)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
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+553|Walk in the Clouds, A (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Walk%20in%20the%20Clouds,%20A%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+554|Waterworld (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Waterworld%20(1995)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+555|White Man's Burden (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?White%20Man's%20Burden%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+556|Wild Bill (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Wild%20Bill%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+557|Farinelli: il castrato (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Farinelli:%20il%20castrato%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0
+558|Heavenly Creatures (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Heavenly%20Creatures%20(1994)|0|0|0|0|0|0|0|0|1|1|0|0|0|0|0|0|1|0|0
+559|Interview with the Vampire (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Interview%20with%20the%20Vampire%20(1994)|0|0|0|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0
+560|Kid in King Arthur's Court, A (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Kid%20in%20King%20Arthur's%20Court,%20A%20(1995)|0|0|1|0|1|1|0|0|0|1|0|0|0|0|1|1|0|0|0
+561|Mary Shelley's Frankenstein (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Mary%20Shelley's%20Frankenstein%20(1994)|0|0|0|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0
+562|Quick and the Dead, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Quick%20and%20the%20Dead,%20The%20(1995)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+563|Stephen King's The Langoliers (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?%22Langoliers,%20The%22%20(1995)%20(mini)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+564|Tales from the Hood (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Tales%20from%20the%20Hood%20(1995)|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0
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+566|Clear and Present Danger (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Clear%20and%20Present%20Danger%20(1994)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+567|Wes Craven's New Nightmare (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Wes%20Craven's%20New%20Nightmare%20(1994)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+568|Speed (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Speed%20(1994/I)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0
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+571|Another Stakeout (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Another%20Stakeout%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|1|0|0
+572|Blown Away (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Blown%20Away%20(1994)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+573|Body Snatchers (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Body%20Snatchers%20(1993)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|1|1|0|0
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+575|City Slickers II: The Legend of Curly's Gold (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?City%20Slickers%20II:%20The%20Legend%20of%20Curly's%20Gold%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1
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+577|Coneheads (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Coneheads%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0
+578|Demolition Man (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Demolition%20Man%20(1993)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
+579|Fatal Instinct (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Fatal%20Instinct%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+580|Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Englishman%20Who%20Went%20Up%20a%20Hill,%20But%20Came%20Down%20a%20Mountain,%20The%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
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+582|Piano, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Piano,%20The%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+583|Romeo Is Bleeding (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Romeo%20Is%20Bleeding%20(1993)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0
+584|Secret Garden, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Secret%20Garden,%20The%20(1993)|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+585|Son in Law (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Son%20in%20Law%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+586|Terminal Velocity (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Terminal%20Velocity%20(1994)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+587|Hour of the Pig, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Hour%20of%20the%20Pig,%20The%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0|0|0|0
+588|Beauty and the Beast (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Beauty%20and%20the%20Beast%20(1991)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
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+590|Hellraiser: Bloodline (1996)|08-Mar-1996||http://us.imdb.com/M/title-exact?Hellraiser:%20Bloodline%20(1996)|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0
+591|Primal Fear (1996)|30-Mar-1996||http://us.imdb.com/M/title-exact?Primal%20Fear%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
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+600|Daniel Defoe's Robinson Crusoe (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?Robinson%20Crusoe%20(1996)|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+601|For Whom the Bell Tolls (1943)|01-Jan-1943||http://us.imdb.com/M/title-exact?For%20Whom%20the%20Bell%20Tolls%20(1943)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0
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+604|It Happened One Night (1934)|01-Jan-1934||http://us.imdb.com/M/title-exact?It%20Happened%20One%20Night%20(1934)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+619|Extreme Measures (1996)|27-Sep-1996||http://us.imdb.com/M/title-exact?Extreme%20Measures%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+620|Chamber, The (1996)|11-Oct-1996||http://us.imdb.com/M/title-exact?Chamber,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+621|Davy Crockett, King of the Wild Frontier (1955)|01-Jan-1955||http://us.imdb.com/M/title-exact?Davy%20Crockett%2C%20King%20of%20the%20Wild%20Frontier%20%281955%29|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+622|Swiss Family Robinson (1960)|01-Jan-1960||http://us.imdb.com/M/title-exact?Swiss%20Family%20Robinson%20(1960)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+623|Angels in the Outfield (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Angels%20in%20the%20Outfield%20(1994)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+624|Three Caballeros, The (1945)|01-Jan-1945||http://us.imdb.com/M/title-exact?Three%20Caballeros,%20The%20(1945)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+625|Sword in the Stone, The (1963)|01-Jan-1963||http://us.imdb.com/M/title-exact?Sword%20in%20the%20Stone,%20The%20(1963)|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+626|So Dear to My Heart (1949)|01-Jan-1949||http://us.imdb.com/Title?So+Dear+to+My+Heart+(1949)|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+627|Robin Hood: Prince of Thieves (1991)|01-Jan-1991||http://us.imdb.com/Title?Robin+Hood%3A+Prince+of+Thieves+(1991)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+628|Sleepers (1996)|18-Oct-1996||http://us.imdb.com/M/title-exact?Sleepers%20(1996)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+629|Victor/Victoria (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Victor/Victoria%20%281982%29|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0
+630|Great Race, The (1965)|01-Jan-1965||http://us.imdb.com/M/title-exact?Great%20Race,%20The%20(1965)|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0
+631|Crying Game, The (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Crying%20Game,%20The%20(1992)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|1|0
+632|Sophie's Choice (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Sophie's%20Choice%20(1982)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+633|Christmas Carol, A (1938)|01-Jan-1938||http://us.imdb.com/M/title-exact?Christmas%20Carol,%20A%20(1938)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+634|Microcosmos: Le peuple de l'herbe (1996)|11-Oct-1996||http://us.imdb.com/M/title-exact?Microcosmos%3A%20Le%20peuple%20de%20l%27herbe%20%281996%29|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+635|Fog, The (1980)|01-Jan-1980||http://us.imdb.com/M/title-exact?Fog,%20The%20(1980)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+636|Escape from New York (1981)|01-Jan-1981||http://us.imdb.com/M/title-exact?Escape%20from%20New%20York%20(1981)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+637|Howling, The (1981)|01-Jan-1981||http://us.imdb.com/M/title-exact?Howling,%20The%20(1981)|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0
+638|Return of Martin Guerre, The (Retour de Martin Guerre, Le) (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Retour%20de%20Martin%20Guerre,%20Le%20(1982)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+639|Tin Drum, The (Blechtrommel, Die) (1979)|01-Jan-1979||http://us.imdb.com/M/title-exact?Blechtrommel,%20Die%20(1979)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+640|Cook the Thief His Wife & Her Lover, The (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Cook%20the%20Thief%20His%20Wife%20&%20Her%20Lover,%20The%20(1989)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+641|Paths of Glory (1957)|01-Jan-1957||http://us.imdb.com/M/title-exact?Paths%20of%20Glory%20(1957)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+642|Grifters, The (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Grifters,%20The%20(1990)|0|0|0|0|0|0|1|0|1|0|1|0|0|0|0|0|0|0|0
+643|The Innocent (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Innocent,%20The%20(1994)%20(TV)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+644|Thin Blue Line, The (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Thin%20Blue%20Line,%20The%20(1988)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+645|Paris Is Burning (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Paris%20Is%20Burning%20(1990)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+646|Once Upon a Time in the West (1969)|01-Jan-1969||http://us.imdb.com/M/title-exact?C'era%20una%20volta%20il%20west%20(1969)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+647|Ran (1985)|01-Jan-1985||http://us.imdb.com/M/title-exact?Ran%20(1985)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+648|Quiet Man, The (1952)|01-Jan-1952||http://us.imdb.com/M/title-exact?Quiet%20Man,%20The%20(1952)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+649|Once Upon a Time in America (1984)|01-Jan-1984||http://us.imdb.com/M/title-exact?Once%20Upon%20a%20Time%20in%20America%20(1984)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|1|0|0
+650|Seventh Seal, The (Sjunde inseglet, Det) (1957)|01-Jan-1957||http://us.imdb.com/M/title-exact?Sjunde%20inseglet,%20Det%20(1957)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+651|Glory (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Glory%20(1989)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+652|Rosencrantz and Guildenstern Are Dead (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Rosencrantz%20and%20Guildenstern%20Are%20Dead%20(1990)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+653|Touch of Evil (1958)|01-Jan-1958||http://us.imdb.com/M/title-exact?Touch%20of%20Evil%20(1958)|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|1|0|0
+654|Chinatown (1974)|01-Jan-1974||http://us.imdb.com/M/title-exact?Chinatown%20(1974)|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0|1|0|0
+655|Stand by Me (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Stand%20by%20Me%20(1986)|0|0|1|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+656|M (1931)|01-Jan-1931||http://us.imdb.com/M/title-exact?M%20(1931)|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|1|0|0
+657|Manchurian Candidate, The (1962)|01-Jan-1962||http://us.imdb.com/M/title-exact?Manchurian%20Candidate,%20The%20(1962)|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0
+658|Pump Up the Volume (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Pump%20Up%20the%20Volume%20(1990)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+659|Arsenic and Old Lace (1944)|01-Jan-1944||http://us.imdb.com/M/title-exact?Arsenic%20and%20Old%20Lace%20(1944)|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0|1|0|0
+660|Fried Green Tomatoes (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Fried%20Green%20Tomatoes%20at%20the%20Whistle%20Stop%20Cafe%20(1991)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+661|High Noon (1952)|01-Jan-1952||http://us.imdb.com/M/title-exact?High%20Noon%20(1952)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+662|Somewhere in Time (1980)|01-Jan-1980||http://us.imdb.com/M/title-exact?Somewhere%20in%20Time%20(1980)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+663|Being There (1979)|01-Jan-1979||http://us.imdb.com/M/title-exact?Being%20There%20(1979)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+664|Paris, Texas (1984)|01-Jan-1984||http://us.imdb.com/M/title-exact?Paris,%20Texas%20(1984)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+665|Alien 3 (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Alien%203%20(1992)|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0|1|1|0|0
+666|Blood For Dracula (Andy Warhol's Dracula) (1974)|01-Jan-1974||http://us.imdb.com/M/title-exact?Andy%20Warhol's%20Dracula%20(1974)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+667|Audrey Rose (1977)|01-Jan-1977||http://us.imdb.com/M/title-exact?Audrey%20Rose%20(1977)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+668|Blood Beach (1981)|01-Jan-1981||http://us.imdb.com/M/title-exact?Blood%20Beach%20(1981)|0|1|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+669|Body Parts (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Body%20Parts%20(1991)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+670|Body Snatchers (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Body%20Snatchers%20(1993)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|1|1|0|0
+671|Bride of Frankenstein (1935)|01-Jan-1935||http://us.imdb.com/M/title-exact?Bride%20of%20Frankenstein%20(1935)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+672|Candyman (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Candyman%20(1992)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+673|Cape Fear (1962)|01-Jan-1962||http://us.imdb.com/M/title-exact?Cape%20Fear%20(1962)|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0
+674|Cat People (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Cat%20People%20(1982)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+675|Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)|01-Jan-1922||http://us.imdb.com/M/title-exact?Nosferatu,%20eine%20Symphonie%20des%20Grauens%20(1922)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+676|Crucible, The (1996)|27-Nov-1996||http://us.imdb.com/M/title-exact?Crucible,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+677|Fire on the Mountain (1996)|24-Jan-1997||http://us.imdb.com/M/title-exact?Fire%20on%20the%20Mountain%20(1996)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+678|Volcano (1997)|25-Apr-1997||http://us.imdb.com/M/title-exact?Volcano%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+679|Conan the Barbarian (1981)|01-Jan-1981||http://us.imdb.com/M/title-exact?Conan+the+Barbarian+(1981)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+680|Kull the Conqueror (1997)|29-Aug-1997||http://us.imdb.com/M/title-exact?Kull+the+Conqueror+(1997)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+681|Wishmaster (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Wishmaster+(1997)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+682|I Know What You Did Last Summer (1997)|17-Oct-1997||http://us.imdb.com/M/title-exact?I+Know+What+You+Did+Last+Summer+(1997)|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0|1|0|0
+683|Rocket Man (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Rocket+Man+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+684|In the Line of Fire (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?In%20the%20Line%20of%20Fire%20(1993)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+685|Executive Decision (1996)|09-Mar-1996||http://us.imdb.com/M/title-exact?Executive%20Decision%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+686|Perfect World, A (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Perfect%20World,%20A%20(1993)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+687|McHale's Navy (1997)|18-Apr-1997||http://us.imdb.com/M/title-exact?McHale's%20Navy%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0
+688|Leave It to Beaver (1997)|22-Aug-1997||http://us.imdb.com/M/title-exact?Leave+It+To+Beaver+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+689|Jackal, The (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Jackal%2C+The+(1997)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+690|Seven Years in Tibet (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Seven+Years+in+Tibet+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+691|Dark City (1998)|09-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-118929|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|1|1|0|0
+692|American President, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?American%20President,%20The%20(1995)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|1|0|0|0|0
+693|Casino (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Casino%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+694|Persuasion (1995)|25-Sep-1995||http://us.imdb.com/Title?Persuasion+(1995/I)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+695|Kicking and Screaming (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Kicking%20and%20Screaming%20(1995)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+696|City Hall (1996)|16-Feb-1996||http://us.imdb.com/M/title-exact?City%20Hall%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+697|Basketball Diaries, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Basketball%20Diaries,%20The%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+698|Browning Version, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Browning%20Version,%20The%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+699|Little Women (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Little%20Women%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+700|Miami Rhapsody (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Miami%20Rhapsody%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+701|Wonderful, Horrible Life of Leni Riefenstahl, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Macht%20der%20Bilder:%20Leni%20Riefenstahl,%20Die%20(1993)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+702|Barcelona (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Barcelona%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+703|Widows' Peak (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Widows'%20Peak%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+704|House of the Spirits, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?House%20of%20the%20Spirits,%20The%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+705|Singin' in the Rain (1952)|01-Jan-1952||http://us.imdb.com/M/title-exact?Singin'%20in%20the%20Rain%20(1952)|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0|0|0
+706|Bad Moon (1996)|01-Nov-1996||http://us.imdb.com/M/title-exact?Bad%20Moon%20(1996)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+707|Enchanted April (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Enchanted%20April%20(1991)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+708|Sex, Lies, and Videotape (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?sex,%20lies,%20and%20videotape%20(1989)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+811|Thirty-Two Short Films About Glenn Gould (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Thirty-Two%20Short%20Films%20About%20Glenn%20Gould%20(1993)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
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+823|Mulholland Falls (1996)|26-Apr-1996||http://us.imdb.com/M/title-exact?Mulholland%20Falls%20(1996)|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|1|0|0
+824|Great White Hype, The (1996)|03-May-1996||http://us.imdb.com/M/title-exact?Great%20White%20Hype,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+827|Daylight (1996)|06-Dec-1996||http://us.imdb.com/M/title-exact?Daylight%20(1996)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+828|Alaska (1996)|21-Aug-1996||http://us.imdb.com/M/title-exact?Alaska%20(1996)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
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+834|Halloween: The Curse of Michael Myers (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Halloween:%20The%20Curse%20of%20Michael%20Myers%20(1995)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0
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+845|That Thing You Do! (1996)|28-Sep-1996||http://us.imdb.com/M/title-exact?That%20Thing%20You%20Do!%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+868|Hearts and Minds (1996)|10-Jan-1997||http://us.imdb.com/M/title-exact?Hearts%20and%20Minds%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+905|Great Expectations (1998)|01-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-119223|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+906|Oscar & Lucinda (1997)|31-Dec-1997||http://us.imdb.com/M/title-exact?imdb-title-119843|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+907|Vermin (1998)|01-Jan-1997||http://us.imdb.com/M/title-exact?imdb-title-120881|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+908|Half Baked (1998)|16-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-120693|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+909|Dangerous Beauty (1998)|23-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-118892|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+910|Nil By Mouth (1997)|06-Feb-1998||http://us.imdb.com/Title?Nil+By+Mouth+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+911|Twilight (1998)|30-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-119594|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+912|U.S. Marshalls (1998)|10-Mar-1998||http://us.imdb.com/Title?U.S.+Marshals+(1998)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+913|Love and Death on Long Island (1997)|10-Mar-1998||http://us.imdb.com/Title?Love+and+Death+on+Long+Island+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+914|Wild Things (1998)|14-Mar-1998||http://us.imdb.com/Title?Wild+Things+(1998)|0|0|0|0|0|0|1|0|1|0|0|0|0|1|0|0|1|0|0
+915|Primary Colors (1998)|20-Mar-1998||http://us.imdb.com/Title?Primary+Colors+(1998)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+916|Lost in Space (1998)|27-Mar-1998||http://us.imdb.com/Title?Lost+in+Space+(1998)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+917|Mercury Rising (1998)|27-Mar-1998||http://us.imdb.com/Title?Mercury+Rising+(1998)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+918|City of Angels (1998)|03-Apr-1998||http://us.imdb.com/Title?City+of+Angels+(1998)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+919|City of Lost Children, The (1995)|01-Jan-1995||http://us.imdb.com/Title?Cit%E9+des+enfants+perdus,+La+(1995)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
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+921|Farewell My Concubine (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Ba%20Wang%20Bie%20Ji%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+922|Dead Man (1995)|10-May-1996||http://us.imdb.com/M/title-exact?Dead%20Man%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+923|Raise the Red Lantern (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Da%20Hong%20Deng%20Long%20Gao%20Gao%20Gua%20(1991)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+924|White Squall (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?White%20Squall%20(1996)|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+925|Unforgettable (1996)|23-Feb-1996||http://us.imdb.com/Title?Unforgettable+(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+926|Down Periscope (1996)|01-Mar-1996||http://us.imdb.com/M/title-exact?Down%20Periscope%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+927|Flower of My Secret, The (Flor de mi secreto, La) (1995)|08-Mar-1996||http://us.imdb.com/M/title-exact?Flor%20de%20mi%20secreto,%20La%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+928|Craft, The (1996)|26-Apr-1996||http://us.imdb.com/M/title-exact?Craft,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0
+929|Harriet the Spy (1996)|03-Jul-1996||http://us.imdb.com/M/title-exact?Harriet%20the%20Spy%20(1996)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+930|Chain Reaction (1996)|31-Jul-1996||http://us.imdb.com/M/title-exact?Chain%20Reaction%20(1996)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+931|Island of Dr. Moreau, The (1996)|23-Aug-1996||http://us.imdb.com/M/title-exact?Island%20of%20Dr.%20Moreau,%20The%20(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+932|First Kid (1996)|30-Aug-1996||http://us.imdb.com/M/title-exact?First%20Kid%20(1996)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+933|Funeral, The (1996)|25-Oct-1996||http://us.imdb.com/M/title-exact?Funeral,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+934|Preacher's Wife, The (1996)|13-Dec-1996||http://us.imdb.com/M/title-exact?Preacher's%20Wife,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+935|Paradise Road (1997)|18-Apr-1997||http://us.imdb.com/M/title-exact?Paradise%20Road%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+936|Brassed Off (1996)|13-Jun-1997||http://us.imdb.com/M/title-exact?Brassed%20Off%20%281996%29|0|0|0|0|0|1|0|0|1|0|0|0|0|0|1|0|0|0|0
+937|Thousand Acres, A (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Thousand+Acres%2C+A+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+938|Smile Like Yours, A (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Smile+Like+Yours%2C+A+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+939|Murder in the First (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Murder%20in%20the%20First%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+940|Airheads (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Airheads%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+941|With Honors (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?With%20Honors%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+942|What's Love Got to Do with It (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?What's%20Love%20Got%20to%20Do%20with%20It%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+943|Killing Zoe (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Killing%20Zoe%20(1994)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+944|Renaissance Man (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Renaissance%20Man%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|1|0
+945|Charade (1963)|01-Jan-1963||http://us.imdb.com/M/title-exact?Charade%20(1963)|0|0|0|0|0|1|0|0|0|0|0|0|0|1|1|0|1|0|0
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+947|Big Blue, The (Grand bleu, Le) (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Grand%20bleu,%20Le%20(1988)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+948|Booty Call (1997)|28-Feb-1997||http://us.imdb.com/M/title-exact?Booty%20Call%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+949|How to Make an American Quilt (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?How%20to%20Make%20an%20American%20Quilt%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+950|Georgia (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Georgia%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+951|Indian in the Cupboard, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Indian%20in%20the%20Cupboard,%20The%20(1995)|0|0|1|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0
+952|Blue in the Face (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Blue%20in%20the%20Face%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+953|Unstrung Heroes (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Unstrung%20Heroes%20(1995)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+954|Unzipped (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Unzipped%20(1995)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+955|Before Sunrise (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Before%20Sunrise%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+956|Nobody's Fool (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Nobody's%20Fool%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+957|Pushing Hands (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Tui%20Shou%20(1992)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+958|To Live (Huozhe) (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Huozhe%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+959|Dazed and Confused (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Dazed%20and%20Confused%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+960|Naked (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Naked%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+961|Orlando (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Orlando%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+962|Ruby in Paradise (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Ruby%20in%20Paradise%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+963|Some Folks Call It a Sling Blade (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Some%20Folks%20Call%20It%20a%20Sling%20Blade%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+964|Month by the Lake, A (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Month%20by%20The%20Lake,%20A%20(1995)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+965|Funny Face (1957)|01-Jan-1957||http://us.imdb.com/M/title-exact?Funny%20Face%20(1957)|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0
+966|Affair to Remember, An (1957)|01-Jan-1957||http://us.imdb.com/M/title-exact?Affair%20to%20Remember,%20An%20(1957)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+967|Little Lord Fauntleroy (1936)|01-Jan-1936||http://us.imdb.com/M/title-exact?Little%20Lord%20Fauntleroy%20(1936)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+968|Inspector General, The (1949)|01-Jan-1949||http://us.imdb.com/M/title-exact?Inspector%20General,%20The%20(1949)|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+969|Winnie the Pooh and the Blustery Day (1968)|01-Jan-1968||http://us.imdb.com/M/title-exact?Winnie%20the%20Pooh%20and%20the%20Blustery%20Day%20%281968%29|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
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+971|Mediterraneo (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Mediterraneo%20(1991)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0
+972|Passion Fish (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Passion%20Fish%20(1992)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+973|Grateful Dead (1995)|18-Oct-1996||http://us.imdb.com/M/title-exact?Grateful%20Dead%20(1995)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+974|Eye for an Eye (1996)|01-Jan-1996||http://us.imdb.com/Title?Eye+for+an+Eye+(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+975|Fear (1996)|12-Apr-1996||http://us.imdb.com/M/title-exact?Fear%20(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+976|Solo (1996)|23-Aug-1996||http://us.imdb.com/M/title-exact?Solo%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+977|Substitute, The (1996)|19-Apr-1996||http://us.imdb.com/M/title-exact?Substitute,%20The%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+978|Heaven's Prisoners (1996)|10-May-1996||http://us.imdb.com/M/title-exact?Heaven's%20Prisoners%20(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+979|Trigger Effect, The (1996)|30-Aug-1996||http://us.imdb.com/M/title-exact?Trigger%20Effect,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+980|Mother Night (1996)|01-Nov-1996||http://us.imdb.com/M/title-exact?Mother%20Night%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+981|Dangerous Ground (1997)|04-Sep-1996||http://us.imdb.com/M/title-exact?Dangerous%20Ground%20(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+982|Maximum Risk (1996)|13-Sep-1996||http://us.imdb.com/M/title-exact?Maximum%20Risk%20(1996)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+983|Rich Man's Wife, The (1996)|13-Sep-1996||http://us.imdb.com/M/title-exact?Rich%20Man's%20Wife,%20The%20(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+984|Shadow Conspiracy (1997)|31-Jan-1997||http://us.imdb.com/M/title-exact?Shadow%20Conspiracy%20(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+985|Blood & Wine (1997)|15-Nov-1996||http://us.imdb.com/Title?Blood+%26+Wine+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+986|Turbulence (1997)|10-Jan-1997||http://us.imdb.com/M/title-exact?Turbulence%20(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+987|Underworld (1997)|09-May-1997||http://us.imdb.com/M/title-exact?Underworld%20(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+988|Beautician and the Beast, The (1997)|07-Feb-1997||http://us.imdb.com/M/title-exact?Beautician%20and%20the%20Beast,%20The%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+989|Cats Don't Dance (1997)|26-Mar-1997||http://us.imdb.com/M/title-exact?Cats%20Don%27t%20Dance%20(1997)|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+990|Anna Karenina (1997)|04-Apr-1997||http://us.imdb.com/M/title-exact?Anna%20Karenina%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+991|Keys to Tulsa (1997)|11-Apr-1997||http://us.imdb.com/Title?Keys+to+Tulsa+(1997)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+992|Head Above Water (1996)|20-Jun-1997||http://us.imdb.com/M/title-exact?Head+Above+Water+(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|1|0|0
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+994|Last Time I Committed Suicide, The (1997)|20-Jun-1997||http://us.imdb.com/M/title-exact?Last+Time+I+Committed+Suicide%2C+The+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+995|Kiss Me, Guido (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Kiss+Me%2C+Guido+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+996|Big Green, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Big%20Green,%20The%20(1995)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+997|Stuart Saves His Family (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Stuart%20Saves%20His%20Family%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+998|Cabin Boy (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Cabin%20Boy%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+1003|That Darn Cat! (1997)|14-Feb-1997||http://us.imdb.com/M/title-exact?That%20Darn%20Cat%20(1997)|0|0|0|0|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0
+1004|Geronimo: An American Legend (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Geronimo:%20An%20American%20Legend%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1
+1005|Double vie de V�ronique, La (Double Life of Veronique, The) (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Podwojne%20zycie%20Weroniki%20(1991)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1006|Until the End of the World (Bis ans Ende der Welt) (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Bis%20ans%20Ende%20der%20Welt%20(1991)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0
+1007|Waiting for Guffman (1996)|31-Jan-1997||http://us.imdb.com/M/title-exact?Waiting%20for%20Guffman%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1008|I Shot Andy Warhol (1996)|01-May-1996||http://us.imdb.com/M/title-exact?I%20Shot%20Andy%20Warhol%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1009|Stealing Beauty (1996)|14-Jun-1996||http://us.imdb.com/M/title-exact?Stealing%20Beauty%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1010|Basquiat (1996)|16-Aug-1996||http://us.imdb.com/M/title-exact?Basquiat%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1011|2 Days in the Valley (1996)|27-Sep-1996||http://us.imdb.com/M/title-exact?2%20Days%20in%20the%20Valley%20(1996)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1012|Private Parts (1997)|07-Mar-1997||http://us.imdb.com/M/title-exact?Private%20Parts%20(1997)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1013|Anaconda (1997)|11-Apr-1997||http://us.imdb.com/M/title-exact?Anaconda%20%281997%29|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1014|Romy and Michele's High School Reunion (1997)|25-Apr-1997||http://us.imdb.com/M/title-exact?Romy%20and%20Michele%27s%20High%20School%20Reunion%20%281997%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1015|Shiloh (1997)|23-May-1997||http://us.imdb.com/M/title-exact?Shiloh%20%281997%29|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1016|Con Air (1997)|06-Jun-1997||http://us.imdb.com/M/title-exact?Con%20Air%20%281997%29|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1017|Trees Lounge (1996)|11-Oct-1996||http://us.imdb.com/M/title-exact?Trees%20Lounge%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1018|Tie Me Up! Tie Me Down! (1990)|01-Jan-1990||http://us.imdb.com/Title?%A1%C1tame%21+(1990)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1019|Die xue shuang xiong (Killer, The) (1989)|01-Jan-1989||http://us.imdb.com/M/title-exact?Die%20xue%20shuang%20xiong%20(1989)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1020|Gaslight (1944)|01-Jan-1944||http://us.imdb.com/M/title-exact?Gaslight%20(1944)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+1021|8 1/2 (1963)|01-Jan-1963||http://us.imdb.com/M/title-exact?8%201/2%20(1963)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1022|Fast, Cheap & Out of Control (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Fast,+Cheap+&+Out+of+Control+(1997)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1023|Fathers' Day (1997)|09-May-1997||http://us.imdb.com/M/title-exact?Fathers%27%20Day%20%281997%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1024|Mrs. Dalloway (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Mrs%2E+Dalloway+(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1025|Fire Down Below (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Fire+Down+Below+(1997)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1026|Lay of the Land, The (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Lay+of+the+Land%2C+The+(1997)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1027|Shooter, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Shooter,%20The%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1028|Grumpier Old Men (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Grumpier%20Old%20Men%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1029|Jury Duty (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Jury%20Duty%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1030|Beverly Hillbillies, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Beverly%20Hillbillies,%20The%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1031|Lassie (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Lassie%20(1994)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1032|Little Big League (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Little%20Big%20League%20(1994)|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1033|Homeward Bound II: Lost in San Francisco (1996)|08-Mar-1996||http://us.imdb.com/M/title-exact?Homeward%20Bound%20II:%20Lost%20in%20San%20Francisco%20(1996)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1034|Quest, The (1996)|26-Apr-1996||http://us.imdb.com/M/title-exact?Quest,%20The%20(1996/I)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1035|Cool Runnings (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Cool%20Runnings%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1036|Drop Dead Fred (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Drop%20Dead%20Fred%20(1991)|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0
+1037|Grease 2 (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Grease%202%20(1982)|0|0|0|0|0|1|0|0|0|0|0|0|1|0|1|0|0|0|0
+1038|Switchback (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Switchback+(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1039|Hamlet (1996)|24-Jan-1997||http://us.imdb.com/M/title-exact?Hamlet%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1040|Two if by Sea (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?Two%20if%20by%20Sea%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1041|Forget Paris (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Forget%20Paris%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1042|Just Cause (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Just%20Cause%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+1043|Rent-a-Kid (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Rent-a-Kid%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1044|Paper, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Paper,%20The%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1045|Fearless (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Fearless%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1046|Malice (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Malice%20(1993)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1047|Multiplicity (1996)|12-Jul-1996||http://us.imdb.com/M/title-exact?Multiplicity%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1048|She's the One (1996)|23-Aug-1996||http://us.imdb.com/M/title-exact?She's%20the%20One%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
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+1050|Ghost and Mrs. Muir, The (1947)|01-Jan-1947||http://us.imdb.com/M/title-exact?Ghost%20and%20Mrs.%20Muir,%20The%20(1947)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+1051|Associate, The (1996)|19-Oct-1996||http://us.imdb.com/M/title-exact?Associate,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1052|Dracula: Dead and Loving It (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Dracula:%20Dead%20and%20Loving%20It%20(1995)|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0
+1053|Now and Then (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Now%20and%20Then%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1054|Mr. Wrong (1996)|16-Feb-1996||http://us.imdb.com/M/title-exact?Mr.%20Wrong%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1055|Simple Twist of Fate, A (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Simple%20Twist%20of%20Fate,%20A%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1056|Cronos (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Cronos%20(1992)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+1057|Pallbearer, The (1996)|19-Apr-1996||http://us.imdb.com/M/title-exact?Pallbearer,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1058|War, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?War,%20The%20(1994)|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1059|Don't Be a Menace to South Central While Drinking Your Juice in the Hood (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?Don't%20Be%20a%20Menace%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1060|Adventures of Pinocchio, The (1996)|26-Jul-1996||http://us.imdb.com/M/title-exact?Adventures%20of%20Pinocchio,%20The%20(1996)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1061|Evening Star, The (1996)|25-Dec-1996||http://us.imdb.com/M/title-exact?Evening%20Star,%20The%20(1996)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1062|Four Days in September (1997)|23-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-119815|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1063|Little Princess, A (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Little%20Princess,%20A%20(1995)|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1064|Crossfire (1947)|01-Jan-1947||http://us.imdb.com/M/title-exact?Crossfire%20(1947)|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0|0|0
+1065|Koyaanisqatsi (1983)|01-Jan-1983||http://us.imdb.com/M/title-exact?Koyaanisqatsi%20(1983)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0
+1066|Balto (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Balto%20(1995)|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1067|Bottle Rocket (1996)|21-Feb-1996||http://us.imdb.com/M/title-exact?Bottle%20Rocket%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1068|Star Maker, The (Uomo delle stelle, L') (1995)|01-Mar-1996||http://us.imdb.com/M/title-exact?Uomo%20delle%20stelle,%20L'%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1069|Amateur (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Amateur%20(1994)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|1|0|0
+1070|Living in Oblivion (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Living%20in%20Oblivion%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+1072|Pyromaniac's Love Story, A (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Pyromaniac's%20Love%20Story,%20A%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
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+1076|Pagemaster, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Pagemaster,%20The%20(1994)|0|1|1|1|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0
+1077|Love and a .45 (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Love%20and%20a%20.45%20(1994)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1078|Oliver & Company (1988)|29-Mar-1988||http://us.imdb.com/M/title-exact?Oliver%20&%20Company%20(1988)|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1079|Joe's Apartment (1996)|26-Jul-1996||http://us.imdb.com/M/title-exact?Joe's%20Apartment%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0
+1080|Celestial Clockwork (1994)|12-Jul-1996||http://us.imdb.com/Title?Cort%E1zar+(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1081|Curdled (1996)|27-Sep-1996||http://us.imdb.com/M/title-exact?Curdled%20(1996)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1082|Female Perversions (1996)|25-Apr-1997||http://us.imdb.com/M/title-exact?Female%20Perversions%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1083|Albino Alligator (1996)|17-Jan-1997||http://us.imdb.com/M/title-exact?Albino%20Alligator%20(1996)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0
+1084|Anne Frank Remembered (1995)|23-Feb-1996||http://us.imdb.com/M/title-exact?Anne%20Frank%20Remembered%20(1995)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1085|Carried Away (1996)|29-Mar-1996||http://us.imdb.com/M/title-exact?Carried%20Away%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+1086|It's My Party (1995)|22-Mar-1996||http://us.imdb.com/M/title-exact?It's%20My%20Party%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1087|Bloodsport 2 (1995)|01-Mar-1996||http://us.imdb.com/M/title-exact?Bloodsport%202%20%281995%29|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1088|Double Team (1997)|04-Apr-1997||http://us.imdb.com/M/title-exact?Double%20Team%20%281997%29|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1089|Speed 2: Cruise Control (1997)|13-Jun-1997||http://us.imdb.com/M/title-exact?Speed%202%3A%20Cruise%20Control%20%281997%29|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0
+1090|Sliver (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Sliver%20(1993)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1091|Pete's Dragon (1977)|01-Jan-1977||http://us.imdb.com/M/title-exact?Pete's%20Dragon%20(1977)|0|0|1|1|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0
+1092|Dear God (1996)|01-Nov-1996||http://us.imdb.com/M/title-exact?Dear%20God%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1093|Live Nude Girls (1995)|01-Mar-1996||http://us.imdb.com/M/title-exact?Live%20Nude%20Girls%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1094|Thin Line Between Love and Hate, A (1996)|03-Apr-1996||http://us.imdb.com/M/title-exact?Thin%20Line%20Between%20Love%20and%20Hate,%20A%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1095|High School High (1996)|25-Oct-1996||http://us.imdb.com/M/title-exact?High%20School%20High%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1096|Commandments (1997)|02-May-1997||http://us.imdb.com/Title?Commandments+(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
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+1128|Heidi Fleiss: Hollywood Madam (1995) |09-Feb-1996||http://us.imdb.com/M/title-exact?Heidi%20Fleiss:%20Hollywood%20Madam%20(1995)%20(TV)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
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+1133|Escape to Witch Mountain (1975)|01-Jan-1975||http://us.imdb.com/M/title-exact?Escape%20to%20Witch%20Mountain%20(1975)|0|0|1|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0
+1134|Get on the Bus (1996)|16-Oct-1996||http://us.imdb.com/M/title-exact?Get%20on%20the%20Bus%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1136|Ghosts of Mississippi (1996)|20-Dec-1996||http://us.imdb.com/M/title-exact?Ghosts%20of%20Mississippi%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1153|Backbeat (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Backbeat%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0
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+1158|Fille seule, La (A Single Girl) (1995)|30-Oct-1996||http://us.imdb.com/M/title-exact?Fille%20seule,%20La%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1166|Love & Human Remains (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Love%20&%20Human%20Remains%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+1173|Bliss (1997)|06-Jun-1997||http://us.imdb.com/M/title-exact?Bliss%20(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+1174|Caught (1996)|27-Sep-1996||http://us.imdb.com/M/title-exact?Caught%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1175|Hugo Pool (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Hugo+Pool+(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
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+1178|Major Payne (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Major%20Payne%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1179|Man of the House (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Man%20of%20the%20House%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1180|I Love Trouble (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?I%20Love%20Trouble%20(1994)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1181|Low Down Dirty Shame, A (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Low%20Down%20Dirty%20Shame,%20A%20(1994)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1182|Cops and Robbersons (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Cops%20and%20Robbersons%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1183|Cowboy Way, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Cowboy%20Way,%20The%20(1994)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1184|Endless Summer 2, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Endless%20Summer%202,%20The%20(1994)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1185|In the Army Now (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?In%20the%20Army%20Now%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0
+1186|Inkwell, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Inkwell,%20The%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1187|Switchblade Sisters (1975)|17-May-1975||http://us.imdb.com/M/title-exact?Switchblade%20Sisters%20(1975)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1188|Young Guns II (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Young%20Guns%20II%20(1990)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|1
+1189|Prefontaine (1997)|24-Jan-1997||http://us.imdb.com/M/title-exact?Prefontaine%20(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1190|That Old Feeling (1997)|04-Apr-1997||http://us.imdb.com/M/title-exact?That%20Old%20Feeling%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1191|Letter From Death Row, A (1998)|01-Feb-1998||http://us.imdb.com/M/title-exact?Letter+From+Death+Row%2C+A+(1998)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+1192|Boys of St. Vincent, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Boys%20of%20St.%20Vincent,%20The%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1193|Before the Rain (Pred dozhdot) (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Pred%20dozhdot%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1194|Once Were Warriors (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Once%20Were%20Warriors%20(1994)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+1195|Strawberry and Chocolate (Fresa y chocolate) (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Fresa%20y%20chocolate%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1196|Savage Nights (Nuits fauves, Les) (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Nuits%20fauves,%20Les%20(1992)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1197|Family Thing, A (1996)|23-Mar-1996||http://us.imdb.com/M/title-exact?Family%20Thing,%20A%20(1996)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1198|Purple Noon (1960)|28-Jun-1960||http://us.imdb.com/M/title-exact?Plein%20soleil%20(1960)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0
+1199|Cemetery Man (Dellamorte Dellamore) (1994)|12-Apr-1996||http://us.imdb.com/M/title-exact?Dellamorte%20Dellamore%20(1994)|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0
+1200|Kim (1950)|01-Jan-1950||http://us.imdb.com/M/title-exact?Kim%20(1950)|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1201|Marlene Dietrich: Shadow and Light (1996) |02-Apr-1996||http://us.imdb.com/M/title-exact?Marlene%20Dietrich:%20Shadow%20and%20Light%20(1996)%20(TV)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1202|Maybe, Maybe Not (Bewegte Mann, Der) (1994)|19-Jul-1996||http://us.imdb.com/M/title-exact?Bewegte%20Mann,%20Der%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1203|Top Hat (1935)|01-Jan-1935||http://us.imdb.com/M/title-exact?Top%20Hat%20(1935)|0|0|0|0|0|1|0|0|0|0|0|0|1|0|1|0|0|0|0
+1204|To Be or Not to Be (1942)|01-Jan-1942||http://us.imdb.com/M/title-exact?To%20Be%20or%20Not%20to%20Be%20(1942)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|1|0
+1205|Secret Agent, The (1996)|08-Nov-1996||http://us.imdb.com/M/title-exact?Secret%20Agent,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1206|Amos & Andrew (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Amos%20&%20Andrew%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1207|Jade (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Jade%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1208|Kiss of Death (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Kiss%20of%20Death%20(1995)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|1|0|0
+1209|Mixed Nuts (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Mixed%20Nuts%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1210|Virtuosity (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Virtuosity%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+1211|Blue Sky (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Blue%20Sky%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+1212|Flesh and Bone (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Flesh%20and%20Bone%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|1|1|0|0|0|0
+1213|Guilty as Sin (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Guilty%20as%20Sin%20(1993)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|1|0|0
+1214|In the Realm of the Senses (Ai no corrida) (1976)|08-Mar-1976||http://us.imdb.com/M/title-exact?Ai%20no%20Corrida%20(1976)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1215|Barb Wire (1996)|03-May-1996||http://us.imdb.com/M/title-exact?Barb%20Wire%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
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+1217|Assassins (1995)|01-Jan-1995||http://us.imdb.com/Title?Assassins+(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1218|Friday (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Friday%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1219|Goofy Movie, A (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Goofy%20Movie,%20A%20(1995)|0|0|0|1|1|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1220|Higher Learning (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Higher%20Learning%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1221|When a Man Loves a Woman (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?When%20a%20Man%20Loves%20a%20Woman%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1222|Judgment Night (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Judgment%20Night%20(1993)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1223|King of the Hill (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?King%20of%20the%20Hill%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1224|Scout, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Scout,%20The%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1225|Angus (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Angus%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1226|Night Falls on Manhattan (1997)|16-May-1997||http://us.imdb.com/M/title-exact?Night%20Falls%20on%20Manhattan%20(1997)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+1227|Awfully Big Adventure, An (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Awfully%20Big%20Adventure,%20An%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1228|Under Siege 2: Dark Territory (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Under%20Siege%202:%20Dark%20Territory%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1229|Poison Ivy II (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Poison%20Ivy%20II%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1230|Ready to Wear (Pret-A-Porter) (1994)|01-Jan-1994||http://us.imdb.com/Title?Pr%EAt-%E0-Porter+(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1231|Marked for Death (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Marked%20for%20Death%20(1990)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1232|Madonna: Truth or Dare (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Madonna:%20Truth%20or%20Dare%20(1991)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1233|N�nette et Boni (1996)|01-Jan-1996||http://us.imdb.com/Title?N%E9nette+et+Boni+(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1235|Big Bang Theory, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?imdb-title-109266|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1236|Other Voices, Other Rooms (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?imdb-title-119845|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1237|Twisted (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?imdb-title-117994|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1238|Full Speed (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?imdb-title-118230|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1239|Cutthroat Island (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Cutthroat%20Island%20(1995)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1240|Ghost in the Shell (Kokaku kidotai) (1995)|12-Apr-1996||http://us.imdb.com/M/title-exact?Kokaku%20Kidotai%20(1995)|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0
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+1242|Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?Vieille%20qui%20marchait%20dans%20la%20mer,%20La%20(1991)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1243|Night Flier (1997)|06-Feb-1998||http://us.imdb.com/M/title-exact?Night+Flier+(1997)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+1244|Metro (1997)|17-Jan-1997||http://us.imdb.com/M/title-exact?Metro%20(1997)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1245|Gridlock'd (1997)|29-Jan-1997||http://us.imdb.com/M/title-exact?Gridlock'd%20(1997)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1246|Bushwhacked (1995)|01-Jan-1995||http://us.imdb.com/Title?Bushwhacked+(1995/I)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1247|Bad Girls (1994)|01-Jan-1994||http://us.imdb.com/Title?Bad+Girls+(1994/I)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+1248|Blink (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Blink%20(1994)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1249|For Love or Money (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?For%20Love%20or%20Money%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1250|Best of the Best 3: No Turning Back (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Best%20of%20the%20Best%203:%20No%20Turning%20Back%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1251|A Chef in Love (1996)|25-Apr-1997||http://us.imdb.com/M/title-exact?Mille%20et%20une%20recettes%20du%20cuisinier%20amoureux%2C%20Les%20%281996%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1252|Contempt (M�pris, Le) (1963)|27-Jun-1997||http://us.imdb.com/M/title-exact?M%E9pris%2C+Le+(1963)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1253|Tie That Binds, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Tie%20That%20Binds,%20The%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1254|Gone Fishin' (1997)|30-May-1997||http://us.imdb.com/M/title-exact?Gone%20Fishin'%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1255|Broken English (1996)|02-May-1997||http://us.imdb.com/M/title-exact?Broken%20English%20%281996%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1256|Designated Mourner, The (1997)|23-May-1997||http://us.imdb.com/M/title-exact?Designated%20Mourner%2C%20The%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1257|Designated Mourner, The (1997)|23-May-1997||http://us.imdb.com/M/title-exact?Designated%20Mourner%2C%20The%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1258|Trial and Error (1997)|30-May-1997||http://us.imdb.com/M/title-exact?Trial%20and%20Error%20%281997%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1259|Pie in the Sky (1995)|09-Feb-1996||http://us.imdb.com/M/title-exact?Pie%20in%20the%20Sky%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1260|Total Eclipse (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Total%20Eclipse%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1262|Walking and Talking (1996)|12-Jul-1996||http://us.imdb.com/M/title-exact?Walking%20and%20Talking%20(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1263|Foxfire (1996)|23-Aug-1996||http://us.imdb.com/M/title-exact?Foxfire%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1264|Nothing to Lose (1994)|16-Aug-1996||http://us.imdb.com/M/title-exact?Nothing%20to%20Lose%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1265|Star Maps (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Star+Maps+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1267|Clockers (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Clockers%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1268|Bitter Moon (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Lunes%20de%20fiel%20(1992)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1269|Love in the Afternoon (1957)|01-Jan-1957||http://us.imdb.com/M/title-exact?Love%20in%20the%20Afternoon%20(1957)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1270|Life with Mikey (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Life%20with%20Mikey%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1271|North (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?North%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1272|Talking About Sex (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Talking%20About%20Sex%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1273|Color of Night (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Color%20of%20Night%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1274|Robocop 3 (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Robocop%203%20(1993)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+1275|Killer (Bulletproof Heart) (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Killer%20(1994)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1276|Sunset Park (1996)|26-Apr-1996||http://us.imdb.com/M/title-exact?Sunset%20Park%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1277|Set It Off (1996)|25-Sep-1996||http://us.imdb.com/M/title-exact?Set%20It%20Off%20(1996)|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1278|Selena (1997)|21-Mar-1997||http://us.imdb.com/M/title-exact?Selena%20(1997)|0|0|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|0
+1279|Wild America (1997)|04-Jul-1997||http://us.imdb.com/M/title-exact?Wild+America+(1997)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1280|Gang Related (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Gang+Related+(1997)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1281|Manny & Lo (1996)|26-Jul-1996||http://us.imdb.com/M/title-exact?Manny%20&%20Lo%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1282|Grass Harp, The (1995)|11-Oct-1996||http://us.imdb.com/M/title-exact?Grass%20Harp,%20The%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1283|Out to Sea (1997)|04-Jul-1997||http://us.imdb.com/M/title-exact?Out+to+Sea+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1284|Before and After (1996)|23-Feb-1996||http://us.imdb.com/M/title-exact?Before%20and%20After%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0|0|0|0
+1285|Princess Caraboo (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Princess%20Caraboo%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1286|Shall We Dance? (1937)|01-Jan-1937||http://us.imdb.com/M/title-exact?Shall%20We%20Dance?%20(1937)|0|0|0|0|0|1|0|0|0|0|0|0|1|0|1|0|0|0|0
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+1288|Denise Calls Up (1995)|29-Mar-1996||http://us.imdb.com/M/title-exact?Denise%20Calls%20Up%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1289|Jack and Sarah (1995)|22-Mar-1996||http://us.imdb.com/M/title-exact?Jack%20and%20Sarah%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
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+1293|Star Kid (1997)|16-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-120478|0|0|1|0|1|0|0|0|0|1|0|0|0|0|0|1|0|0|0
+1294|Ayn Rand: A Sense of Life (1997)|13-Feb-1998||http://us.imdb.com/Title?Ayn+Rand%3A+A+Sense+of+Life+(1997)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
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+1297|Love Affair (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Love%20Affair%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
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+1303|Getaway, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Getaway,%20The%20(1994)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1304|New York Cop (1996)|01-Jan-1996||http://us.imdb.com/Title?New+York+Cop+(1996)|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1305|National Lampoon's Senior Trip (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?National%20Lampoon's%20Senior%20Trip%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1306|Delta of Venus (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Delta%20of%20Venus%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1307|Carmen Miranda: Bananas Is My Business (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Carmen%20Miranda:%20Bananas%20Is%20My%20Business%20(1994)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1308|Babyfever (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Babyfever%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1309|Very Natural Thing, A (1974)|01-Jan-1974||http://us.imdb.com/M/title-exact?Very%20Natural%20Thing,%20A%20(1974)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1311|Waiting to Exhale (1995)|15-Jan-1996||http://us.imdb.com/M/title-exact?Waiting%20to%20Exhale%20(1995)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1312|Pompatus of Love, The (1996)|26-Jul-1996||http://us.imdb.com/M/title-exact?Pompatus%20of%20Love,%20The%20(1996)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1314|Surviving the Game (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Surviving%20the%20Game%20(1994)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
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+1316|Horse Whisperer, The (1998)|25-Dec-1997||http://us.imdb.com/M/title-exact?imdb-title-119314|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1317|Journey of August King, The (1995)|22-Mar-1996||http://us.imdb.com/M/title-exact?Journey%20of%20August%20King,%20The%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1319|Neon Bible, The (1995)|01-Mar-1996||http://us.imdb.com/M/title-exact?Neon%20Bible,%20The%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1320|Homage (1995)|03-May-1996||http://us.imdb.com/M/title-exact?Homage%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1321|Open Season (1996)|10-May-1996||http://us.imdb.com/Title?Open+Season+(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+1323|Wooden Man's Bride, The (Wu Kui) (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Wu%20Kui%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1327|Captives (1994)|16-Sep-1994||http://us.imdb.com/Title?Captives+(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1328|Of Love and Shadows (1994)|10-May-1996||http://us.imdb.com/M/title-exact?Of%20Love%20and%20Shadows%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1330|An Unforgettable Summer (1994)|01-Jan-1994||http://us.imdb.com/Title?Un+%E9t%E9+inoubliable+(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1331|Last Klezmer: Leopold Kozlowski, His Life and Music, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Last%20Klezmer%3A%20Leopold%20Kozlowski%2C%20His%20Life%20and%20Music%2C%20The%20%281995%29|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1332|My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?En%20compagnie%20d'Antonin%20Artaud%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1333|Midnight Dancers (Sibak) (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Sibak%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1334|Somebody to Love (1994)|14-Jun-1996||http://us.imdb.com/Title?Somebody+to+Love+(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1335|American Buffalo (1996)|13-Sep-1996||http://us.imdb.com/M/title-exact?American%20Buffalo%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1336|Kazaam (1996)|17-Jul-1996||http://us.imdb.com/M/title-exact?Kazaam%20(1996)|0|0|0|0|1|1|0|0|0|1|0|0|0|0|0|0|0|0|0
+1337|Larger Than Life (1996)|01-Nov-1996||http://us.imdb.com/M/title-exact?Larger%20Than%20Life%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1338|Two Deaths (1995)|09-Aug-1996||http://us.imdb.com/Title?Two+Deaths+(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1339|Stefano Quantestorie (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Stefano%20Quantestorie%20%281993%29|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1340|Crude Oasis, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Crude%20Oasis,%20The%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
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+1342|Convent, The (Convento, O) (1995)|14-Jun-1996||http://us.imdb.com/M/title-exact?Convento,%20O%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1344|Story of Xinghua, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Story%20of%20Xinghua,%20The%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1345|Day the Sun Turned Cold, The (Tianguo niezi) (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Tianguo%20Niezi%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1349|Mille bolle blu (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Mille%20bolle%20blu%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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+1353|1-900 (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?06%20(1994)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1354|Venice/Venice (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Venice/Venice%20(1992)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1355|Infinity (1996)|04-Oct-1996||http://us.imdb.com/M/title-exact?Infinity%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1356|Ed's Next Move (1996)|04-Oct-1996||http://us.imdb.com/M/title-exact?Ed%27s%20Next%20Move%20%281996%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1357|For the Moment (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?For%20the%20Moment%20(1994)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0
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+1361|Search for One-eye Jimmy, The (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?Search%20for%20One-eye%20Jimmy,%20The%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1362|American Strays (1996)|13-Sep-1996||http://us.imdb.com/M/title-exact?American%20Strays%20(1996)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1363|Leopard Son, The (1996)|27-Sep-1996||http://us.imdb.com/M/title-exact?Leopard%20Son,%20The%20(1996)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
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+1365|Johnny 100 Pesos (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Johnny%20100%20Pesos%20(1993)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1366|JLG/JLG - autoportrait de d�cembre (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?JLG/JLG%20-%20autoportrait%20de%20d%E9cembre%20%281994%29|0|0|0|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0
+1367|Faust (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Faust%20%281994%29|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1368|Mina Tannenbaum (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Mina%20Tannenbaum%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1369|Forbidden Christ, The (Cristo proibito, Il) (1950)|01-Jan-1950||http://us.imdb.com/M/title-exact?Cristo%20proibito%2C%20Il%20%281950%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1370|I Can't Sleep (J'ai pas sommeil) (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?J'ai%20pas%20sommeil%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1371|Machine, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Machine,%20La%20(1994)|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0|0|0|0
+1372|Stranger, The (1994)|01-Jan-1994||http://us.imdb.com/Title?Stranger,+The+(1994/II)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1373|Good Morning (1971)|4-Feb-1971||http://us.imdb.com/M/title-exact?Good%20Morning%20(1971)|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1374|Falling in Love Again (1980)|01-Jan-1980||http://us.imdb.com/M/title-exact?Falling%20in%20Love%20Again%20(1980)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1375|Cement Garden, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Cement%20Garden,%20The%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1376|Meet Wally Sparks (1997)|31-Jan-1997||http://us.imdb.com/M/title-exact?Meet%20Wally%20Sparks%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1377|Hotel de Love (1996)|07-Feb-1997||http://us.imdb.com/M/title-exact?Hotel%20de%20Love%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1378|Rhyme & Reason (1997)|05-Mar-1997||http://us.imdb.com/M/title-exact?Rhyme%20%26%20Reason%20(1997)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1379|Love and Other Catastrophes (1996)|28-Mar-1997||http://us.imdb.com/M/title-exact?Love%20and%20Other%20Catastrophes%20%281996%29|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
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+1474|Nina Takes a Lover (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Nina%20Takes%20a%20Lover%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
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+1476|Raw Deal (1948)|01-Jan-1948||http://us.imdb.com/M/title-exact?Raw%20Deal%20(1948)|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0
+1477|Nightwatch (1997)|22-Apr-1997||http://us.imdb.com/M/title-exact?Nightwatch%20(1997)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0
+1478|Dead Presidents (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Dead%20Presidents%20(1995)|0|1|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+1479|Reckless (1995)|01-Jan-1995||http://us.imdb.com/Title?Reckless+(1995/I)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1480|Herbie Rides Again (1974)|01-Jan-1974||http://us.imdb.com/M/title-exact?Herbie%20Rides%20Again%20(1974)|0|0|1|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1481|S.F.W. (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?S.F.W.%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1482|Gate of Heavenly Peace, The (1995)|10-May-1996||http://us.imdb.com/M/title-exact?Gate%20of%20Heavenly%20Peace,%20The%20(1995)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1483|Man in the Iron Mask, The (1998)|17-Mar-1998||http://us.imdb.com/Title?Man+in+the+Iron+Mask,+The+(1998/I)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+1484|Jerky Boys, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Jerky%20Boys,%20The%20(1994)|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1485|Colonel Chabert, Le (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Colonel%20Chabert,%20Le%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|1|0
+1486|Girl in the Cadillac (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Girl%20in%20the%20Cadillac%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1487|Even Cowgirls Get the Blues (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Even%20Cowgirls%20Get%20the%20Blues%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1488|Germinal (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Germinal%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1489|Chasers (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Chasers%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1490|Fausto (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Fausto%20%281993%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1491|Tough and Deadly (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Tough%20and%20Deadly%20(1995)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1492|Window to Paris (1994)|01-Jan-1994||http://us.imdb.com/Title?Okno+v+Parizh+(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1493|Modern Affair, A (1995)|06-Sep-1996||http://us.imdb.com/M/title-exact?Modern%20Affair,%20A%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1494|Mostro, Il (1994)|19-Apr-1996||http://us.imdb.com/M/title-exact?Mostro,%20Il%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1495|Flirt (1995)|07-Aug-1996||http://us.imdb.com/Title?Flirt+(1995/I)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1496|Carpool (1996)|23-Aug-1996||http://us.imdb.com/M/title-exact?Carpool%20(1996)|0|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0
+1497|Line King: Al Hirschfeld, The (1996)|11-Oct-1996||http://us.imdb.com/M/title-exact?Line%20King,%20The%20(1996)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1498|Farmer & Chase (1995)|10-Jan-1997||http://us.imdb.com/M/title-exact?Farmer%20&%20Chase%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1499|Grosse Fatigue (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Grosse%20fatigue%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1500|Santa with Muscles (1996)|08-Nov-1996||http://us.imdb.com/M/title-exact?Santa%20with%20Muscles%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1501|Prisoner of the Mountains (Kavkazsky Plennik) (1996)|31-Jan-1997||http://us.imdb.com/M/title-exact?Kavkazsky%20Plennik%20(1996)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0
+1502|Naked in New York (1994)|01-Jan-1994||http://us.imdb.com/Title?Naked+in+New+York+(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1503|Gold Diggers: The Secret of Bear Mountain (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Gold%20Diggers:%20The%20Secret%20of%20Bear%20Mountain%20(1995)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1504|Bewegte Mann, Der (1994)|12-Jul-1996||http://us.imdb.com/M/title-exact?Bewegte%20Mann%2C%20Der%20%281994%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1505|Killer: A Journal of Murder (1995)|06-Sep-1996||http://us.imdb.com/M/title-exact?Killer:%20A%20Journal%20of%20Murder%20(1995)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+1506|Nelly & Monsieur Arnaud (1995)|12-Apr-1996||http://us.imdb.com/M/title-exact?Nelly%20%26%20Monsieur%20Arnaud%20%281995%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1507|Three Lives and Only One Death (1996)|11-Oct-1996||http://us.imdb.com/M/title-exact?Trois%20vies%20et%20une%20seule%20mort%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1508|Babysitter, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Babysitter,%20The%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1509|Getting Even with Dad (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Getting%20Even%20with%20Dad%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1510|Mad Dog Time (1996)|08-Nov-1996||http://us.imdb.com/M/title-exact?Mad%20Dog%20Time%20(1996)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1511|Children of the Revolution (1996)|01-May-1997||http://us.imdb.com/M/title-exact?Children%20of%20the%20Revolution%20%281996%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1512|World of Apu, The (Apur Sansar) (1959)|05-Apr-1996||http://us.imdb.com/M/title-exact?Apur%20Sansar%20(1959)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1513|Sprung (1997)|14-May-1997||http://us.imdb.com/M/title-exact?Sprung%20%281997%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1514|Dream With the Fishes (1997)|20-Jun-1997||http://us.imdb.com/M/title-exact?Dream+With+the+Fishes+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1515|Wings of Courage (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Wings%20of%20Courage%20(1995)|0|0|1|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1516|Wedding Gift, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Wedding%20Gift,%20The%20(1994)%20(TV)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1517|Race the Sun (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?Race%20the%20Sun%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1518|Losing Isaiah (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Losing%20Isaiah%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1519|New Jersey Drive (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?New%20Jersey%20Drive%20(1995)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+1520|Fear, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Fear,%20The%20(1995)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+1521|Mr. Wonderful (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Mr.%20Wonderful%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1522|Trial by Jury (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Trial%20by%20Jury%20(1994)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1523|Good Man in Africa, A (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Good%20Man%20in%20Africa,%20A%20(1994)|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1524|Kaspar Hauser (1993)|07-Jun-1996||http://us.imdb.com/Title?Kaspar+Hauser+(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1525|Object of My Affection, The (1998)|20-Mar-1998||http://us.imdb.com/Title?Object+of+My+Affection,+The+(1998)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1526|Witness (1985)|01-Jan-1985||http://us.imdb.com/M/title-exact?Witness+(1985)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|1|0|0
+1527|Senseless (1998)|09-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-120820|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1528|Nowhere (1997)|09-May-1997||http://us.imdb.com/M/title-exact?Nowhere%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1529|Underground (1995)|29-Mar-1996||http://us.imdb.com/M/title-exact?Underground%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0
+1530|Jefferson in Paris (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Jefferson%20in%20Paris%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1531|Far From Home: The Adventures of Yellow Dog (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Far%20From%20Home:%20The%20Adventures%20of%20Yellow%20Dog%20(1995)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1532|Foreign Student (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Foreign%20Student%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1533|I Don't Want to Talk About It (De eso no se habla) (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?De%20Eso%20No%20Se%20Habla%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1534|Twin Town (1997)|30-May-1997||http://us.imdb.com/M/title-exact?Twin%20Town%20%281997%29|0|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0|0|0
+1535|Enfer, L' (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Enfer,%20L'%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1536|Aiqing wansui (1994)|22-Jul-1996||http://us.imdb.com/M/title-exact?Aiqing%20Wansui%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1537|Cosi (1996)|11-Apr-1997||http://us.imdb.com/M/title-exact?Cosi%20(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1538|All Over Me (1997)|25-Apr-1997||http://us.imdb.com/M/title-exact?All%20Over%20Me%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1539|Being Human (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Being%20Human%20(1993)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1540|Amazing Panda Adventure, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Amazing%20Panda%20Adventure,%20The%20(1995)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1541|Beans of Egypt, Maine, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Beans%20of%20Egypt,%20Maine,%20The%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1542|Scarlet Letter, The (1926)|01-Jan-1926||http://us.imdb.com/M/title-exact?Scarlet%20Letter,%20The%20(1926)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1543|Johns (1996)|18-Oct-1996||http://us.imdb.com/M/title-exact?Johns%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1544|It Takes Two (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?It%20Takes%20Two%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1545|Frankie Starlight (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Frankie%20Starlight%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+1546|Shadows (Cienie) (1988)|01-Jan-1988||http://us.imdb.com/M/title-exact?Cienie%20(1988)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1547|Show, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Show,%20The%20(1995)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1548|The Courtyard (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Courtyard,%20The%20(1995)%20(TV)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1549|Dream Man (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Dream%20Man%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1550|Destiny Turns on the Radio (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Destiny%20Turns%20on%20the%20Radio%20(1995)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1551|Glass Shield, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Glass%20Shield,%20The%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1552|Hunted, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Hunted,%20The%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1553|Underneath, The (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Underneath,%20The%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|1|0|0
+1554|Safe Passage (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Safe%20Passage%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1555|Secret Adventures of Tom Thumb, The (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Secret%20Adventures%20of%20Tom%20Thumb,%20The%20(1993)|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0
+1556|Condition Red (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Condition%20Red%20(1995)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1557|Yankee Zulu (1994)|16-Feb-1996||http://us.imdb.com/M/title-exact?Yankee%20Zulu%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1558|Aparajito (1956)|29-Mar-1996||http://us.imdb.com/M/title-exact?Aparajito%20(1956)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1559|Hostile Intentions (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Hostile%20Intentions%20(1994)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1560|Clean Slate (Coup de Torchon) (1981)|01-Jan-1981||http://us.imdb.com/M/title-exact?Coup%20de%20torchon%20(1981)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1561|Tigrero: A Film That Was Never Made (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Tigrero:%20A%20Film%20That%20Was%20Never%20Made%20(1994)|0|0|0|0|0|0|0|1|1|0|0|0|0|0|0|0|0|0|0
+1562|Eye of Vichy, The (Oeil de Vichy, L') (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Oeil%20de%20Vichy,%20L'%20(1993)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1563|Promise, The (Versprechen, Das) (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Versprechen,%20Das%20(1994)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1564|To Cross the Rubicon (1991)|01-Jan-1991||http://us.imdb.com/M/title-exact?To%20Cross%20the%20Rubicon%20(1991)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1565|Daens (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Daens%20(1992)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1566|Man from Down Under, The (1943)|01-Jan-1943||http://us.imdb.com/Title?Man+from+Down+Under,+The+(1943)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1567|Careful (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Careful%20(1992)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1568|Vermont Is For Lovers (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Vermont%20Is%20For%20Lovers%20(1992)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1569|Vie est belle, La (Life is Rosey) (1987)|01-Jan-1987||http://us.imdb.com/M/title-exact?Vie%20est%20belle,%20La%20(1987)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1570|Quartier Mozart (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Quartier%20Mozart%20(1992)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1571|Touki Bouki (Journey of the Hyena) (1973)|01-Jan-1973||http://us.imdb.com/M/title-exact?Touki%20Bouki%20(1973)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1572|Wend Kuuni (God's Gift) (1982)|01-Jan-1982||http://us.imdb.com/M/title-exact?Wend%20Kuuni%20(1982)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1573|Spirits of the Dead (Tre passi nel delirio) (1968)|01-Jan-1968||http://us.imdb.com/M/title-exact?Tre%20passi%20nel%20delirio%20(1968)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0
+1574|Pharaoh's Army (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Pharaoh's%20Army%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0
+1575|I, Worst of All (Yo, la peor de todas) (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?Yo,%20la%20Peor%20de%20Todas%20(1990)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1576|Hungarian Fairy Tale, A (1987)|01-Jan-1987||http://us.imdb.com/M/title-exact?Hol%20volt,%20hol%20nem%20volt%20(1987)|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0
+1577|Death in the Garden (Mort en ce jardin, La) (1956)|01-Jan-1956||http://us.imdb.com/Title?Mort+en+ce+jardin,+La+(1956)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1578|Collectionneuse, La (1967)|01-Jan-1967||http://us.imdb.com/M/title-exact?Collectionneuse,%20La%20(1967)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1579|Baton Rouge (1988)|01-Jan-1988||http://us.imdb.com/Title?B%E2ton+rouge+(1988)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1580|Liebelei (1933)|01-Jan-1933||http://us.imdb.com/M/title-exact?Liebelei%20(1933)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1581|Woman in Question, The (1950)|01-Jan-1950||http://us.imdb.com/M/title-exact?Woman%20in%20Question,%20The%20(1950)|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0
+1582|T-Men (1947)|01-Jan-1947||http://us.imdb.com/M/title-exact?T-Men%20(1947)|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0
+1583|Invitation, The (Zaproszenie) (1986)|01-Jan-1986||http://us.imdb.com/M/title-exact?Zaproszenie%20(1986)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1584|Symphonie pastorale, La (1946)|01-Jan-1946||http://us.imdb.com/M/title-exact?Symphonie%20pastorale,%20La%20(1946)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1585|American Dream (1990)|01-Jan-1990||http://us.imdb.com/M/title-exact?American%20Dream%20(1990)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1586|Lashou shentan (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Lashou%20Shentan%20(1992)|0|1|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+1587|Terror in a Texas Town (1958)|01-Jan-1958||http://us.imdb.com/M/title-exact?Terror%20in%20a%20Texas%20Town%20(1958)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1
+1588|Salut cousin! (1996)|21-Feb-1997||http://us.imdb.com/M/title-exact?Salut%20cousin!%20(1996)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1589|Schizopolis (1996)|23-May-1997||http://us.imdb.com/Title?Schizopolis+(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1590|To Have, or Not (1995)|06-Jun-1997||http://us.imdb.com/M/title-exact?En%20avoir%20%28ou%20pas%29%20%281995%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1591|Duoluo tianshi (1995)|21-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-112913|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1592|Magic Hour, The (1998)|30-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-119594|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1593|Death in Brunswick (1991)|16-Aug-1996||http://us.imdb.com/M/title-exact?Death%20in%20Brunswick%20(1991)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1594|Everest (1998)|10-Mar-1998||http://us.imdb.com/Title?Everest+(1998)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1595|Shopping (1994)|09-Feb-1996||http://us.imdb.com/M/title-exact?Shopping%20(1994)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1596|Nemesis 2: Nebula (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Nemesis%202:%20Nebula%20(1995)|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|1|1|0|0
+1597|Romper Stomper (1992)|01-Jan-1992||http://us.imdb.com/M/title-exact?Romper%20Stomper%20(1992)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1598|City of Industry (1997)|14-Mar-1997||http://us.imdb.com/M/title-exact?City%20of%20Industry%20(1997)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|1|0|0
+1599|Someone Else's America (1995)|10-May-1996||http://us.imdb.com/M/title-exact?Someone%20Else's%20America%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1600|Guantanamera (1994)|16-May-1997||http://us.imdb.com/M/title-exact?Guantanamera%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1601|Office Killer (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?imdb-title-119819|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1602|Price Above Rubies, A (1998)|20-Mar-1998||http://us.imdb.com/Title?Price+Above+Rubies,+A+(1998)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1603|Angela (1995)|16-Feb-1996||http://us.imdb.com/M/title-exact?Angela%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1604|He Walked by Night (1948)|01-Jan-1948||http://us.imdb.com/M/title-exact?He%20Walked%20by%20Night%20(1948)|0|0|0|0|0|0|1|0|0|0|1|0|0|0|0|0|1|0|0
+1605|Love Serenade (1996)|11-Jul-1997||http://us.imdb.com/M/title-exact?Love+Serenade+(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1606|Deceiver (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Liar+(1997)|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1607|Hurricane Streets (1998)|01-Jan-1998||http://us.imdb.com/Title?Hurricane+Streets+(1998)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1608|Buddy (1997)|06-Jun-1997||http://us.imdb.com/M/title-exact?Buddy%20%281997%29|0|0|1|0|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1609|B*A*P*S (1997)|28-Mar-1997||http://us.imdb.com/M/title-exact?B%2EA%2EP%2ES%2E%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1610|Truth or Consequences, N.M. (1997)|02-May-1997||http://us.imdb.com/Title?Truth+or+Consequences,+N.M.+(1997)|0|1|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0|0|0
+1611|Intimate Relations (1996)|09-May-1997||http://us.imdb.com/M/title-exact?Intimate%20Relations%20%281996%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1612|Leading Man, The (1996)|16-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-116845|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1613|Tokyo Fist (1995)|11-Feb-1998||http://us.imdb.com/M/title-exact?Tokyo+Fist+(1995)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1614|Reluctant Debutante, The (1958)|01-Jan-1958||http://us.imdb.com/M/title-exact?Reluctant%20Debutante,%20The%20(1958)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1615|Warriors of Virtue (1997)|02-May-1997||http://us.imdb.com/M/title-exact?Warriors%20of%20Virtue%20%281997%29|0|1|1|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0
+1616|Desert Winds (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Desert%20Winds%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1617|Hugo Pool (1997)|01-Jan-1997||http://us.imdb.com/M/title-exact?Hugo+Pool+(1997)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1618|King of New York (1990)|01-Jan-1990||http://us.imdb.com/Title?King+of+New+York+(1990)|0|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0
+1619|All Things Fair (1996)|08-Mar-1996||http://us.imdb.com/Title?Lust+och+f%E4gring+stor+(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1620|Sixth Man, The (1997)|28-Mar-1997||http://us.imdb.com/M/title-exact?Sixth%20Man%2C%20The%20(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1621|Butterfly Kiss (1995)|26-Apr-1996||http://us.imdb.com/M/title-exact?Butterfly%20Kiss%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1622|Paris, France (1993)|01-Jan-1993||http://us.imdb.com/M/title-exact?Paris,%20France%20(1993)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1623|C�r�monie, La (1995)|20-Dec-1996||http://us.imdb.com/M/title-exact?C%E9r%E9monie%2C%20La%20%281995%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1624|Hush (1998)|10-Mar-1998||http://us.imdb.com/Title?Hush+(1998)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0
+1625|Nightwatch (1997)|22-Apr-1997||http://us.imdb.com/M/title-exact?Nightwatch%20(1997)|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|1|0|0
+1626|Nobody Loves Me (Keiner liebt mich) (1994)|09-Feb-1996||http://us.imdb.com/M/title-exact?Keiner%20liebt%20mich%20(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1627|Wife, The (1995)|26-Jul-1996||http://us.imdb.com/Title?Wife,+The+(1995)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1628|Lamerica (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Lamerica%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1629|Nico Icon (1995)|01-Jan-1995||http://us.imdb.com/M/title-exact?Nico%20Icon%20(1995)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1630|Silence of the Palace, The (Saimt el Qusur) (1994)|02-Feb-1996||http://us.imdb.com/M/title-exact?Saimt%20el%20Qusur%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1631|Slingshot, The (1993)|01-Jan-1993||http://us.imdb.com/Title?K%E5disbellan+(1993)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1632|Land and Freedom (Tierra y libertad) (1995)|29-Mar-1996||http://us.imdb.com/M/title-exact?Tierra%20y%20libertad%20(1995)|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0
+1633|� k�ldum klaka (Cold Fever) (1994)|08-Mar-1996||http://us.imdb.com/Title?%C1+k%F6ldum+klaka+(1994)|0|0|0|0|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0
+1634|Etz Hadomim Tafus (Under the Domin Tree) (1994)|19-Apr-1996||http://us.imdb.com/M/title-exact?Etz%20Hadomim%20Tafus%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1635|Two Friends (1986) |26-Apr-1986||http://us.imdb.com/M/title-exact?Two%20Friends%20(1986)%20(TV)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1636|Brothers in Trouble (1995)|26-Apr-1996||http://us.imdb.com/M/title-exact?Brothers%20in%20Trouble%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1637|Girls Town (1996)|23-Aug-1996||http://us.imdb.com/M/title-exact?Girls%20Town%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1638|Normal Life (1996)|25-Oct-1996||http://us.imdb.com/M/title-exact?Normal%20Life%20(1996)|0|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0
+1639|Bitter Sugar (Azucar Amargo) (1996)|22-Nov-1996||http://us.imdb.com/M/title-exact?Bitter%20Sugar%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1640|Eighth Day, The (1996)|01-Nov-1996||http://us.imdb.com/Title?Huiti%E8me+jour,+Le+(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1641|Dadetown (1995)|18-Sep-1996||http://us.imdb.com/M/title-exact?Dadetown%20(1995)|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0
+1642|Some Mother's Son (1996)|27-Dec-1996||http://us.imdb.com/M/title-exact?Some%20Mother's%20Son%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1643|Angel Baby (1995)|10-Jan-1997||http://us.imdb.com/Title?Angel+Baby+(1995/I)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1644|Sudden Manhattan (1996)|13-Jun-1997||http://us.imdb.com/M/title-exact?Sudden%20Manhattan%20%281996%29|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1645|Butcher Boy, The (1998)|01-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-118804|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1646|Men With Guns (1997)|06-Mar-1998||http://us.imdb.com/Title?Men+with+Guns+(1997/I)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1647|Hana-bi (1997)|20-Mar-1998||http://us.imdb.com/Title?Hana-bi+(1997)|0|0|0|0|0|1|1|0|1|0|0|0|0|0|0|0|0|0|0
+1648|Niagara, Niagara (1997)|20-Mar-1998||http://us.imdb.com/Title?Niagara,+Niagara+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1649|Big One, The (1997)|27-Mar-1998||http://us.imdb.com/Title?Big+One,+The+(1997)|0|0|0|0|0|1|0|1|0|0|0|0|0|0|0|0|0|0|0
+1650|Butcher Boy, The (1998)|01-Jan-1998||http://us.imdb.com/M/title-exact?imdb-title-118804|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1651|Spanish Prisoner, The (1997)|27-Mar-1998||http://us.imdb.com/Title?Spanish+Prisoner,+The+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|1|0|0
+1652|Temptress Moon (Feng Yue) (1996)|13-Jun-1997||http://us.imdb.com/M/title-exact?Feng%20Yue%20%281996%29|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0
+1653|Entertaining Angels: The Dorothy Day Story (1996)|27-Sep-1996||http://us.imdb.com/M/title-exact?Entertaining%20Angels:%20The%20Dorothy%20Day%20Story%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1654|Chairman of the Board (1998)|01-Jan-1998||http://us.imdb.com/Title?Chairman+of+the+Board+(1998)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1655|Favor, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?Favor,%20The%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1656|Little City (1998)|20-Feb-1998||http://us.imdb.com/M/title-exact?Little+City+(1998)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0
+1657|Target (1995)|28-Feb-1996||http://us.imdb.com/M/title-exact?Target%20(1995)|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1658|Substance of Fire, The (1996)|06-Dec-1996||http://us.imdb.com/M/title-exact?Substance%20of%20Fire,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1659|Getting Away With Murder (1996)|12-Apr-1996||http://us.imdb.com/Title?Getting+Away+With+Murder+(1996)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1660|Small Faces (1995)|09-Aug-1996||http://us.imdb.com/M/title-exact?Small%20Faces%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1661|New Age, The (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?New%20Age,%20The%20(1994)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1662|Rough Magic (1995)|30-May-1997||http://us.imdb.com/M/title-exact?Rough%20Magic%20%281995%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|1|0|0|0|0
+1663|Nothing Personal (1995)|30-Apr-1997||http://us.imdb.com/M/title-exact?Nothing%20Personal%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|1|0
+1664|8 Heads in a Duffel Bag (1997)|18-Apr-1997||http://us.imdb.com/Title?8+Heads+in+a+Duffel+Bag+(1997)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
+1665|Brother's Kiss, A (1997)|25-Apr-1997||http://us.imdb.com/M/title-exact?Brother%27s%20Kiss%2C%20A%20%281997%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1667|Next Step, The (1995)|13-Jun-1997||http://us.imdb.com/M/title-exact?Next%20Step%2C%20The%20%281995%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1675|Sunchaser, The (1996)|25-Oct-1996||http://us.imdb.com/M/title-exact?Sunchaser,%20The%20(1996)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1676|War at Home, The (1996)|01-Jan-1996||http://us.imdb.com/M/title-exact?War%20at%20Home%2C%20The%20%281996%29|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1677|Sweet Nothing (1995)|20-Sep-1996||http://us.imdb.com/M/title-exact?Sweet%20Nothing%20(1995)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
+1678|Mat' i syn (1997)|06-Feb-1998||http://us.imdb.com/M/title-exact?Mat%27+i+syn+(1997)|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0
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+1681|You So Crazy (1994)|01-Jan-1994||http://us.imdb.com/M/title-exact?You%20So%20Crazy%20(1994)|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0
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new file mode 100644
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+406::Beyond Space Remastered Edition
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+567::Hotline Miami 2: Wrong Number Digital Comic
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+572::DRAGON BALL XENOVERSE
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+980::Bitardia Cards: Memes of 2ch
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+985::Grandpa's Table
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+993::Summer Sale
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+1014::ChuSingura46+1 S
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+1047::Intrude
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+1052::The Cat! Porfirio's Adventure
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+1054::Aircraft War X
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+1058::Music Maker 2017 Premium Steam Edition
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+1202::Trick and Treat - Visual Novel
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+1213::Imperium Galactica II
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+1219::PERFECT ANGLE: The puzzle game based on optical illusions
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+1228::Off-Road Paradise: Trial 4x4
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+1230::BOOR
+1231::Battle Islands: Commanders
+1232::Fairy Fencer F Advent Dark Force | フェアリーフェンサー エフ ADVENT DARK FORCE | 妖精劍士 F ADVENT DARK FORCE
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+1256::Zoop! - Hunter's Grimm
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+1258::ULTIMATE MARVEL VS. CAPCOM 3
+1259::SimAirport
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+1272::Toukiden 2
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+1280::Free! - Iwatobi Swim Club
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+1283::Idle Evolution
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+1288::Slime-san
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+1295::Cosmic Star Heroine
+1296::Skyforge
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+1298::Full Throttle Remastered
+1299::Odyssey - The Next Generation Science Game
+1300::Pixel Sand
+1301::Brawlout
+1302::Academagia: The Making of Mages
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+1308::Dawn of Andromeda
+1309::Welcome to Moreytown
+1310::Fur Fun
+1311::Chronicle of Innsmouth
+1312::Birthdays the Beginning / バースデイズ・ザ・ビギニング
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+3458::MDK 2
+3459::Nation Red
+3460::Red Faction
+3461::Red Faction II
+3462::Batman: Arkham Asylum Game of the Year Edition
+3463::Cooking Dash®
+3464::Chocolatier®: Decadence by Design™
+3465::East India Company
+3466::Sniper Elite
+3467::Bad Rats: the Rats' Revenge
+3468::The Dig®
+3469::Supreme Ruler 2020 Gold
+3470::Delta Force: Task Force Dagger
+3471::Tachyon: The Fringe
+3472::Delta Force
+3473::Delta Force — Black Hawk Down: Team Sabre
+3474::Arma 2
+3475::Trine Enchanted Edition
+3476::Overlord II
+3477::Lunnye Devitsy
+3478::F-16 Multirole Fighter
+3479::Crayon Physics Deluxe
+3480::Alien Shooter: Revisited
+3481::Zombie Shooter
+3482::Restaurant Empire II
+3483::Mightier
+3484::Dark Sector
+3485::The Path
+3486::COIL
+3487::Caster
+3488::Eternal Silence
+3489::Eschalon: Book I
+3490::Spectromancer
+3491::Hospital Tycoon
+3492::Tom Clancy's Splinter Cell Double Agent®
+3493::Driver® Parallel Lines
+3494::Warhammer 40,000: Dawn of War II
+3495::Puzzle Quest: Galactrix
+3496::SlamIt Pinball Big Score
+3497::Tom Clancy's EndWar™
+3498::Grand Ages: Rome
+3499::Monster Trucks Nitro
+3500::Europa Universalis III Complete
+3501::Exodus from the Earth
+3502::Starscape
+3503::Precipice of Darkness, Episode Two
+3504::7 Wonders: Treasures of Seven
+3505::Left 4 Dead
+3506::Prince of Persia: Warrior Within™
+3507::Prince of Persia®: The Sands of Time
+3508::Prince of Persia®
+3509::Need for Speed Undercover
+3510::Mass Effect
+3511::SPORE™
+3512::Imperium Romanum Gold Edition
+3513::INSURGENCY: Modern Infantry Combat
+3514::Age of Chivalry
+3515::D.I.P.R.I.P. Warm Up
+3516::Zombie Panic! Source
+3517::Insecticide Part 1
+3518::Sacred Gold
+3519::Tom Clancy's Ghost Recon® Desert Siege™
+3520::Tom Clancy's Ghost Recon®
+3521::Silverfall: Earth Awakening
+3522::Assassin's Creed™: Director's Cut Edition
+3523::Trackmania United Forever Star Edition
+3524::Virtual Villagers: A New Home
+3525::Cold Fear™
+3526::Tom Clancy's Rainbow Six Lockdown™
+3527::Precipice of Darkness, Episode One
+3528::Warhammer® 40,000: Dawn of War® - Soulstorm
+3529::Safecracker: The Ultimate Puzzle Adventure
+3530::Full Spectrum Warrior: Ten Hammers
+3531::ThreadSpace: Hyperbol
+3532::Full Spectrum Warrior
+3533::DOOM 3
+3534::Quake III Arena
+3535::QUAKE II Mission Pack: Ground Zero
+3536::HeXen: Deathkings of the Dark Citadel
+3537::QUAKE III: Team Arena
+3538::Painkiller Overdose
+3539::Call of Juarez™
+3540::Call of Duty® 4: Modern Warfare®
+3541::Amazing Adventures The Lost Tomb™
+3542::Heretic: Shadow of the Serpent Riders
+3543::Sam &amp; Max 105: Reality 2.0
+3544::Geometry Wars: Retro Evolved
+3545::Just Cause
+3546::Hitman: Codename 47
+3547::Commandos 2: Men of Courage
+3548::Thief: Deadly Shadows
+3549::Deus Ex: Game of the Year Edition
+3550::Jade Empire™: Special Edition
+3551::Peggle Deluxe
+3552::Silverfall
+3553::Runaway, The Dream of The Turtle
+3554::GUN™
+3555::Call of Duty: United Offensive
+3556::Sid Meier's Civilization® III Complete
+3557::Shattered Union
+3558::Medieval II: Total War™
+3559::Heroes of Annihilated Empires
+3560::Global Adventures
+3561::Six Days of Snow
+3562::Bombernauts
+3563::Bloodworks
+3564::Sid Meier's Pirates!
+3565::Half-Life 2: Lost Coast
+3566::Red Orchestra: Ostfront 41-45
+3567::Space Empires IV Deluxe
+3568::Earth 2160
+3569::Half-Life Deathmatch: Source
+3570::SiN Episodes: Emergence
+3571::Typer Shark! Deluxe
+3572::Bejeweled Deluxe
+3573::Dynomite Deluxe
+3574::AstroPop Deluxe
+3575::Zuma Deluxe
+3576::Insaniquarium Deluxe
+3577::PLAYERUNKN4WN: Zombie
+3578::Gothic 1
+3579::Geneforge 1
+3580::Counter-Strike: Condition Zero
diff --git a/data/ref/steam/test.ipynb b/data/ref/steam/test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..705a4b61643224d85f85c471011d4fdb54745cc9
--- /dev/null
+++ b/data/ref/steam/test.ipynb
@@ -0,0 +1,190 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                      seq  next  len_seq  \\\n",
+      "0       [3442, 3448, 3451, 97, 180, 78, 3300, 152, 340...  3462       10   \n",
+      "1       [3448, 3451, 97, 180, 78, 3300, 152, 3401, 185...  3354       10   \n",
+      "2       [3451, 97, 180, 78, 3300, 152, 3401, 185, 3462...   274       10   \n",
+      "3       [97, 180, 78, 3300, 152, 3401, 185, 3462, 3354...  3276       10   \n",
+      "4       [180, 78, 3300, 152, 3401, 185, 3462, 3354, 27...  3225       10   \n",
+      "...                                                   ...   ...      ...   \n",
+      "151051  [129, 2411, 1033, 204, 498, 3242, 3505, 3224, ...  1734       10   \n",
+      "151052  [2411, 1033, 204, 498, 3242, 3505, 3224, 2524,...  1945       10   \n",
+      "151053  [1033, 204, 498, 3242, 3505, 3224, 2524, 1099,...  1134       10   \n",
+      "151054  [3431, 2808, 3214, 3284, 3001, 800, 2636, 3581...  2636        7   \n",
+      "151055  [14, 58, 3510, 15, 68, 86, 3238, 2411, 1134, 2...  1073       10   \n",
+      "\n",
+      "                                                   review  \\\n",
+      "0       [Maybe a niche thing, but it has solid gamepla...   \n",
+      "1       [Borderlands at its core is spectacular: the g...   \n",
+      "2       [You get to be the hero of the people WHILE bl...   \n",
+      "3       [The game is far from perfect, I could point o...   \n",
+      "4       [Very instructive', \"There's a ton of stuff wr...   \n",
+      "...                                                   ...   \n",
+      "151051  [This game is the best when I was bored playin...   \n",
+      "151052  [I don't even know where to begin on how good ...   \n",
+      "151053  [BrainBread 2 has really fun single player bec...   \n",
+      "151054  [Starting the game, you will go into an area w...   \n",
+      "151055  [Think the original Doom but with more awesome...   \n",
+      "\n",
+      "                                              next_review  \n",
+      "0       I played to the end, I feel obligated to recom...  \n",
+      "1       I was pretty shocked by how much I liked this ...  \n",
+      "2       words cant explain how good this game is GET I...  \n",
+      "3       best game i ever bought a rts with actual play...  \n",
+      "4       Extremely punishing game, but every death is p...  \n",
+      "...                                                   ...  \n",
+      "151051            I had this game since it first came out  \n",
+      "151052    give me a reason to actually hate white people.  \n",
+      "151053  Heroes and Generals is a really fun game but r...  \n",
+      "151054  The game is fun it's a cool concept, and I lik...  \n",
+      "151055  It's Wasted Potential but still a pretty good ...  \n",
+      "\n",
+      "[151056 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# 读取pickle文件\n",
+    "df = pd.read_pickle(\"./train_data.df\")\n",
+    "\n",
+    "# 打印数据框\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                    seq  next  len_seq  \\\n",
+      "0     [1221, 899, 490, 1985, 255, 3058, 3119, 2930, ...  1417       10   \n",
+      "1     [3448, 855, 2710, 6, 2694, 3581, 3581, 3581, 3...  2694        5   \n",
+      "2     [3255, 3399, 3330, 3500, 2729, 1335, 68, 1866,...   642       10   \n",
+      "3     [273, 3462, 2656, 2033, 3333, 3284, 2110, 470,...   470        8   \n",
+      "4     [3284, 1134, 2992, 727, 3442, 831, 6, 76, 2899...  2899        9   \n",
+      "...                                                 ...   ...      ...   \n",
+      "1189  [6, 2986, 2683, 495, 3580, 3171, 234, 164, 2, ...  2853       10   \n",
+      "1190  [2262, 2694, 1358, 883, 2174, 6, 2683, 1604, 4...  1478       10   \n",
+      "1191  [150, 1099, 3488, 3580, 2725, 3171, 535, 1622,...   206       10   \n",
+      "1192  [206, 6, 52, 3272, 204, 926, 498, 2712, 2496, ...  3202       10   \n",
+      "1193  [3251, 3300, 1822, 150, 2160, 2046, 6, 1038, 2...  1602       10   \n",
+      "\n",
+      "                                                 review  \\\n",
+      "0     [Really nice game, Would recommend!, meh, This...   \n",
+      "1     [finished it, its good but after i finished it...   \n",
+      "2     [Paradox Interactive\\tdouble the price for alm...   \n",
+      "3     [Nice, Combat system can be better, All the fu...   \n",
+      "4     [9/10, 10/10, 9/10, 10/10, -99999/10, 9/10, id...   \n",
+      "...                                                 ...   \n",
+      "1189  [RDM simulator, 192 cubic tonnes of mud simula...   \n",
+      "1190  [do I have a brain?, 18+, relationship of Park...   \n",
+      "1191  [For DC, Любителям Аниме., 123, Ну такое., Hor...   \n",
+      "1192  [Peed all over my dog then I started the game....   \n",
+      "1193  [Great for a few hours until you beat it., Fun...   \n",
+      "\n",
+      "                                            next_review  \n",
+      "0     HUGE improvements on graphics and gameplay and...  \n",
+      "1                                                   ok.  \n",
+      "2                                              Unstable  \n",
+      "3     You've probably already heard that this game i...  \n",
+      "4                                                 10/10  \n",
+      "...                                                 ...  \n",
+      "1189                      best game ever made simulator  \n",
+      "1190                                new vision, new way  \n",
+      "1191    Кратко: мужик живёт с мамой в маленьком фургоне  \n",
+      "1192  Story mode kinda sucks but the rest of the gam...  \n",
+      "1193  While being free-to-play, Robocraft seems very...  \n",
+      "\n",
+      "[1194 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 读取pickle文件\n",
+    "df = pd.read_pickle(\"./Test_data.df\")\n",
+    "\n",
+    "# 打印数据框\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "interaction_pairs = []\n",
+    "\n",
+    "for idx, row in df.iterrows():\n",
+    "    user_id = idx  # 如果你有显式的用户ID，这里应该使用那个用户ID\n",
+    "    items = row['seq'] + [row['next']]\n",
+    "    for item in items:\n",
+    "        interaction_pairs.append((user_id, item))\n",
+    "\n",
+    "# 转换成DataFrame\n",
+    "interaction_df = pd.DataFrame(interaction_pairs, columns=['user_id', 'item_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Interaction Density: 0.0031\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 创建用户-项矩阵\n",
+    "user_item_matrix = pd.crosstab(interaction_df['user_id'], interaction_df['item_id'])\n",
+    "\n",
+    "# 计算非零元素的比例来确定稠密度\n",
+    "non_zero_count = user_item_matrix.astype(bool).sum().sum()  # 计算非零元素的总数\n",
+    "total_elements = user_item_matrix.size  # 矩阵中元素的总数\n",
+    "\n",
+    "interaction_density = non_zero_count / total_elements\n",
+    "print(f\"Interaction Density: {interaction_density:.4f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "MOE4REC",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/ref/steam/train_data.df b/data/ref/steam/train_data.df
new file mode 100644
index 0000000000000000000000000000000000000000..ca8aa4186e94c75d581381edf10f88970831aa54
--- /dev/null
+++ b/data/ref/steam/train_data.df
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90f8e2ec827427d271cf0b03db45ed5ac3231840fbd022ac57cfae6061ed5b63
+size 7322362
diff --git a/data/steam_data.py b/data/steam_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..055292ce608d50e5a096c92401dffa1a20d368b1
--- /dev/null
+++ b/data/steam_data.py
@@ -0,0 +1,103 @@
+import torch
+import os.path as op
+import numpy as np
+import pickle as pkl
+import torch.utils.data as data
+
+import pandas as pd
+import random
+
+class SteamData(data.Dataset):
+    def __init__(self, data_dir=r'data/ref/steam',
+                 stage=None,
+                 cans_num=10,
+                 sep=", ",
+                 no_augment=True):
+        self.__dict__.update(locals())
+        self.aug = (stage=='train') and not no_augment
+        self.padding_item_id=3581
+        self.check_files()
+
+    # 返回session_data['seq']长度
+    def __len__(self):
+        return len(self.session_data['seq'])
+
+    # 获取索引i的样本(批次i)
+    def __getitem__(self, i):
+        temp = self.session_data.iloc[i]
+        candidates = self.negative_sampling(temp['seq_unpad'],temp['next'])
+        cans_name=[self.item_id2name[can] for can in candidates]
+        sample = {
+            'seq': temp['seq'],
+            'seq_name': temp['seq_title'],
+            'len_seq': temp['len_seq'],
+            'seq_str': self.sep.join(temp['seq_title']),
+            'cans': candidates,
+            'cans_name': cans_name,
+            'cans_str': self.sep.join(cans_name),
+            'len_cans': self.cans_num,
+            'item_id': temp['next'],
+            'item_name': temp['next_item_name'],
+            'correct_answer': temp['next_item_name']
+        }
+        return sample
+    
+    # 进行负采样, 返回序列ID列表
+    def negative_sampling(self,seq_unpad,next_item):
+        # canset: 所有游戏id中不在seq_unpad中的游戏id
+        canset=[i for i in list(self.item_id2name.keys()) if i not in seq_unpad and i!=next_item]
+        # 随机选择cans_num-1个游戏id, 加上next_item
+        candidates=random.sample(canset, self.cans_num-1)+[next_item]
+        random.shuffle(candidates)
+        return candidates  
+
+    # 检查并加载数据文件
+    def check_files(self):
+        self.item_id2name=self.get_game_id2name()
+        if self.stage=='train':
+            filename="train_data.df"
+        elif self.stage=='val':
+            filename="Val_data.df"
+        elif self.stage=='test':
+            filename="Test_data.df"
+        data_path=op.join(self.data_dir, filename)
+        # 根据data_path和id2name字典加载数据
+        self.session_data = self.session_data4frame(data_path, self.item_id2name)  
+
+    # 获取游戏id到游戏名的映射, 返回字典
+    def get_game_id2name(self):
+        game_id2name = dict()
+        item_path=op.join(self.data_dir, 'id2name.txt')
+        with open(item_path, 'r') as f:
+            for l in f.readlines():
+                ll = l.strip('\n').split('::')
+                game_id2name[int(ll[0])] = ll[1].strip()
+        return game_id2name
+    
+    # 对数据进行预处理
+    def session_data4frame(self, datapath, game_id2name):
+        # 根据datapath读取pd数据
+        train_data = pd.read_pickle(datapath)
+        train_data = train_data[train_data['len_seq'] >= 3]
+        # 从序列中移除填充项
+        def remove_padding(xx):
+            x = xx[:]
+            for i in range(10):
+                try:
+                    x.remove(self.padding_item_id)
+                except:
+                    break
+            return x
+        # 去除pad的train_data序列 -> train_data['seq_unpad']
+        train_data['seq_unpad'] = train_data['seq'].apply(remove_padding)
+        # 序列号 -> 游戏名
+        def seq_to_title(x): 
+            return [game_id2name[x_i] for x_i in x]
+        # 转换train_data ID序列为游戏名序列 -> train_data['seq_title']
+        train_data['seq_title'] = train_data['seq_unpad'].apply(seq_to_title)
+        # 单个序列 -> 游戏名
+        def next_item_title(x): 
+            return game_id2name[x]
+        # 转换train_data['next'] ID序列为游戏名序列 -> train_data['next_item_name']
+        train_data['next_item_name'] = train_data['next'].apply(next_item_title)
+        return train_data
\ No newline at end of file
diff --git a/debug/modeling_llama.py b/debug/modeling_llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..62ac21c2de9068f9e4d2ce0d7059fea690d0db21
--- /dev/null
+++ b/debug/modeling_llama.py
@@ -0,0 +1,886 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch LLaMA model."""
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from ...activations import ACT2FN
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from ...modeling_utils import PreTrainedModel
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from .configuration_llama import LlamaConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        # convert into half-precision if necessary
+        if self.weight.dtype in [torch.float16, torch.bfloat16]:
+            hidden_states = hidden_states.to(self.weight.dtype)
+
+        return self.weight * hidden_states
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+        # Build here to make `torch.jit.trace` work.
+        self.max_seq_len_cached = max_position_embeddings
+        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
+        if seq_len > self.max_seq_len_cached:
+            self.max_seq_len_cached = seq_len
+            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
+            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            # Different from paper, but it uses a different permutation in order to obtain the same calculation
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+            self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+            self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
+    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
+    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class LlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+        # [bsz, nh, t, hd]
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaDecoderLayer(nn.Module):
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+LLAMA_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`LlamaConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaPreTrainedModel(PreTrainedModel):
+    config_class = LlamaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaDecoderLayer"]
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, LlamaModel):
+            module.gradient_checkpointing = value
+
+
+LLAMA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaModel(LlamaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class LlamaForCausalLM(LlamaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LlamaModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        # !!!
+        **kwargs
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you consciours? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The LLaMa Model transformer with a sequence classification head on top (linear layer).
+
+    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    LLAMA_START_DOCSTRING,
+)
+class LlamaForSequenceClassification(LlamaPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = LlamaModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
diff --git a/debug/utils.py b/debug/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b54a59cf1ea72ff5d6713d50ee8bcf4c25436455
--- /dev/null
+++ b/debug/utils.py
@@ -0,0 +1,4032 @@
+# coding=utf-8
+# Copyright 2020 The Google AI Language Team Authors, Facebook AI Research authors and The HuggingFace Inc. team.
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import inspect
+import warnings
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.distributed as dist
+from torch import nn
+
+from ..deepspeed import is_deepspeed_zero3_enabled
+from ..modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
+from ..models.auto import (
+    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
+    MODEL_FOR_CAUSAL_LM_MAPPING,
+    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
+    MODEL_FOR_VISION_2_SEQ_MAPPING,
+)
+from ..utils import ModelOutput, logging
+from .beam_constraints import DisjunctiveConstraint, PhrasalConstraint
+from .beam_search import BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
+from .configuration_utils import GenerationConfig
+from .logits_process import (
+    EncoderNoRepeatNGramLogitsProcessor,
+    EncoderRepetitionPenaltyLogitsProcessor,
+    EpsilonLogitsWarper,
+    EtaLogitsWarper,
+    ExponentialDecayLengthPenalty,
+    ForcedBOSTokenLogitsProcessor,
+    ForcedEOSTokenLogitsProcessor,
+    ForceTokensLogitsProcessor,
+    HammingDiversityLogitsProcessor,
+    InfNanRemoveLogitsProcessor,
+    LogitNormalization,
+    LogitsProcessorList,
+    MinLengthLogitsProcessor,
+    MinNewTokensLengthLogitsProcessor,
+    NoBadWordsLogitsProcessor,
+    NoRepeatNGramLogitsProcessor,
+    PrefixConstrainedLogitsProcessor,
+    RepetitionPenaltyLogitsProcessor,
+    SuppressTokensAtBeginLogitsProcessor,
+    SuppressTokensLogitsProcessor,
+    TemperatureLogitsWarper,
+    TopKLogitsWarper,
+    TopPLogitsWarper,
+    TypicalLogitsWarper,
+)
+from .stopping_criteria import (
+    MaxLengthCriteria,
+    MaxTimeCriteria,
+    StoppingCriteria,
+    StoppingCriteriaList,
+    validate_stopping_criteria,
+)
+
+
+if TYPE_CHECKING:
+    from .streamers import BaseStreamer
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class GreedySearchDecoderOnlyOutput(ModelOutput):
+    """
+    Base class for outputs of decoder-only generation models using greedy search.
+
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
+            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
+            each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
+        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class ContrastiveSearchEncoderDecoderOutput(ModelOutput):
+    """
+    Base class for outputs of decoder-only generation models using contrastive search.
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
+            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
+            each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
+        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape `(batch_size, sequence_length, hidden_size)`.
+        decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class ContrastiveSearchDecoderOnlyOutput(ModelOutput):
+    """
+    Base class for outputs of decoder-only generation models using contrastive search.
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when
+        `config.output_scores=True`):
+            Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
+            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
+            each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
+        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is
+        passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class GreedySearchEncoderDecoderOutput(ModelOutput):
+    """
+    Base class for outputs of encoder-decoder generation models using greedy search. Hidden states and attention
+    weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
+    encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
+
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
+            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
+            each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
+        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape `(batch_size, sequence_length, hidden_size)`.
+        decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class SampleDecoderOnlyOutput(ModelOutput):
+    """
+    Base class for outputs of decoder-only generation models using sampling.
+
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
+            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
+            each generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
+        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(num_return_sequences*batch_size, num_heads, generated_length,
+            sequence_length)`.
+        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(num_return_sequences*batch_size, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class SampleEncoderDecoderOutput(ModelOutput):
+    """
+    Base class for outputs of encoder-decoder generation models using sampling. Hidden states and attention weights of
+    the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
+    attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
+
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Processed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
+            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
+            each generated token), with each tensor of shape `(batch_size*num_return_sequences, config.vocab_size)`.
+        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape
+            `(batch_size*num_return_sequences, num_heads, sequence_length, sequence_length)`.
+        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape `(batch_size*num_return_sequences, sequence_length, hidden_size)`.
+        decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_return_sequences, num_heads, generated_length,
+            sequence_length)`.
+        cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_return_sequences, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class BeamSearchDecoderOnlyOutput(ModelOutput):
+    """
+    Base class for outputs of decoder-only generation models using beam search.
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Final beam scores of the generated `sequences`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
+            of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
+            Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
+            with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
+        beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
+            `(batch_size*num_return_sequences, sequence_length)`.
+        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
+        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    sequences_scores: Optional[torch.FloatTensor] = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    beam_indices: Optional[torch.LongTensor] = None
+    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class BeamSearchEncoderDecoderOutput(ModelOutput):
+    """
+    Base class for outputs of encoder-decoder generation models using beam search. Hidden states and attention weights
+    of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the encoder_hidden_states
+    attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        sequences_scores (`torch.FloatTensor` of shape `(batch_size*num_return_sequences)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Final beam scores of the generated `sequences`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
+            of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
+            Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
+            with each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
+        beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
+            `(batch_size*num_return_sequences, sequence_length)`.
+        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape `(batch_size*num_beams*num_return_sequences, sequence_length, hidden_size)`.
+        decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, num_heads, generated_length,
+            sequence_length)`.
+        cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_beams*num_return_sequences, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    sequences_scores: Optional[torch.FloatTensor] = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    beam_indices: Optional[torch.LongTensor] = None
+    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class BeamSampleDecoderOnlyOutput(ModelOutput):
+    """
+    Base class for outputs of decoder-only generation models using beam sample.
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        sequences_scores (`torch.FloatTensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Final beam scores of the generated `sequences`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
+            of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
+            Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
+            with each tensor of shape `(batch_size*num_beams*num_return_sequences, config.vocab_size)`.
+        beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
+            `(batch_size*num_return_sequences, sequence_length)`.
+        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
+        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    sequences_scores: Optional[torch.FloatTensor] = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    beam_indices: Optional[torch.LongTensor] = None
+    attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+@dataclass
+class BeamSampleEncoderDecoderOutput(ModelOutput):
+    """
+    Base class for outputs of encoder-decoder generation models using beam sampling. Hidden states and attention
+    weights of the decoder (respectively the encoder) can be accessed via the encoder_attentions and the
+    encoder_hidden_states attributes (respectively the decoder_attentions and the decoder_hidden_states attributes)
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size*num_beams, sequence_length)`):
+            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
+            if all batches finished early due to the `eos_token_id`.
+        sequences_scores (`torch.FloatTensor` of shape `(batch_size * num_return_sequence)`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Final beam scores of the generated `sequences`.
+        scores (`tuple(torch.FloatTensor)` *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Beam transition scores for each vocabulary token at each generation step. Beam transition scores consisting
+            of log probabilities of tokens conditioned on log softmax of previously generated tokens in this beam.
+            Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token),
+            with each tensor of shape `(batch_size*num_beams, config.vocab_size)`).
+        beam_indices (`torch.LongTensor`, *optional*, returned when `output_scores=True` is passed or when `config.output_scores=True`):
+            Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
+            `(batch_size*num_return_sequences, sequence_length)`.
+        encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer of the decoder) of shape `(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+        encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+            shape `(batch_size*num_beams, sequence_length, hidden_size)`.
+        decoder_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_beams, num_heads, generated_length, sequence_length)`.
+        cross_attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or `config.output_attentions=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
+        decoder_hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
+            `torch.FloatTensor` of shape `(batch_size*num_beams, generated_length, hidden_size)`.
+    """
+
+    sequences: torch.LongTensor = None
+    sequences_scores: Optional[torch.FloatTensor] = None
+    scores: Optional[Tuple[torch.FloatTensor]] = None
+    beam_indices: Optional[torch.LongTensor] = None
+    encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None
+    encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    decoder_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    cross_attentions: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    decoder_hidden_states: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+
+
+GreedySearchOutput = Union[GreedySearchEncoderDecoderOutput, GreedySearchDecoderOnlyOutput]
+SampleOutput = Union[SampleEncoderDecoderOutput, SampleDecoderOnlyOutput]
+BeamSearchOutput = Union[BeamSearchEncoderDecoderOutput, BeamSearchDecoderOnlyOutput]
+BeamSampleOutput = Union[BeamSampleEncoderDecoderOutput, BeamSampleDecoderOnlyOutput]
+ContrastiveSearchOutput = Union[ContrastiveSearchEncoderDecoderOutput, ContrastiveSearchDecoderOnlyOutput]
+GenerateOutput = Union[GreedySearchOutput, SampleOutput, BeamSearchOutput, BeamSampleOutput, ContrastiveSearchOutput]
+
+
+class GenerationMixin:
+    """
+    A class containing all functions for auto-regressive text generation, to be used as a mixin in [`PreTrainedModel`].
+
+    The class exposes [`~generation.GenerationMixin.generate`], which can be used for:
+        - *greedy decoding* by calling [`~generation.GenerationMixin.greedy_search`] if `num_beams=1` and
+          `do_sample=False`
+        - *contrastive search* by calling [`~generation.GenerationMixin.contrastive_search`] if `penalty_alpha>0` and
+          `top_k>1`
+        - *multinomial sampling* by calling [`~generation.GenerationMixin.sample`] if `num_beams=1` and
+          `do_sample=True`
+        - *beam-search decoding* by calling [`~generation.GenerationMixin.beam_search`] if `num_beams>1` and
+          `do_sample=False`
+        - *beam-search multinomial sampling* by calling [`~generation.GenerationMixin.beam_sample`] if `num_beams>1`
+          and `do_sample=True`
+        - *diverse beam-search decoding* by calling [`~generation.GenerationMixin.group_beam_search`], if `num_beams>1`
+          and `num_beam_groups>1`
+        - *constrained beam-search decoding* by calling [`~generation.GenerationMixin.constrained_beam_search`], if
+          `constraints!=None` or `force_words_ids!=None`
+
+    You do not need to call any of the above methods directly. Pass custom parameter values to 'generate' instead. To
+    learn more about decoding strategies refer to the [text generation strategies guide](../generation_strategies).
+    """
+
+    def prepare_inputs_for_generation(self, *args, **kwargs):
+        raise NotImplementedError(
+            "A model class needs to define a `prepare_inputs_for_generation` method in order to use `generate`."
+        )
+
+    def _prepare_model_inputs(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        bos_token_id: Optional[int] = None,
+        model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, Optional[str], Dict[str, torch.Tensor]]:
+        """
+        This function extracts the model-specific `inputs` for generation.
+        """
+        # 1. retrieve all kwargs that are non-None or non-model input related.
+        # some encoder-decoder models have different names for model and encoder
+        if (
+            self.config.is_encoder_decoder
+            and hasattr(self, "encoder")
+            and self.encoder.main_input_name != self.main_input_name
+        ):
+            input_name = self.encoder.main_input_name
+        else:
+            input_name = self.main_input_name
+
+        model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None or k != input_name}
+
+        # 2. check whether model_input_name is passed as kwarg
+        # if yes and `inputs` is None use kwarg inputs
+        inputs_kwarg = model_kwargs.pop(input_name, None)
+        if inputs_kwarg is not None and inputs is not None:
+            raise ValueError(
+                f"`inputs`: {inputs}` were passed alongside {input_name} which is not allowed."
+                f"Make sure to either pass {inputs} or {input_name}=..."
+            )
+        elif inputs_kwarg is not None:
+            inputs = inputs_kwarg
+
+        # 3. In the presence of `inputs_embeds` for text models:
+        # - decoder-only models should complain if the user attempts to pass `inputs_embeds`, but the model
+        # doesn't have its forwarding implemented. `inputs_embeds` is kept in `model_kwargs` and can coexist with
+        # input_ids (`inputs_embeds` will be used in the 1st generation step, as opposed to `input_ids`)
+        # - encoder-decoder models should complain if the user attempts to pass `inputs_embeds` and `input_ids`, and
+        # pull the former to inputs. It will be used in place of `input_ids` to get the encoder hidden states.
+        if input_name == "input_ids" and "inputs_embeds" in model_kwargs:
+            if not self.config.is_encoder_decoder:
+                has_inputs_embeds_forwarding = "inputs_embeds" in set(
+                    inspect.signature(self.prepare_inputs_for_generation).parameters.keys()
+                )
+                if not has_inputs_embeds_forwarding:
+                    raise ValueError(
+                        f"You passed `inputs_embeds` to `.generate()`, but the model class {self.__class__.__name__} "
+                        "doesn't have its forwarding implemented. See the GPT2 implementation for an example "
+                        "(https://github.com/huggingface/transformers/pull/21405), and feel free to open a PR with it!"
+                    )
+                # In this case, `input_ids` is moved to the `model_kwargs`, so a few automations (like the creation of
+                # the attention mask) can rely on the actual model input.
+                model_kwargs["input_ids"] = self._maybe_initialize_input_ids_for_generation(
+                    inputs, bos_token_id, model_kwargs=model_kwargs
+                )
+            else:
+                if inputs is not None:
+                    raise ValueError("You passed `inputs_embeds` and `input_ids` to `.generate()`. Please pick one.")
+            inputs, input_name = model_kwargs["inputs_embeds"], "inputs_embeds"
+
+        # 4. if `inputs` is still None, try to create `input_ids` from BOS token
+        inputs = self._maybe_initialize_input_ids_for_generation(inputs, bos_token_id, model_kwargs)
+        return inputs, input_name, model_kwargs
+
+    def adjust_logits_during_generation(self, logits: torch.FloatTensor, **kwargs) -> torch.FloatTensor:
+        """
+        Implement in subclasses of [`PreTrainedModel`] for custom behavior to adjust the logits in the generate method.
+        """
+        return logits
+
+    def _maybe_initialize_input_ids_for_generation(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        bos_token_id: Optional[int] = None,
+        model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.LongTensor:
+        """Initializes input ids for generation, if necessary."""
+        if inputs is not None:
+            return inputs
+
+        encoder_outputs = model_kwargs.get("encoder_outputs")
+        if self.config.is_encoder_decoder and encoder_outputs is not None:
+            # make dummy input_ids with value -100, as a sanity check ensuring that they won't be used for encoding
+            shape = encoder_outputs.last_hidden_state.size()[:-1]
+            return torch.ones(shape, dtype=torch.long, device=self.device) * -100
+
+        if bos_token_id is None:
+            raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.")
+
+        # If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with
+        # soft-prompting or in multimodal implementations built on top of decoder-only language models.
+        batch_size = 1
+        for value in model_kwargs.values():
+            if isinstance(value, torch.Tensor):
+                batch_size = value.shape[0]
+                break
+        return torch.ones((batch_size, 1), dtype=torch.long, device=self.device) * bos_token_id
+
+    def _prepare_attention_mask_for_generation(
+        self,
+        inputs: torch.Tensor,
+        pad_token_id: Optional[int],
+        eos_token_id: Optional[Union[int, List[int]]],
+    ) -> torch.LongTensor:
+        is_input_ids = len(inputs.shape) == 2 and inputs.dtype in [torch.int, torch.long]
+        is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs)
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or (pad_token_id not in eos_token_id)
+
+        # Check if input is input_ids and padded -> only then is attention_mask defined
+        if is_input_ids and is_pad_token_in_inputs and is_pad_token_not_equal_to_eos_token_id:
+            return inputs.ne(pad_token_id).long()
+        else:
+            return torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device)
+
+    def _prepare_encoder_decoder_kwargs_for_generation(
+        self, inputs_tensor: torch.Tensor, model_kwargs, model_input_name: Optional[str] = None
+    ) -> Dict[str, Any]:
+        # 1. get encoder
+        encoder = self.get_encoder()
+
+        # 2. Prepare encoder args and encoder kwargs from model kwargs.
+        irrelevant_prefix = ["decoder_", "cross_attn", "use_cache"]
+        encoder_kwargs = {
+            argument: value
+            for argument, value in model_kwargs.items()
+            if not any(argument.startswith(p) for p in irrelevant_prefix)
+        }
+        encoder_signature = set(inspect.signature(encoder.forward).parameters)
+        encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature
+        if not encoder_accepts_wildcard:
+            encoder_kwargs = {
+                argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature
+            }
+
+        # 3. make sure that encoder returns `ModelOutput`
+        model_input_name = model_input_name if model_input_name is not None else self.main_input_name
+        encoder_kwargs["return_dict"] = True
+        encoder_kwargs[model_input_name] = inputs_tensor
+        model_kwargs["encoder_outputs"]: ModelOutput = encoder(**encoder_kwargs)
+
+        return model_kwargs
+
+    def _prepare_decoder_input_ids_for_generation(
+        self,
+        batch_size: int,
+        decoder_start_token_id: int = None,
+        bos_token_id: int = None,
+        model_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        device: torch.device = None,
+    ) -> torch.LongTensor:
+        if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
+            return model_kwargs.pop("decoder_input_ids")
+        else:
+            decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id)
+            if device is None:
+                device = self.device
+            return torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id
+
+    def _get_decoder_start_token_id(self, decoder_start_token_id: int = None, bos_token_id: int = None) -> int:
+        decoder_start_token_id = (
+            decoder_start_token_id
+            if decoder_start_token_id is not None
+            else self.generation_config.decoder_start_token_id
+        )
+        bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id
+
+        if decoder_start_token_id is not None:
+            return decoder_start_token_id
+        elif bos_token_id is not None:
+            return bos_token_id
+        raise ValueError(
+            "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation."
+        )
+
+    @staticmethod
+    def _expand_inputs_for_generation(
+        expand_size: int = 1,
+        is_encoder_decoder: bool = False,
+        input_ids: Optional[torch.LongTensor] = None,
+        **model_kwargs,
+    ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
+        """Expands tensors from [batch_size, ...] to [batch_size * expand_size, ...]"""
+
+        def _expand_dict_for_generation(dict_to_expand):
+            for key in dict_to_expand:
+                if dict_to_expand[key] is not None and isinstance(dict_to_expand[key], torch.Tensor):
+                    dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
+            return dict_to_expand
+
+        if input_ids is not None:
+            input_ids = input_ids.repeat_interleave(expand_size, dim=0)
+
+        model_kwargs = _expand_dict_for_generation(model_kwargs)
+
+        if is_encoder_decoder:
+            if model_kwargs.get("encoder_outputs") is None:
+                raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
+            model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
+
+        return input_ids, model_kwargs
+
+    def _extract_past_from_model_output(self, outputs: ModelOutput, standardize_cache_format: bool = False):
+        past_key_values = None
+        if "past_key_values" in outputs:
+            past_key_values = outputs.past_key_values
+        elif "mems" in outputs:
+            past_key_values = outputs.mems
+        elif "past_buckets_states" in outputs:
+            past_key_values = outputs.past_buckets_states
+
+        # Bloom fix: standardizes the cache format when requested
+        if standardize_cache_format and hasattr(self, "_convert_to_standard_cache"):
+            batch_size = outputs.logits.shape[0]
+            past_key_values = self._convert_to_standard_cache(past_key_values, batch_size=batch_size)
+        return past_key_values
+
+    def _update_model_kwargs_for_generation(
+        self,
+        outputs: ModelOutput,
+        model_kwargs: Dict[str, Any],
+        is_encoder_decoder: bool = False,
+        standardize_cache_format: bool = False,
+    ) -> Dict[str, Any]:
+        # update past_key_values
+        model_kwargs["past_key_values"] = self._extract_past_from_model_output(
+            outputs, standardize_cache_format=standardize_cache_format
+        )
+
+        # update token_type_ids with last value
+        if "token_type_ids" in model_kwargs:
+            token_type_ids = model_kwargs["token_type_ids"]
+            model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
+
+        if not is_encoder_decoder:
+            # update attention mask
+            if "attention_mask" in model_kwargs:
+                attention_mask = model_kwargs["attention_mask"]
+                model_kwargs["attention_mask"] = torch.cat(
+                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+                )
+        else:
+            # update decoder attention mask
+            if "decoder_attention_mask" in model_kwargs:
+                decoder_attention_mask = model_kwargs["decoder_attention_mask"]
+                model_kwargs["decoder_attention_mask"] = torch.cat(
+                    [decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))],
+                    dim=-1,
+                )
+
+        return model_kwargs
+
+    def _reorder_cache(self, past_key_values, beam_idx):
+        raise NotImplementedError(
+            f"Make sure that a `_reorder_cache` function is correctly implemented in {self.__class__.__module__} to"
+            f" enable beam search for {self.__class__}"
+        )
+
+    def _get_logits_warper(
+        self,
+        generation_config: GenerationConfig,
+    ) -> LogitsProcessorList:
+        """
+        This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsWarper`] instances
+        used for multinomial sampling.
+        """
+
+        # instantiate warpers list
+        warpers = LogitsProcessorList()
+
+        # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
+        # all samplers can be found in `generation_utils_samplers.py`
+        if generation_config.temperature is not None and generation_config.temperature != 1.0:
+            warpers.append(TemperatureLogitsWarper(generation_config.temperature))
+        min_tokens_to_keep = 2 if generation_config.num_beams > 1 else 1
+        if generation_config.top_k is not None and generation_config.top_k != 0:
+            warpers.append(TopKLogitsWarper(top_k=generation_config.top_k, min_tokens_to_keep=min_tokens_to_keep))
+        if generation_config.top_p is not None and generation_config.top_p < 1.0:
+            warpers.append(TopPLogitsWarper(top_p=generation_config.top_p, min_tokens_to_keep=min_tokens_to_keep))
+        if generation_config.typical_p is not None and generation_config.typical_p < 1.0:
+            warpers.append(
+                TypicalLogitsWarper(mass=generation_config.typical_p, min_tokens_to_keep=min_tokens_to_keep)
+            )
+        if generation_config.epsilon_cutoff is not None and 0.0 < generation_config.epsilon_cutoff < 1.0:
+            warpers.append(
+                EpsilonLogitsWarper(epsilon=generation_config.epsilon_cutoff, min_tokens_to_keep=min_tokens_to_keep)
+            )
+        if generation_config.eta_cutoff is not None and 0.0 < generation_config.eta_cutoff < 1.0:
+            warpers.append(
+                EtaLogitsWarper(epsilon=generation_config.eta_cutoff, min_tokens_to_keep=min_tokens_to_keep)
+            )
+        # `LogitNormalization` should always be the last logit processor, when present
+        if generation_config.renormalize_logits is True:
+            warpers.append(LogitNormalization())
+        return warpers
+
+    def _get_logits_processor(
+        self,
+        generation_config: GenerationConfig,
+        input_ids_seq_length: int,
+        encoder_input_ids: torch.LongTensor,
+        prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]],
+        logits_processor: Optional[LogitsProcessorList],
+    ) -> LogitsProcessorList:
+        """
+        This class returns a [`LogitsProcessorList`] list object that contains all relevant [`LogitsProcessor`]
+        instances used to modify the scores of the language model head.
+        """
+        # instantiate processors list
+        processors = LogitsProcessorList()
+
+        # the following idea is largely copied from this PR: https://github.com/huggingface/transformers/pull/5420/files
+        # all samplers can be found in `generation_utils_samplers.py`
+        if generation_config.diversity_penalty is not None and generation_config.diversity_penalty > 0.0:
+            processors.append(
+                HammingDiversityLogitsProcessor(
+                    diversity_penalty=generation_config.diversity_penalty,
+                    num_beams=generation_config.num_beams,
+                    num_beam_groups=generation_config.num_beam_groups,
+                )
+            )
+        if (
+            generation_config.encoder_repetition_penalty is not None
+            and generation_config.encoder_repetition_penalty != 1.0
+        ):
+            processors.append(
+                EncoderRepetitionPenaltyLogitsProcessor(
+                    penalty=generation_config.encoder_repetition_penalty, encoder_input_ids=encoder_input_ids
+                )
+            )
+        if generation_config.repetition_penalty is not None and generation_config.repetition_penalty != 1.0:
+            processors.append(RepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
+        if generation_config.no_repeat_ngram_size is not None and generation_config.no_repeat_ngram_size > 0:
+            processors.append(NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
+        if (
+            generation_config.encoder_no_repeat_ngram_size is not None
+            and generation_config.encoder_no_repeat_ngram_size > 0
+        ):
+            if self.config.is_encoder_decoder:
+                processors.append(
+                    EncoderNoRepeatNGramLogitsProcessor(
+                        generation_config.encoder_no_repeat_ngram_size, encoder_input_ids
+                    )
+                )
+            else:
+                raise ValueError(
+                    "It's impossible to use `encoder_no_repeat_ngram_size` with decoder-only architecture"
+                )
+        if generation_config.bad_words_ids is not None:
+            processors.append(
+                NoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id)
+            )
+        if (
+            generation_config.min_length is not None
+            and generation_config.eos_token_id is not None
+            and generation_config.min_length > 0
+        ):
+            processors.append(MinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id))
+        if (
+            generation_config.min_new_tokens is not None
+            and generation_config.eos_token_id is not None
+            and generation_config.min_new_tokens > 0
+        ):
+            processors.append(
+                MinNewTokensLengthLogitsProcessor(
+                    input_ids_seq_length, generation_config.min_new_tokens, generation_config.eos_token_id
+                )
+            )
+        if prefix_allowed_tokens_fn is not None:
+            processors.append(
+                PrefixConstrainedLogitsProcessor(
+                    prefix_allowed_tokens_fn, generation_config.num_beams // generation_config.num_beam_groups
+                )
+            )
+        if generation_config.forced_bos_token_id is not None:
+            processors.append(ForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id))
+        if generation_config.forced_eos_token_id is not None:
+            processors.append(
+                ForcedEOSTokenLogitsProcessor(generation_config.max_length, generation_config.forced_eos_token_id)
+            )
+        if generation_config.remove_invalid_values is True:
+            processors.append(InfNanRemoveLogitsProcessor())
+        if generation_config.exponential_decay_length_penalty is not None:
+            processors.append(
+                ExponentialDecayLengthPenalty(
+                    generation_config.exponential_decay_length_penalty,
+                    generation_config.eos_token_id,
+                    input_ids_seq_length,
+                )
+            )
+        if generation_config.suppress_tokens is not None:
+            processors.append(SuppressTokensLogitsProcessor(generation_config.suppress_tokens))
+        if generation_config.begin_suppress_tokens is not None:
+            begin_index = input_ids_seq_length
+            begin_index = (
+                begin_index
+                if (input_ids_seq_length > 1 or generation_config.forced_bos_token_id is None)
+                else begin_index + 1
+            )
+            if generation_config.forced_decoder_ids is not None:
+                # generation starts after the last token that is forced
+                begin_index += generation_config.forced_decoder_ids[-1][0]
+            processors.append(
+                SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index)
+            )
+        if generation_config.forced_decoder_ids is not None:
+            processors.append(ForceTokensLogitsProcessor(generation_config.forced_decoder_ids))
+        processors = self._merge_criteria_processor_list(processors, logits_processor)
+        # `LogitNormalization` should always be the last logit processor, when present
+        if generation_config.renormalize_logits is True:
+            processors.append(LogitNormalization())
+        return processors
+
+    def _get_stopping_criteria(
+        self, generation_config: GenerationConfig, stopping_criteria: Optional[StoppingCriteriaList]
+    ) -> StoppingCriteriaList:
+        criteria = StoppingCriteriaList()
+        if generation_config.max_length is not None:
+            criteria.append(MaxLengthCriteria(max_length=generation_config.max_length))
+        if generation_config.max_time is not None:
+            criteria.append(MaxTimeCriteria(max_time=generation_config.max_time))
+        criteria = self._merge_criteria_processor_list(criteria, stopping_criteria)
+        return criteria
+
+    def _merge_criteria_processor_list(
+        self,
+        default_list: Union[LogitsProcessorList, StoppingCriteriaList],
+        custom_list: Union[LogitsProcessorList, StoppingCriteriaList],
+    ) -> Union[LogitsProcessorList, StoppingCriteriaList]:
+        if len(custom_list) == 0:
+            return default_list
+        for default in default_list:
+            for custom in custom_list:
+                if type(custom) is type(default):
+                    object_type = "stopping criteria" if isinstance(custom, StoppingCriteria) else "logits processor"
+                    raise ValueError(
+                        f"A custom {object_type} of type {type(custom)} with values {custom} has been passed to"
+                        f" `generate`, but it has already been created with the values {default}. {default} has been"
+                        " created by passing the corresponding arguments to generate or by the model's config default"
+                        f" values. If you just want to change the default values of {object_type} consider passing"
+                        f" them as arguments to `generate` instead of using a custom {object_type}."
+                    )
+        default_list.extend(custom_list)
+        return default_list
+
+    def compute_transition_scores(
+        self,
+        sequences: torch.Tensor,
+        scores: Tuple[torch.Tensor],
+        beam_indices: Optional[torch.Tensor] = None,
+        normalize_logits: bool = False,
+    ) -> torch.Tensor:
+        """
+        Computes the transition scores of sequences given the generation scores (and beam indices, if beam search was
+        used). This is a convenient method to quicky obtain the scores of the selected tokens at generation time.
+
+        Parameters:
+            sequences (`torch.LongTensor`):
+                The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or
+                shorter if all batches finished early due to the `eos_token_id`.
+            scores (`tuple(torch.FloatTensor)`):
+                Transition scores for each vocabulary token at each generation step. Beam transition scores consisting
+                of log probabilities of tokens conditioned on log softmax of previously generated tokens Tuple of
+                `torch.FloatTensor` with up to `max_new_tokens` elements (one element for each generated token), with
+                each tensor of shape `(batch_size*num_beams, config.vocab_size)`.
+            beam_indices (`torch.LongTensor`, *optional*):
+                Beam indices of generated token id at each generation step. `torch.LongTensor` of shape
+                `(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at
+                generate-time.
+            normalize_logits (`bool`, *optional*, defaults to `False`):
+                Whether to normalize the logits (which, for legacy reasons, may be unnormalized).
+
+        Return:
+            `torch.Tensor`: A `torch.Tensor` of shape `(batch_size*num_return_sequences, sequence_length)` containing
+                the transition scores (logits)
+
+        Examples:
+
+        ```python
+        >>> from transformers import GPT2Tokenizer, AutoModelForCausalLM
+        >>> import numpy as np
+
+        >>> tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+        >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+        >>> tokenizer.pad_token_id = tokenizer.eos_token_id
+        >>> inputs = tokenizer(["Today is"], return_tensors="pt")
+
+        >>> # Example 1: Print the scores for each token generated with Greedy Search
+        >>> outputs = model.generate(**inputs, max_new_tokens=5, return_dict_in_generate=True, output_scores=True)
+        >>> transition_scores = model.compute_transition_scores(
+        ...     outputs.sequences, outputs.scores, normalize_logits=True
+        ... )
+        >>> # input_length is the length of the input prompt for decoder-only models, like the GPT family, and 1 for
+        >>> # encoder-decoder models, like BART or T5.
+        >>> input_length = 1 if model.config.is_encoder_decoder else inputs.input_ids.shape[1]
+        >>> generated_tokens = outputs.sequences[:, input_length:]
+        >>> for tok, score in zip(generated_tokens[0], transition_scores[0]):
+        ...     # | token | token string | logits | probability
+        ...     print(f"| {tok:5d} | {tokenizer.decode(tok):8s} | {score.numpy():.3f} | {np.exp(score.numpy()):.2%}")
+        |   262 |  the     | -1.414 | 24.33%
+        |  1110 |  day     | -2.609 | 7.36%
+        |   618 |  when    | -2.010 | 13.40%
+        |   356 |  we      | -1.859 | 15.58%
+        |   460 |  can     | -2.508 | 8.14%
+
+        >>> # Example 2: Reconstruct the sequence scores from Beam Search
+        >>> outputs = model.generate(
+        ...     **inputs,
+        ...     max_new_tokens=5,
+        ...     num_beams=4,
+        ...     num_return_sequences=4,
+        ...     return_dict_in_generate=True,
+        ...     output_scores=True,
+        ... )
+        >>> transition_scores = model.compute_transition_scores(
+        ...     outputs.sequences, outputs.scores, outputs.beam_indices, normalize_logits=False
+        ... )
+        >>> # If you sum the generated tokens' scores and apply the length penalty, you'll get the sequence scores.
+        >>> # Tip: recomputing the scores is only guaranteed to match with `normalize_logits=False`. Depending on the
+        >>> # use case, you might want to recompute it with `normalize_logits=True`.
+        >>> output_length = input_length + np.sum(transition_scores.numpy() < 0, axis=1)
+        >>> length_penalty = model.generation_config.length_penalty
+        >>> reconstructed_scores = transition_scores.sum(axis=1) / (output_length**length_penalty)
+        >>> print(np.allclose(outputs.sequences_scores, reconstructed_scores))
+        True
+        ```"""
+        # 1. In absence of `beam_indices`, we can assume that we come from e.g. greedy search, which is equivalent
+        # to a beam search approach were the first (and only) beam is always selected
+        if beam_indices is None:
+            beam_indices = torch.arange(scores[0].shape[0]).view(-1, 1).to(sequences.device)
+            beam_indices = beam_indices.expand(-1, len(scores))
+
+        # 2. reshape scores as [batch_size*vocab_size, # generation steps] with # generation steps being
+        # seq_len - input_length
+        scores = torch.stack(scores).reshape(len(scores), -1).transpose(0, 1)
+
+        # 3. Optionally normalize the logits (across the vocab dimension)
+        if normalize_logits:
+            scores = scores.reshape(-1, self.config.vocab_size, scores.shape[-1])
+            scores = torch.nn.functional.log_softmax(scores, dim=1)
+            scores = scores.reshape(-1, scores.shape[-1])
+
+        # 4. cut beam_indices to longest beam length
+        beam_indices_mask = beam_indices < 0
+        max_beam_length = (1 - beam_indices_mask.long()).sum(-1).max()
+        beam_indices = beam_indices.clone()[:, :max_beam_length]
+        beam_indices_mask = beam_indices_mask[:, :max_beam_length]
+
+        # 5. Set indices of beams that finished early to 0; such indices will be masked correctly afterwards
+        beam_indices[beam_indices_mask] = 0
+
+        # 6. multiply beam_indices with vocab size to gather correctly from scores
+        beam_sequence_indices = beam_indices * self.config.vocab_size
+
+        # 7. Define which indices contributed to scores
+        cut_idx = sequences.shape[-1] - max_beam_length
+        indices = sequences[:, cut_idx:] + beam_sequence_indices
+
+        # 8. Compute scores
+        transition_scores = scores.gather(0, indices)
+
+        # 9. Mask out transition_scores of beams that stopped early
+        transition_scores[beam_indices_mask] = 0
+
+        return transition_scores
+
+    def _validate_model_class(self):
+        """
+        Confirms that the model class is compatible with generation. If not, raises an exception that points to the
+        right class to use.
+        """
+        if not self.can_generate():
+            generate_compatible_mappings = [
+                MODEL_FOR_CAUSAL_LM_MAPPING,
+                MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
+                MODEL_FOR_VISION_2_SEQ_MAPPING,
+                MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
+                MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
+            ]
+            generate_compatible_classes = set()
+            for model_mapping in generate_compatible_mappings:
+                supported_models = model_mapping.get(type(self.config), default=None)
+                if supported_models is not None:
+                    generate_compatible_classes.add(supported_models.__name__)
+            exception_message = (
+                f"The current model class ({self.__class__.__name__}) is not compatible with `.generate()`, as "
+                "it doesn't have a language model head."
+            )
+            if generate_compatible_classes:
+                exception_message += f" Please use one of the following classes instead: {generate_compatible_classes}"
+            raise TypeError(exception_message)
+
+    def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
+        """Validates model kwargs for generation. Generate argument typos will also be caught here."""
+        # Excludes arguments that are handled before calling any model function
+        if self.config.is_encoder_decoder:
+            for key in ["decoder_input_ids"]:
+                model_kwargs.pop(key, None)
+
+        unused_model_args = []
+        model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
+        # `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
+        # `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
+        if "kwargs" in model_args or "model_kwargs" in model_args:
+            model_args |= set(inspect.signature(self.forward).parameters)
+        for key, value in model_kwargs.items():
+            # !!!
+            if value is not None and key not in model_args and key not in ['user_embeds', 'gate_weights', 'domain_id']:
+                unused_model_args.append(key)
+        
+        if unused_model_args:
+            raise ValueError(
+                f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
+                " generate arguments will also show up in this list)"
+            )
+
+    @torch.no_grad()
+    def generate(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        generation_config: Optional[GenerationConfig] = None,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
+        synced_gpus: Optional[bool] = None,
+        streamer: Optional["BaseStreamer"] = None,
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor]:
+        r"""
+
+        Generates sequences of token ids for models with a language modeling head.
+
+        <Tip warning={true}>
+
+        Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
+        model's default generation configuration. You can override any `generation_config` by passing the corresponding
+        parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
+
+        For an overview of generation strategies and code examples, check out the [following
+        guide](../generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
+                The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
+                method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
+                should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
+                `input_ids`, `input_values`, `input_features`, or `pixel_values`.
+            generation_config (`~generation.GenerationConfig`, *optional*):
+                The generation configuration to be used as base parametrization for the generation call. `**kwargs`
+                passed to generate matching the attributes of `generation_config` will override them. If
+                `generation_config` is not provided, the default will be used, which had the following loading
+                priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
+                configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
+                default values, whose documentation should be checked to parameterize generation.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                Custom logits processors that complement the default logits processors built from arguments and
+                generation config. If a logit processor is passed that is already created with the arguments or a
+                generation config an error is thrown. This feature is intended for advanced users.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                Custom stopping criteria that complement the default stopping criteria built from arguments and a
+                generation config. If a stopping criteria is passed that is already created with the arguments or a
+                generation config an error is thrown. This feature is intended for advanced users.
+            prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*):
+                If provided, this function constraints the beam search to allowed tokens only at each step. If not
+                provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
+                `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
+                on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
+                for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
+                Retrieval](https://arxiv.org/abs/2010.00904).
+            synced_gpus (`bool`, *optional*):
+                Whether to continue running the while loop until max_length. Unless overridden this flag will be set to
+                `True` under DeepSpeed ZeRO Stage 3 multiple GPUs environment to avoid hanging if one GPU finished
+                generating before other GPUs. Otherwise it'll be set to `False`.
+            streamer (`BaseStreamer`, *optional*):
+                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+
+            kwargs:
+                Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
+                forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
+                specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
+
+        Return:
+            [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
+            or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
+
+                If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
+                [`~utils.ModelOutput`] types are:
+
+                    - [`~generation.GreedySearchDecoderOnlyOutput`],
+                    - [`~generation.SampleDecoderOnlyOutput`],
+                    - [`~generation.BeamSearchDecoderOnlyOutput`],
+                    - [`~generation.BeamSampleDecoderOnlyOutput`]
+
+                If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
+                [`~utils.ModelOutput`] types are:
+
+                    - [`~generation.GreedySearchEncoderDecoderOutput`],
+                    - [`~generation.SampleEncoderDecoderOutput`],
+                    - [`~generation.BeamSearchEncoderDecoderOutput`],
+                    - [`~generation.BeamSampleEncoderDecoderOutput`]
+        """
+
+        if synced_gpus is None:
+            if is_deepspeed_zero3_enabled() and dist.get_world_size() > 1:
+                synced_gpus = True
+            else:
+                synced_gpus = False
+
+        # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
+        self._validate_model_class()
+
+        # priority: `generation_config` argument > `model.generation_config` (the default generation config)
+        if generation_config is None:
+            # legacy: users may modify the model configuration to control generation -- update the generation config
+            # model attribute accordingly, if it was created from the model config
+            if self.generation_config._from_model_config:
+                new_generation_config = GenerationConfig.from_model_config(self.config)
+                if new_generation_config != self.generation_config:
+                    warnings.warn(
+                        "You have modified the pretrained model configuration to control generation. This is a"
+                        " deprecated strategy to control generation and will be removed soon, in a future version."
+                        " Please use a generation configuration file (see"
+                        " https://huggingface.co/docs/transformers/main_classes/text_generation)"
+                    )
+                    self.generation_config = new_generation_config
+            generation_config = self.generation_config
+
+        generation_config = copy.deepcopy(generation_config)
+        model_kwargs = generation_config.update(**kwargs)  # All unused kwargs must be model kwargs
+        generation_config.validate()
+        # !!!
+        self._validate_model_kwargs(model_kwargs.copy())
+
+        # 2. Set generation parameters if not already defined
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+        if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
+            if model_kwargs.get("attention_mask", None) is None:
+                logger.warning(
+                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
+                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
+                )
+            eos_token_id = generation_config.eos_token_id
+            if isinstance(eos_token_id, list):
+                eos_token_id = eos_token_id[0]
+            logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
+            generation_config.pad_token_id = eos_token_id
+
+        # 3. Define model inputs
+        # inputs_tensor has to be defined
+        # model_input_name is defined if model-specific keyword input is passed
+        # otherwise model_input_name is None
+        # all model-specific keyword inputs are removed from `model_kwargs`
+        inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
+            inputs, generation_config.bos_token_id, model_kwargs
+        )
+        batch_size = inputs_tensor.shape[0]
+
+        # 4. Define other model kwargs
+        model_kwargs["output_attentions"] = generation_config.output_attentions
+        model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
+        model_kwargs["use_cache"] = generation_config.use_cache
+
+        accepts_attention_mask = "attention_mask" in set(inspect.signature(self.forward).parameters.keys())
+        requires_attention_mask = "encoder_outputs" not in model_kwargs
+
+        if model_kwargs.get("attention_mask", None) is None and requires_attention_mask and accepts_attention_mask:
+            model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
+                inputs_tensor, generation_config.pad_token_id, generation_config.eos_token_id
+            )
+
+        # decoder-only models should use left-padding for generation
+        if not self.config.is_encoder_decoder:
+            if (
+                generation_config.pad_token_id is not None
+                and torch.sum(inputs_tensor[:, -1] == generation_config.pad_token_id) > 0
+            ):
+                logger.warning(
+                    "A decoder-only architecture is being used, but right-padding was detected! For correct "
+                    "generation results, please set `padding_side='left'` when initializing the tokenizer."
+                )
+
+        if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
+            # if model is encoder decoder encoder_outputs are created
+            # and added to `model_kwargs`
+            model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
+                inputs_tensor, model_kwargs, model_input_name
+            )
+
+        # 5. Prepare `input_ids` which will be used for auto-regressive generation
+        if self.config.is_encoder_decoder:
+            input_ids = self._prepare_decoder_input_ids_for_generation(
+                batch_size,
+                decoder_start_token_id=generation_config.decoder_start_token_id,
+                bos_token_id=generation_config.bos_token_id,
+                model_kwargs=model_kwargs,
+                device=inputs_tensor.device,
+            )
+
+            # conditional generation for multi-modal models.
+            if "input_ids" in model_kwargs and model_input_name == "pixel_values":
+                input_ids = torch.cat([input_ids, model_kwargs.pop("input_ids")], dim=-1)
+        else:
+            input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
+
+        if streamer is not None:
+            streamer.put(input_ids.cpu())
+
+        # 6. Prepare `max_length` depending on other stopping criteria.
+        input_ids_seq_length = input_ids.shape[-1]
+        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+        if has_default_max_length and generation_config.max_new_tokens is None:
+            warnings.warn(
+                f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
+                "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
+                " recommend using `max_new_tokens` to control the maximum length of the generation.",
+                UserWarning,
+            )
+        elif generation_config.max_new_tokens is not None:
+            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
+            if not has_default_max_length:
+                logger.warn(
+                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
+                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
+                    "Please refer to the documentation for more information. "
+                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
+                    UserWarning,
+                )
+
+        if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
+            raise ValueError(
+                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
+                f" the maximum length ({generation_config.max_length})"
+            )
+        if input_ids_seq_length >= generation_config.max_length:
+            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
+            logger.warning(
+                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
+                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+                " increasing `max_new_tokens`."
+            )
+
+        # 7. determine generation mode
+        is_constraint_gen_mode = (
+            generation_config.constraints is not None or generation_config.force_words_ids is not None
+        )
+
+        is_contrastive_search_gen_mode = (
+            (generation_config.num_beams == 1)
+            and generation_config.top_k is not None
+            and generation_config.top_k > 1
+            and generation_config.do_sample is False
+            and generation_config.penalty_alpha is not None
+            and generation_config.penalty_alpha > 0
+        )
+
+        is_greedy_gen_mode = (
+            (generation_config.num_beams == 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_sample is False
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_sample_gen_mode = (
+            (generation_config.num_beams == 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_sample is True
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_beam_gen_mode = (
+            (generation_config.num_beams > 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_sample is False
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_beam_sample_gen_mode = (
+            (generation_config.num_beams > 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_sample is True
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_group_beam_gen_mode = (
+            (generation_config.num_beams > 1)
+            and (generation_config.num_beam_groups > 1)
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+
+        if generation_config.num_beam_groups > generation_config.num_beams:
+            raise ValueError("`num_beam_groups` has to be smaller or equal to `num_beams`")
+        if is_group_beam_gen_mode and generation_config.do_sample is True:
+            raise ValueError(
+                "Diverse beam search cannot be used in sampling mode. Make sure that `do_sample` is set to `False`."
+            )
+
+        if streamer is not None and (generation_config.num_beams > 1):
+            raise ValueError(
+                "`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
+            )
+
+        if self.device.type != input_ids.device.type:
+            warnings.warn(
+                "You are calling .generate() with the `input_ids` being on a device type different"
+                f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
+                f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
+                " Please make sure that you have put `input_ids` to the"
+                f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
+                " running `.generate()`.",
+                UserWarning,
+            )
+
+        # 8. prepare distribution pre_processing samplers
+        logits_processor = self._get_logits_processor(
+            generation_config=generation_config,
+            input_ids_seq_length=input_ids_seq_length,
+            encoder_input_ids=inputs_tensor,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            logits_processor=logits_processor,
+        )
+
+        # 9. prepare stopping criteria
+        stopping_criteria = self._get_stopping_criteria(
+            generation_config=generation_config, stopping_criteria=stopping_criteria
+        )
+        # 10. go into different generation modes
+        if is_greedy_gen_mode:
+            if generation_config.num_return_sequences > 1:
+                raise ValueError(
+                    f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
+                    " greedy search."
+                )
+
+            # 11. run greedy search
+            return self.greedy_search(
+                input_ids,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                streamer=streamer,
+                **model_kwargs,
+            )
+
+        elif is_contrastive_search_gen_mode:
+            if generation_config.num_return_sequences > 1:
+                raise ValueError(
+                    f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
+                    " contrastive search."
+                )
+
+            return self.contrastive_search(
+                input_ids,
+                top_k=generation_config.top_k,
+                penalty_alpha=generation_config.penalty_alpha,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                streamer=streamer,
+                **model_kwargs,
+            )
+
+        elif is_sample_gen_mode:
+            # 11. prepare logits warper
+            logits_warper = self._get_logits_warper(generation_config)
+
+            # 12. expand input_ids with `num_return_sequences` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_return_sequences,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+
+            # 13. run sample
+            return self.sample(
+                input_ids,
+                logits_processor=logits_processor,
+                logits_warper=logits_warper,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                streamer=streamer,
+                **model_kwargs,
+            )
+
+        elif is_beam_gen_mode:
+            if generation_config.num_return_sequences > generation_config.num_beams:
+                raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
+
+            if stopping_criteria.max_length is None:
+                raise ValueError("`max_length` needs to be a stopping_criteria for now.")
+
+            # 11. prepare beam search scorer
+            beam_scorer = BeamSearchScorer(
+                batch_size=batch_size,
+                num_beams=generation_config.num_beams,
+                device=inputs_tensor.device,
+                length_penalty=generation_config.length_penalty,
+                do_early_stopping=generation_config.early_stopping,
+                num_beam_hyps_to_keep=generation_config.num_return_sequences,
+                max_length=generation_config.max_length,
+            )
+            # 12. interleave input_ids with `num_beams` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_beams,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+            # 13. run beam search
+            return self.beam_search(
+                input_ids,
+                beam_scorer,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+        elif is_beam_sample_gen_mode:
+            # 11. prepare logits warper
+            logits_warper = self._get_logits_warper(generation_config)
+
+            if stopping_criteria.max_length is None:
+                raise ValueError("`max_length` needs to be a stopping_criteria for now.")
+            # 12. prepare beam search scorer
+            beam_scorer = BeamSearchScorer(
+                batch_size=batch_size * generation_config.num_return_sequences,
+                num_beams=generation_config.num_beams,
+                device=inputs_tensor.device,
+                length_penalty=generation_config.length_penalty,
+                do_early_stopping=generation_config.early_stopping,
+                max_length=generation_config.max_length,
+            )
+
+            # 13. interleave input_ids with `num_beams` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_beams * generation_config.num_return_sequences,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+
+            # 14. run beam sample
+            return self.beam_sample(
+                input_ids,
+                beam_scorer,
+                logits_processor=logits_processor,
+                logits_warper=logits_warper,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+        elif is_group_beam_gen_mode:
+            if generation_config.num_return_sequences > generation_config.num_beams:
+                raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
+
+            if generation_config.num_beams % generation_config.num_beam_groups != 0:
+                raise ValueError("`num_beams` should be divisible by `num_beam_groups` for group beam search.")
+
+            if stopping_criteria.max_length is None:
+                raise ValueError("`max_length` needs to be a stopping_criteria for now.")
+
+            has_default_typical_p = kwargs.get("typical_p") is None and generation_config.typical_p == 1.0
+            if not has_default_typical_p:
+                raise ValueError("Decoder argument `typical_p` is not supported with beam groups.")
+
+            # 11. prepare beam search scorer
+            beam_scorer = BeamSearchScorer(
+                batch_size=batch_size,
+                num_beams=generation_config.num_beams,
+                device=inputs_tensor.device,
+                length_penalty=generation_config.length_penalty,
+                do_early_stopping=generation_config.early_stopping,
+                num_beam_hyps_to_keep=generation_config.num_return_sequences,
+                num_beam_groups=generation_config.num_beam_groups,
+                max_length=generation_config.max_length,
+            )
+            # 12. interleave input_ids with `num_beams` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_beams,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+            # 13. run beam search
+            return self.group_beam_search(
+                input_ids,
+                beam_scorer,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+        elif is_constraint_gen_mode:
+            if generation_config.num_return_sequences > generation_config.num_beams:
+                raise ValueError("`num_return_sequences` has to be smaller or equal to `num_beams`.")
+
+            if stopping_criteria.max_length is None:
+                raise ValueError("`max_length` needs to be a stopping_criteria for now.")
+
+            if generation_config.num_beams <= 1:
+                raise ValueError("`num_beams` needs to be greater than 1 for constrained generation.")
+
+            if generation_config.do_sample:
+                raise ValueError("`do_sample` needs to be false for constrained generation.")
+
+            if generation_config.num_beam_groups is not None and generation_config.num_beam_groups > 1:
+                raise ValueError("`num_beam_groups` not supported yet for constrained generation.")
+
+            final_constraints = []
+            if generation_config.constraints is not None:
+                final_constraints = generation_config.constraints
+
+            if generation_config.force_words_ids is not None:
+
+                def typeerror():
+                    raise ValueError(
+                        "`force_words_ids` has to either be a `List[List[List[int]]]` or `List[List[int]]`"
+                        f"of positive integers, but is {generation_config.force_words_ids}."
+                    )
+
+                if (
+                    not isinstance(generation_config.force_words_ids, list)
+                    or len(generation_config.force_words_ids) == 0
+                ):
+                    typeerror()
+
+                for word_ids in generation_config.force_words_ids:
+                    if isinstance(word_ids[0], list):
+                        if not isinstance(word_ids, list) or len(word_ids) == 0:
+                            typeerror()
+                        if any(not isinstance(token_ids, list) for token_ids in word_ids):
+                            typeerror()
+                        if any(
+                            any((not isinstance(token_id, int) or token_id < 0) for token_id in token_ids)
+                            for token_ids in word_ids
+                        ):
+                            typeerror()
+
+                        constraint = DisjunctiveConstraint(word_ids)
+                    else:
+                        if not isinstance(word_ids, list) or len(word_ids) == 0:
+                            typeerror()
+                        if any((not isinstance(token_id, int) or token_id < 0) for token_id in word_ids):
+                            typeerror()
+
+                        constraint = PhrasalConstraint(word_ids)
+                    final_constraints.append(constraint)
+
+            # 11. prepare beam search scorer
+            constrained_beam_scorer = ConstrainedBeamSearchScorer(
+                constraints=final_constraints,
+                batch_size=batch_size,
+                num_beams=generation_config.num_beams,
+                device=inputs_tensor.device,
+                length_penalty=generation_config.length_penalty,
+                do_early_stopping=generation_config.early_stopping,
+                num_beam_hyps_to_keep=generation_config.num_return_sequences,
+                max_length=generation_config.max_length,
+            )
+            # 12. interleave input_ids with `num_beams` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_beams,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+            # 13. run beam search
+            return self.constrained_beam_search(
+                input_ids,
+                constrained_beam_scorer=constrained_beam_scorer,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+    @torch.no_grad()
+    def contrastive_search(
+        self,
+        input_ids: torch.LongTensor,
+        top_k: Optional[int] = 1,
+        penalty_alpha: Optional[float] = 0,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        logits_warper: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: Optional[bool] = False,
+        streamer: Optional["BaseStreamer"] = None,
+        **model_kwargs,
+    ) -> Union[ContrastiveSearchOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **contrastive search** and can
+        be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        <Tip warning={true}>
+
+        In most cases, you do not need to call [`~generation.GenerationMixin.contrastive_search`] directly. Use
+        generate() instead. For an overview of generation strategies and code examples, check the [following
+        guide](../generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            top_k (`int`, *optional*, defaults to 1):
+                The size of the candidate set that is used to re-rank for contrastive search
+            penalty_alpha (`float`, *optional*, defaults to 0):
+                The degeneration penalty for contrastive search; activate when it is larger than 0
+            logits_processor (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            logits_warper (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
+                to warp the prediction score distribution of the language modeling head applied before multinomial
+                sampling at each generation step.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            pad_token_id (`int`, *optional*):
+                The id of the *padding* token.
+            eos_token_id (`Union[int, List[int]]`, *optional*):
+                The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more details.
+            output_hidden_states (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more details.
+            output_scores (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
+            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            streamer (`BaseStreamer`, *optional*):
+                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+            model_kwargs:
+                Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
+                If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`~generation.ContrastiveSearchDecoderOnlyOutput`], [`~generation.ContrastiveSearchEncoderDecoderOutput`]
+            or `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.ContrastiveSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.ContrastiveSearchEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+
+        Examples:
+        ```python
+        >>> from transformers import (
+        ...     AutoTokenizer,
+        ...     AutoModelForCausalLM,
+        ...     StoppingCriteriaList,
+        ...     MaxLengthCriteria,
+        ... )
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
+        >>> model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m")
+        >>> # set pad_token_id to eos_token_id because OPT does not have a PAD token
+        >>> model.config.pad_token_id = model.config.eos_token_id
+        >>> input_prompt = "DeepMind Company is"
+        >>> input_ids = tokenizer(input_prompt, return_tensors="pt")
+        >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=64)])
+        >>> outputs = model.contrastive_search(
+        ...     **input_ids, penalty_alpha=0.6, top_k=4, stopping_criteria=stopping_criteria
+        ... )
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['DeepMind Company is a company that focuses on the development and commercialization of artificial intelligence (AI). DeepMind’s mission is to help people understand and solve problems that are difficult to solve in the world today.\n\nIn this post, we talk about the benefits of deep learning in business and how it']
+        ```"""
+        # init values
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
+        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.generation_config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
+        )
+        return_dict_in_generate = (
+            return_dict_in_generate
+            if return_dict_in_generate is not None
+            else self.generation_config.return_dict_in_generate
+        )
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+        if return_dict_in_generate and self.config.is_encoder_decoder:
+            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+            encoder_hidden_states = (
+                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+            )
+
+        # keep track of which sequences are already finished
+        unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
+
+        this_peer_finished = False  # used by synced_gpus only
+        batch_size = input_ids.shape[0]
+
+        while True:
+            if synced_gpus:
+                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+                # The following logic allows an early break if all peers finished generating their sequence
+                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
+                # send 0.0 if we finished, 1.0 otherwise
+                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+                # did all peers finish? the reduced sum will be 0.0 then
+                if this_peer_finished_flag.item() == 0.0:
+                    break
+
+            # if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
+            # (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
+            if model_kwargs.get("past_key_values") is None:
+                # prepare inputs
+                model_kwargs["use_cache"] = True
+                model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+                # encode the given prefix and prepare model inputs; encoder-decoder model process the prefix and save
+                # the `encoder_outputs`
+                outputs = self(
+                    **model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
+                )
+
+                # last decoder hidden states will be used to compute the degeneration penalty (cosine similarity with
+                # previous tokens)
+                if self.config.is_encoder_decoder:
+                    last_hidden_states = outputs.decoder_hidden_states[-1]
+                else:
+                    last_hidden_states = outputs.hidden_states[-1]
+                # next logit for contrastive search to select top-k candidate tokens
+                logit_for_next_step = outputs.logits[:, -1, :]
+
+                model_kwargs = self._update_model_kwargs_for_generation(
+                    outputs,
+                    model_kwargs,
+                    is_encoder_decoder=self.config.is_encoder_decoder,
+                    standardize_cache_format=True,
+                )
+
+                # Expands model inputs top_k times, for batched forward passes (akin to beam search).
+                _, model_kwargs = self._expand_inputs_for_generation(
+                    expand_size=top_k, is_encoder_decoder=self.config.is_encoder_decoder, **model_kwargs
+                )
+
+                past_key_values = model_kwargs.get("past_key_values")
+                if past_key_values is None:
+                    raise ValueError(
+                        f"{self.__class__.__name__} does not support caching and therefore **can't** be used "
+                        "for contrastive search."
+                    )
+                elif (
+                    not isinstance(past_key_values[0], (tuple, torch.Tensor))
+                    or past_key_values[0][0].shape[0] != batch_size
+                ):
+                    raise ValueError(
+                        f"{self.__class__.__name__} does not have a standard cache format and therefore **can't** be "
+                        "used for contrastive search without further modifications."
+                    )
+
+            # contrastive_search main logic start:
+            # contrastive search decoding consists of two steps: (1) candidate tokens recall; (2) candidate re-rank by
+            # degeneration penalty
+
+            logit_for_next_step = logits_processor(input_ids, logit_for_next_step)
+            logit_for_next_step = logits_warper(input_ids, logit_for_next_step)
+            next_probs = nn.functional.softmax(logit_for_next_step, dim=-1)
+            top_k_probs, top_k_ids = torch.topk(next_probs, dim=-1, k=top_k)
+
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (logit_for_next_step,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            # Replicates the new past_key_values to match the `top_k` candidates
+            new_key_values = []
+            for layer in model_kwargs["past_key_values"]:
+                items = []
+                # item is either the key or the value matrix
+                for item in layer:
+                    items.append(item.repeat_interleave(top_k, dim=0))
+                new_key_values.append(items)
+            model_kwargs["past_key_values"] = new_key_values
+
+            # compute the candidate tokens by the language model and collects their hidden_states
+            next_model_inputs = self.prepare_inputs_for_generation(top_k_ids.view(-1, 1), **model_kwargs)
+            outputs = self(
+                **next_model_inputs, return_dict=True, output_hidden_states=True, output_attentions=output_attentions
+            )
+            next_past_key_values = self._extract_past_from_model_output(outputs, standardize_cache_format=True)
+
+            logits = outputs.logits[:, -1, :]
+            # name is different for encoder-decoder and decoder-only models
+            if self.config.is_encoder_decoder:
+                next_hidden = outputs.decoder_hidden_states[-1]
+                full_hidden_states = outputs.decoder_hidden_states
+            else:
+                next_hidden = outputs.hidden_states[-1]
+                full_hidden_states = outputs.hidden_states
+            context_hidden = last_hidden_states.repeat_interleave(top_k, dim=0)
+
+            # compute the degeneration penalty and re-rank the candidates based on the degeneration penalty and the
+            # model confidence
+            selected_idx = _ranking_fast(context_hidden, next_hidden, top_k_probs, penalty_alpha, top_k)
+
+            # prepare for the next step: (1) next token_id; (2) past_key_values; (3) last_hidden_states for computing
+            # the degeneration penalty; (4) logits for selecting next top-k candidates; (5) selected tokens scores
+            # (model confidence minus degeneration penalty); (6) decoder hidden_states
+            next_tokens = top_k_ids[range(len(top_k_ids)), selected_idx]
+            next_hidden = torch.stack(torch.split(next_hidden.squeeze(dim=1), top_k))
+            next_hidden = next_hidden[range(batch_size), selected_idx, :]
+            last_hidden_states = torch.cat([last_hidden_states, next_hidden.unsqueeze(1)], dim=1)
+
+            next_decoder_hidden_states = ()
+            for layer in full_hidden_states:
+                layer = torch.stack(torch.split(layer, top_k))[range(batch_size), selected_idx, :]
+                next_decoder_hidden_states += (layer,)
+
+            # select the past_key_value
+            new_key_values = ()
+            for layer in next_past_key_values:
+                items = ()
+                # item is either the key or the value matrix
+                for item in layer:
+                    item = torch.stack(torch.split(item, top_k, dim=0))  # [B, K, num_head, seq_len, esz]
+                    item = item[range(batch_size), selected_idx, ...]  # [B, num_head, seq_len, esz]
+                    items += (item,)
+                new_key_values += (items,)
+            next_past_key_values = new_key_values
+
+            logit_for_next_step = torch.stack(torch.split(logits, top_k))[range(batch_size), selected_idx, :]
+
+            # Rebuilds the relevant parts of the model output for the selected token, for use in the next iteration
+            if self.config.is_encoder_decoder:
+                next_step_cross_attentions = ()
+                next_step_decoder_attentions = ()
+                if output_attentions:
+                    for layer in outputs.cross_attentions:
+                        layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
+                        next_step_cross_attentions += (layer,)
+                    for layer in outputs.decoder_attentions:
+                        layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
+                        next_step_decoder_attentions += (layer,)
+                outputs = Seq2SeqLMOutput(
+                    past_key_values=next_past_key_values,
+                    decoder_hidden_states=next_decoder_hidden_states,
+                    decoder_attentions=next_step_decoder_attentions or None,
+                    cross_attentions=next_step_cross_attentions or None,
+                )
+            else:
+                next_step_attentions = ()
+                if output_attentions:
+                    for layer in outputs.attentions:
+                        layer = torch.stack(torch.split(layer, top_k, dim=0))[range(batch_size), selected_idx, ...]
+                        next_step_attentions += (layer,)
+                outputs = CausalLMOutputWithPast(
+                    past_key_values=next_past_key_values,
+                    hidden_states=next_decoder_hidden_states,
+                    attentions=next_step_attentions or None,
+                )
+            # contrastive_search main logic end
+
+            if synced_gpus and this_peer_finished:
+                continue  # don't waste resources running the code we don't need
+
+            # finished sentences should have their next token be a padding token
+            if eos_token_id is not None:
+                if pad_token_id is None:
+                    raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
+                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
+
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+            if streamer is not None:
+                streamer.put(next_tokens.cpu())
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+
+            # if eos_token was found in one sentence, set sentence to finished
+            if eos_token_id_tensor is not None:
+                unfinished_sequences = unfinished_sequences.mul(
+                    next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
+                )
+
+            # stop when each sentence is finished, or if we exceed the maximum length
+            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+                if not synced_gpus:
+                    break
+                else:
+                    this_peer_finished = True
+
+        if streamer is not None:
+            streamer.end()
+
+        if return_dict_in_generate:
+            if self.config.is_encoder_decoder:
+                return ContrastiveSearchEncoderDecoderOutput(
+                    sequences=input_ids,
+                    scores=scores,
+                    encoder_attentions=encoder_attentions,
+                    encoder_hidden_states=encoder_hidden_states,
+                    decoder_attentions=decoder_attentions,
+                    cross_attentions=cross_attentions,
+                    decoder_hidden_states=decoder_hidden_states,
+                )
+            else:
+                return ContrastiveSearchDecoderOnlyOutput(
+                    sequences=input_ids,
+                    scores=scores,
+                    attentions=decoder_attentions,
+                    hidden_states=decoder_hidden_states,
+                )
+        else:
+            return input_ids
+
+    def greedy_search(
+        self,
+        input_ids: torch.LongTensor,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: Optional[bool] = False,
+        streamer: Optional["BaseStreamer"] = None,
+        **model_kwargs,
+    ) -> Union[GreedySearchOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **greedy decoding** and can be
+        used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        <Tip warning={true}>
+
+        In most cases, you do not need to call [`~generation.GenerationMixin.greedy_search`] directly. Use generate()
+        instead. For an overview of generation strategies and code examples, check the [following
+        guide](../generation_strategies).
+
+        </Tip>
+
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+
+            max_length (`int`, *optional*, defaults to 20):
+                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
+                tokens. The maximum length of the sequence to be generated.
+            pad_token_id (`int`, *optional*):
+                The id of the *padding* token.
+            eos_token_id (`Union[int, List[int]]`, *optional*):
+                The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more details.
+            output_hidden_states (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more details.
+            output_scores (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
+            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            streamer (`BaseStreamer`, *optional*):
+                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+            model_kwargs:
+                Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
+                If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`~generation.GreedySearchDecoderOnlyOutput`], [`~generation.GreedySearchEncoderDecoderOutput`] or
+            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.GreedySearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.GreedySearchEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+
+        Examples:
+
+        ```python
+        >>> from transformers import (
+        ...     AutoTokenizer,
+        ...     AutoModelForCausalLM,
+        ...     LogitsProcessorList,
+        ...     MinLengthLogitsProcessor,
+        ...     StoppingCriteriaList,
+        ...     MaxLengthCriteria,
+        ... )
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+        >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+
+        >>> # set pad_token_id to eos_token_id because GPT2 does not have a PAD token
+        >>> model.generation_config.pad_token_id = model.generation_config.eos_token_id
+
+        >>> input_prompt = "It might be possible to"
+        >>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
+
+        >>> # instantiate logits processors
+        >>> logits_processor = LogitsProcessorList(
+        ...     [
+        ...         MinLengthLogitsProcessor(10, eos_token_id=model.generation_config.eos_token_id),
+        ...     ]
+        ... )
+        >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
+
+        >>> outputs = model.greedy_search(
+        ...     input_ids, logits_processor=logits_processor, stopping_criteria=stopping_criteria
+        ... )
+
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ["It might be possible to get a better understanding of the nature of the problem, but it's not"]
+        ```"""
+        # init values
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use"
+                " `stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)])` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
+        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
+        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.generation_config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
+        )
+        return_dict_in_generate = (
+            return_dict_in_generate
+            if return_dict_in_generate is not None
+            else self.generation_config.return_dict_in_generate
+        )
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+        if return_dict_in_generate and self.config.is_encoder_decoder:
+            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+            encoder_hidden_states = (
+                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+            )
+
+        # keep track of which sequences are already finished
+        unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
+
+        this_peer_finished = False  # used by synced_gpus only
+        while True:
+            if synced_gpus:
+                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+                # The following logic allows an early break if all peers finished generating their sequence
+                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
+                # send 0.0 if we finished, 1.0 otherwise
+                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+                # did all peers finish? the reduced sum will be 0.0 then
+                if this_peer_finished_flag.item() == 0.0:
+                    break
+
+            # prepare model inputs
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+            # forward pass to get next token
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            if synced_gpus and this_peer_finished:
+                continue  # don't waste resources running the code we don't need
+
+            next_token_logits = outputs.logits[:, -1, :]
+
+            # pre-process distribution
+            next_tokens_scores = logits_processor(input_ids, next_token_logits)
+
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (next_tokens_scores,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            # argmax
+            next_tokens = torch.argmax(next_tokens_scores, dim=-1)
+
+            # finished sentences should have their next token be a padding token
+            if eos_token_id is not None:
+                if pad_token_id is None:
+                    raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
+                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
+
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+            if streamer is not None:
+                streamer.put(next_tokens.cpu())
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+
+            # if eos_token was found in one sentence, set sentence to finished
+            if eos_token_id_tensor is not None:
+                unfinished_sequences = unfinished_sequences.mul(
+                    next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
+                )
+
+            # stop when each sentence is finished, or if we exceed the maximum length
+            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+                if not synced_gpus:
+                    break
+                else:
+                    this_peer_finished = True
+
+        if streamer is not None:
+            streamer.end()
+
+        if return_dict_in_generate:
+            if self.config.is_encoder_decoder:
+                return GreedySearchEncoderDecoderOutput(
+                    sequences=input_ids,
+                    scores=scores,
+                    encoder_attentions=encoder_attentions,
+                    encoder_hidden_states=encoder_hidden_states,
+                    decoder_attentions=decoder_attentions,
+                    cross_attentions=cross_attentions,
+                    decoder_hidden_states=decoder_hidden_states,
+                )
+            else:
+                return GreedySearchDecoderOnlyOutput(
+                    sequences=input_ids,
+                    scores=scores,
+                    attentions=decoder_attentions,
+                    hidden_states=decoder_hidden_states,
+                )
+        else:
+            return input_ids
+
+    def sample(
+        self,
+        input_ids: torch.LongTensor,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        logits_warper: Optional[LogitsProcessorList] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: Optional[bool] = False,
+        streamer: Optional["BaseStreamer"] = None,
+        **model_kwargs,
+    ) -> Union[SampleOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **multinomial sampling** and
+        can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        <Tip warning={true}>
+
+        In most cases, you do not need to call [`~generation.GenerationMixin.sample`] directly. Use generate() instead.
+        For an overview of generation strategies and code examples, check the [following
+        guide](../generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            logits_warper (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
+                to warp the prediction score distribution of the language modeling head applied before multinomial
+                sampling at each generation step.
+            max_length (`int`, *optional*, defaults to 20):
+                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
+                tokens. The maximum length of the sequence to be generated.
+            pad_token_id (`int`, *optional*):
+                The id of the *padding* token.
+            eos_token_id (`Union[int, List[int]]`, *optional*):
+                The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more details.
+            output_hidden_states (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more details.
+            output_scores (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
+            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            streamer (`BaseStreamer`, *optional*):
+                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+            model_kwargs:
+                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
+                an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`~generation.SampleDecoderOnlyOutput`], [`~generation.SampleEncoderDecoderOutput`] or `torch.LongTensor`:
+            A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.SampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.SampleEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+
+        Examples:
+
+        ```python
+        >>> from transformers import (
+        ...     AutoTokenizer,
+        ...     AutoModelForCausalLM,
+        ...     LogitsProcessorList,
+        ...     MinLengthLogitsProcessor,
+        ...     TopKLogitsWarper,
+        ...     TemperatureLogitsWarper,
+        ...     StoppingCriteriaList,
+        ...     MaxLengthCriteria,
+        ... )
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+        >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+
+        >>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
+        >>> model.config.pad_token_id = model.config.eos_token_id
+        >>> model.generation_config.pad_token_id = model.config.eos_token_id
+
+        >>> input_prompt = "Today is a beautiful day, and"
+        >>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
+
+        >>> # instantiate logits processors
+        >>> logits_processor = LogitsProcessorList(
+        ...     [
+        ...         MinLengthLogitsProcessor(15, eos_token_id=model.generation_config.eos_token_id),
+        ...     ]
+        ... )
+        >>> # instantiate logits processors
+        >>> logits_warper = LogitsProcessorList(
+        ...     [
+        ...         TopKLogitsWarper(50),
+        ...         TemperatureLogitsWarper(0.7),
+        ...     ]
+        ... )
+
+        >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
+
+        >>> torch.manual_seed(0)  # doctest: +IGNORE_RESULT
+        >>> outputs = model.sample(
+        ...     input_ids,
+        ...     logits_processor=logits_processor,
+        ...     logits_warper=logits_warper,
+        ...     stopping_criteria=stopping_criteria,
+        ... )
+
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['Today is a beautiful day, and we must do everything possible to make it a day of celebration.']
+        ```"""
+        # init values
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use"
+                " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
+        logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
+        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
+        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.generation_config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
+        )
+        return_dict_in_generate = (
+            return_dict_in_generate
+            if return_dict_in_generate is not None
+            else self.generation_config.return_dict_in_generate
+        )
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+        if return_dict_in_generate and self.config.is_encoder_decoder:
+            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+            encoder_hidden_states = (
+                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+            )
+
+        # keep track of which sequences are already finished
+        unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
+
+        this_peer_finished = False  # used by synced_gpus only
+        # auto-regressive generation
+        while True:
+            if synced_gpus:
+                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+                # The following logic allows an early break if all peers finished generating their sequence
+                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
+                # send 0.0 if we finished, 1.0 otherwise
+                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+                # did all peers finish? the reduced sum will be 0.0 then
+                if this_peer_finished_flag.item() == 0.0:
+                    break
+
+            # prepare model inputs
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+            # forward pass to get next token
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            if synced_gpus and this_peer_finished:
+                continue  # don't waste resources running the code we don't need
+
+            next_token_logits = outputs.logits[:, -1, :]
+
+            # pre-process distribution
+            next_token_scores = logits_processor(input_ids, next_token_logits)
+            next_token_scores = logits_warper(input_ids, next_token_scores)
+
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (next_token_scores,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            # sample
+            probs = nn.functional.softmax(next_token_scores, dim=-1)
+            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+
+            # finished sentences should have their next token be a padding token
+            if eos_token_id is not None:
+                if pad_token_id is None:
+                    raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
+                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
+
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+            if streamer is not None:
+                streamer.put(next_tokens.cpu())
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+
+            # if eos_token was found in one sentence, set sentence to finished
+            if eos_token_id_tensor is not None:
+                unfinished_sequences = unfinished_sequences.mul(
+                    next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
+                )
+
+            # stop when each sentence is finished, or if we exceed the maximum length
+            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+                if not synced_gpus:
+                    break
+                else:
+                    this_peer_finished = True
+
+        if streamer is not None:
+            streamer.end()
+
+        if return_dict_in_generate:
+            if self.config.is_encoder_decoder:
+                return SampleEncoderDecoderOutput(
+                    sequences=input_ids,
+                    scores=scores,
+                    encoder_attentions=encoder_attentions,
+                    encoder_hidden_states=encoder_hidden_states,
+                    decoder_attentions=decoder_attentions,
+                    cross_attentions=cross_attentions,
+                    decoder_hidden_states=decoder_hidden_states,
+                )
+            else:
+                return SampleDecoderOnlyOutput(
+                    sequences=input_ids,
+                    scores=scores,
+                    attentions=decoder_attentions,
+                    hidden_states=decoder_hidden_states,
+                )
+        else:
+            return input_ids
+
+    def beam_search(
+        self,
+        input_ids: torch.LongTensor,
+        beam_scorer: BeamScorer,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: Optional[bool] = False,
+        **model_kwargs,
+    ) -> Union[BeamSearchOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **beam search decoding** and
+        can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        <Tip warning={true}>
+
+        In most cases, you do not need to call [`~generation.GenerationMixin.beam_search`] directly. Use generate()
+        instead. For an overview of generation strategies and code examples, check the [following
+        guide](../generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            beam_scorer (`BeamScorer`):
+                An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
+                sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            max_length (`int`, *optional*, defaults to 20):
+                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
+                tokens. The maximum length of the sequence to be generated.
+            pad_token_id (`int`, *optional*):
+                The id of the *padding* token.
+            eos_token_id (`Union[int, List[int]]`, *optional*):
+                The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more details.
+            output_hidden_states (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more details.
+            output_scores (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
+            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            model_kwargs:
+                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
+                an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`generation.BeamSearchDecoderOnlyOutput`], [`~generation.BeamSearchEncoderDecoderOutput`] or
+            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.BeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.BeamSearchEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+
+
+        Examples:
+
+        ```python
+        >>> from transformers import (
+        ...     AutoTokenizer,
+        ...     AutoModelForSeq2SeqLM,
+        ...     LogitsProcessorList,
+        ...     MinLengthLogitsProcessor,
+        ...     BeamSearchScorer,
+        ... )
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
+        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+        >>> encoder_input_str = "translate English to German: How old are you?"
+        >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
+
+
+        >>> # lets run beam search using 3 beams
+        >>> num_beams = 3
+        >>> # define decoder start token ids
+        >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
+        >>> input_ids = input_ids * model.config.decoder_start_token_id
+
+        >>> # add encoder_outputs to model keyword arguments
+        >>> model_kwargs = {
+        ...     "encoder_outputs": model.get_encoder()(
+        ...         encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
+        ...     )
+        ... }
+
+        >>> # instantiate beam scorer
+        >>> beam_scorer = BeamSearchScorer(
+        ...     batch_size=1,
+        ...     num_beams=num_beams,
+        ...     device=model.device,
+        ... )
+
+        >>> # instantiate logits processors
+        >>> logits_processor = LogitsProcessorList(
+        ...     [
+        ...         MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
+        ...     ]
+        ... )
+
+        >>> outputs = model.beam_search(input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs)
+
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['Wie alt bist du?']
+        ```"""
+        # init values
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use"
+                " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
+        if len(stopping_criteria) == 0:
+            warnings.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
+        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.generation_config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
+        )
+        return_dict_in_generate = (
+            return_dict_in_generate
+            if return_dict_in_generate is not None
+            else self.generation_config.return_dict_in_generate
+        )
+
+        batch_size = len(beam_scorer._beam_hyps)
+        num_beams = beam_scorer.num_beams
+
+        batch_beam_size, cur_len = input_ids.shape
+
+        if num_beams * batch_size != batch_beam_size:
+            raise ValueError(
+                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+            )
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        beam_indices = (
+            tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
+        )
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+        if return_dict_in_generate and self.config.is_encoder_decoder:
+            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+            encoder_hidden_states = (
+                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+            )
+
+        # initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
+        # of the first beam are considered to avoid sampling the exact same tokens across all beams.
+        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
+        beam_scores[:, 1:] = -1e9
+        beam_scores = beam_scores.view((batch_size * num_beams,))
+
+        this_peer_finished = False  # used by synced_gpus only
+        while True:
+            if synced_gpus:
+                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+                # The following logic allows an early break if all peers finished generating their sequence
+                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
+                # send 0.0 if we finished, 1.0 otherwise
+                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+                # did all peers finish? the reduced sum will be 0.0 then
+                if this_peer_finished_flag.item() == 0.0:
+                    break
+
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            if synced_gpus and this_peer_finished:
+                cur_len = cur_len + 1
+                continue  # don't waste resources running the code we don't need
+
+            next_token_logits = outputs.logits[:, -1, :]
+            # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
+            # cannot be generated both before and after the `nn.functional.log_softmax` operation.
+            next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
+            next_token_scores = nn.functional.log_softmax(
+                next_token_logits, dim=-1
+            )  # (batch_size * num_beams, vocab_size)
+
+            next_token_scores_processed = logits_processor(input_ids, next_token_scores)
+            next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)
+
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (next_token_scores_processed,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            # reshape for beam search
+            vocab_size = next_token_scores.shape[-1]
+            next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
+
+            # Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search)
+            next_token_scores, next_tokens = torch.topk(
+                next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
+            )
+
+            next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
+            next_tokens = next_tokens % vocab_size
+
+            # stateless
+            beam_outputs = beam_scorer.process(
+                input_ids,
+                next_token_scores,
+                next_tokens,
+                next_indices,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+                beam_indices=beam_indices,
+            )
+
+            beam_scores = beam_outputs["next_beam_scores"]
+            beam_next_tokens = beam_outputs["next_beam_tokens"]
+            beam_idx = beam_outputs["next_beam_indices"]
+
+            input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
+
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+            if model_kwargs["past_key_values"] is not None:
+                model_kwargs["past_key_values"] = self._reorder_cache(model_kwargs["past_key_values"], beam_idx)
+
+            if return_dict_in_generate and output_scores:
+                beam_indices = tuple((beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices))))
+
+            # increase cur_len
+            cur_len = cur_len + 1
+
+            if beam_scorer.is_done or stopping_criteria(input_ids, scores):
+                if not synced_gpus:
+                    break
+                else:
+                    this_peer_finished = True
+
+        sequence_outputs = beam_scorer.finalize(
+            input_ids,
+            beam_scores,
+            next_tokens,
+            next_indices,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            max_length=stopping_criteria.max_length,
+            beam_indices=beam_indices,
+        )
+
+        if return_dict_in_generate:
+            if not output_scores:
+                sequence_outputs["sequence_scores"] = None
+
+            if self.config.is_encoder_decoder:
+                return BeamSearchEncoderDecoderOutput(
+                    sequences=sequence_outputs["sequences"],
+                    sequences_scores=sequence_outputs["sequence_scores"],
+                    scores=scores,
+                    beam_indices=sequence_outputs["beam_indices"],
+                    encoder_attentions=encoder_attentions,
+                    encoder_hidden_states=encoder_hidden_states,
+                    decoder_attentions=decoder_attentions,
+                    cross_attentions=cross_attentions,
+                    decoder_hidden_states=decoder_hidden_states,
+                )
+            else:
+                return BeamSearchDecoderOnlyOutput(
+                    sequences=sequence_outputs["sequences"],
+                    sequences_scores=sequence_outputs["sequence_scores"],
+                    scores=scores,
+                    beam_indices=sequence_outputs["beam_indices"],
+                    attentions=decoder_attentions,
+                    hidden_states=decoder_hidden_states,
+                )
+        else:
+            return sequence_outputs["sequences"]
+
+    def beam_sample(
+        self,
+        input_ids: torch.LongTensor,
+        beam_scorer: BeamScorer,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        logits_warper: Optional[LogitsProcessorList] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: Optional[bool] = False,
+        **model_kwargs,
+    ) -> Union[BeamSampleOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **beam search multinomial
+        sampling** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        <Tip warning={true}>
+
+        In most cases, you do not need to call [`~generation.GenerationMixin.beam_sample`] directly. Use generate()
+        instead. For an overview of generation strategies and code examples, check the [following
+        guide](../generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            beam_scorer (`BeamScorer`):
+                A derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
+                sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            logits_warper (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
+                to warp the prediction score distribution of the language modeling head applied before multinomial
+                sampling at each generation step.
+            max_length (`int`, *optional*, defaults to 20):
+                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
+                tokens. The maximum length of the sequence to be generated.
+            pad_token_id (`int`, *optional*):
+                The id of the *padding* token.
+            eos_token_id (`Union[int, List[int]]`, *optional*):
+                The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more details.
+            output_hidden_states (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more details.
+            output_scores (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
+            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            model_kwargs:
+                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
+                an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`~generation.BeamSampleDecoderOnlyOutput`], [`~generation.BeamSampleEncoderDecoderOutput`] or
+            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.BeamSampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.BeamSampleEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+
+        Examples:
+
+        ```python
+        >>> from transformers import (
+        ...     AutoTokenizer,
+        ...     AutoModelForSeq2SeqLM,
+        ...     LogitsProcessorList,
+        ...     MinLengthLogitsProcessor,
+        ...     TopKLogitsWarper,
+        ...     TemperatureLogitsWarper,
+        ...     BeamSearchScorer,
+        ... )
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
+        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+        >>> encoder_input_str = "translate English to German: How old are you?"
+        >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
+
+        >>> # lets run beam search using 3 beams
+        >>> num_beams = 3
+        >>> # define decoder start token ids
+        >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
+        >>> input_ids = input_ids * model.config.decoder_start_token_id
+
+        >>> # add encoder_outputs to model keyword arguments
+        >>> model_kwargs = {
+        ...     "encoder_outputs": model.get_encoder()(
+        ...         encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
+        ...     )
+        ... }
+
+        >>> # instantiate beam scorer
+        >>> beam_scorer = BeamSearchScorer(
+        ...     batch_size=1,
+        ...     max_length=model.config.max_length,
+        ...     num_beams=num_beams,
+        ...     device=model.device,
+        ... )
+
+        >>> # instantiate logits processors
+        >>> logits_processor = LogitsProcessorList(
+        ...     [MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id)]
+        ... )
+        >>> # instantiate logits processors
+        >>> logits_warper = LogitsProcessorList(
+        ...     [
+        ...         TopKLogitsWarper(50),
+        ...         TemperatureLogitsWarper(0.7),
+        ...     ]
+        ... )
+
+        >>> outputs = model.beam_sample(
+        ...     input_ids, beam_scorer, logits_processor=logits_processor, logits_warper=logits_warper, **model_kwargs
+        ... )
+
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['Wie alt bist du?']
+        ```"""
+        # init values
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use"
+                " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
+        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.generation_config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
+        )
+        return_dict_in_generate = (
+            return_dict_in_generate
+            if return_dict_in_generate is not None
+            else self.generation_config.return_dict_in_generate
+        )
+
+        batch_size = len(beam_scorer._beam_hyps)
+        num_beams = beam_scorer.num_beams
+
+        batch_beam_size, cur_len = input_ids.shape
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        beam_indices = (
+            tuple(() for _ in range(batch_beam_size)) if (return_dict_in_generate and output_scores) else None
+        )
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+        if return_dict_in_generate and self.config.is_encoder_decoder:
+            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+            encoder_hidden_states = (
+                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+            )
+
+        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
+        beam_scores = beam_scores.view((batch_size * num_beams,))
+
+        this_peer_finished = False  # used by synced_gpus only
+        while True:
+            if synced_gpus:
+                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+                # The following logic allows an early break if all peers finished generating their sequence
+                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
+                # send 0.0 if we finished, 1.0 otherwise
+                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+                # did all peers finish? the reduced sum will be 0.0 then
+                if this_peer_finished_flag.item() == 0.0:
+                    break
+
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            if synced_gpus and this_peer_finished:
+                cur_len = cur_len + 1
+                continue  # don't waste resources running the code we don't need
+
+            next_token_logits = outputs.logits[:, -1, :]
+
+            # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
+            # cannot be generated both before and after the `nn.functional.log_softmax` operation.
+            next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
+            next_token_scores = nn.functional.log_softmax(
+                next_token_logits, dim=-1
+            )  # (batch_size * num_beams, vocab_size)
+
+            next_token_scores_processed = logits_processor(input_ids, next_token_scores)
+            next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)
+            # Note: logits warpers are intentionally applied after adding running beam scores. On some logits warpers
+            # (like top_p) this is indiferent, but on others (like temperature) it is not. For reference, see
+            # https://github.com/huggingface/transformers/pull/5420#discussion_r449779867
+            next_token_scores = logits_warper(input_ids, next_token_scores)
+
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (logits_warper(input_ids, next_token_scores_processed),)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            # reshape for beam search
+            vocab_size = next_token_scores.shape[-1]
+            next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
+
+            probs = nn.functional.softmax(next_token_scores, dim=-1)
+
+            next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)
+            next_token_scores = torch.gather(next_token_scores, -1, next_tokens)
+
+            next_token_scores, _indices = torch.sort(next_token_scores, descending=True, dim=1)
+            next_tokens = torch.gather(next_tokens, -1, _indices)
+
+            next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
+            next_tokens = next_tokens % vocab_size
+
+            # stateless
+            beam_outputs = beam_scorer.process(
+                input_ids,
+                next_token_scores,
+                next_tokens,
+                next_indices,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+                beam_indices=beam_indices,
+            )
+            beam_scores = beam_outputs["next_beam_scores"]
+            beam_next_tokens = beam_outputs["next_beam_tokens"]
+            beam_idx = beam_outputs["next_beam_indices"]
+
+            input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
+
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+            if model_kwargs["past_key_values"] is not None:
+                model_kwargs["past_key_values"] = self._reorder_cache(model_kwargs["past_key_values"], beam_idx)
+
+            if return_dict_in_generate and output_scores:
+                beam_indices = tuple((beam_indices[beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices))))
+
+            # increase cur_len
+            cur_len = cur_len + 1
+
+            if beam_scorer.is_done or stopping_criteria(input_ids, scores):
+                if not synced_gpus:
+                    break
+                else:
+                    this_peer_finished = True
+
+        sequence_outputs = beam_scorer.finalize(
+            input_ids,
+            beam_scores,
+            next_tokens,
+            next_indices,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            max_length=stopping_criteria.max_length,
+            beam_indices=beam_indices,
+        )
+
+        if return_dict_in_generate:
+            if not output_scores:
+                sequence_outputs["sequence_scores"] = None
+
+            if self.config.is_encoder_decoder:
+                return BeamSampleEncoderDecoderOutput(
+                    sequences=sequence_outputs["sequences"],
+                    sequences_scores=sequence_outputs["sequence_scores"],
+                    scores=scores,
+                    beam_indices=sequence_outputs["beam_indices"],
+                    encoder_attentions=encoder_attentions,
+                    encoder_hidden_states=encoder_hidden_states,
+                    decoder_attentions=decoder_attentions,
+                    cross_attentions=cross_attentions,
+                    decoder_hidden_states=decoder_hidden_states,
+                )
+            else:
+                return BeamSampleDecoderOnlyOutput(
+                    sequences=sequence_outputs["sequences"],
+                    sequences_scores=sequence_outputs["sequence_scores"],
+                    scores=scores,
+                    beam_indices=sequence_outputs["beam_indices"],
+                    attentions=decoder_attentions,
+                    hidden_states=decoder_hidden_states,
+                )
+        else:
+            return sequence_outputs["sequences"]
+
+    def group_beam_search(
+        self,
+        input_ids: torch.LongTensor,
+        beam_scorer: BeamScorer,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: Optional[bool] = False,
+        **model_kwargs,
+    ):
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **diverse beam search
+        decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        <Tip warning={true}>
+
+        In most cases, you do not need to call [`~generation.GenerationMixin.group_beam_search`] directly. Use
+        generate() instead. For an overview of generation strategies and code examples, check the [following
+        guide](../generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            beam_scorer (`BeamScorer`):
+                An derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
+                sorted during generation. For more information, the documentation of [`BeamScorer`] should be read.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            max_length (`int`, *optional*, defaults to 20):
+                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
+                tokens. The maximum length of the sequence to be generated.
+            pad_token_id (`int`, *optional*):
+                The id of the *padding* token.
+            eos_token_id (`Union[int, List[int]]`, *optional*):
+                The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more details.
+            output_hidden_states (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more details.
+            output_scores (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
+            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+
+            model_kwargs:
+                Additional model specific kwargs that will be forwarded to the `forward` function of the model. If
+                model is an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`~generation.BeamSearchDecoderOnlyOutput`], [`~generation.BeamSearchEncoderDecoderOutput`] or
+            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.BeamSearchDecoderOnlyOutput`] if [`~generation.BeamSearchDecoderOnlyOutput`] if
+            `model.config.is_encoder_decoder=False` and `return_dict_in_generate=True` or a
+            [`~generation.BeamSearchEncoderDecoderOutput`] if `model.config.is_encoder_decoder=True`.
+
+        Examples:
+
+        ```python
+        >>> from transformers import (
+        ...     AutoTokenizer,
+        ...     AutoModelForSeq2SeqLM,
+        ...     LogitsProcessorList,
+        ...     MinLengthLogitsProcessor,
+        ...     HammingDiversityLogitsProcessor,
+        ...     BeamSearchScorer,
+        ... )
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
+        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+        >>> encoder_input_str = "translate English to German: How old are you?"
+        >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
+
+
+        >>> # lets run diverse beam search using 6 beams
+        >>> num_beams = 6
+        >>> # define decoder start token ids
+        >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
+        >>> input_ids = input_ids * model.config.decoder_start_token_id
+
+        >>> # add encoder_outputs to model keyword arguments
+        >>> model_kwargs = {
+        ...     "encoder_outputs": model.get_encoder()(
+        ...         encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
+        ...     )
+        ... }
+
+        >>> # instantiate beam scorer
+        >>> beam_scorer = BeamSearchScorer(
+        ...     batch_size=1,
+        ...     max_length=model.config.max_length,
+        ...     num_beams=num_beams,
+        ...     device=model.device,
+        ...     num_beam_groups=3,
+        ... )
+
+        >>> # instantiate logits processors
+        >>> logits_processor = LogitsProcessorList(
+        ...     [
+        ...         HammingDiversityLogitsProcessor(5.5, num_beams=6, num_beam_groups=3),
+        ...         MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
+        ...     ]
+        ... )
+
+        >>> outputs = model.group_beam_search(
+        ...     input_ids, beam_scorer, logits_processor=logits_processor, **model_kwargs
+        ... )
+
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['Wie alt bist du?']
+        ```"""
+        # init values
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use"
+                " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
+        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.generation_config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
+        )
+        return_dict_in_generate = (
+            return_dict_in_generate
+            if return_dict_in_generate is not None
+            else self.generation_config.return_dict_in_generate
+        )
+
+        batch_size = len(beam_scorer._beam_hyps)
+        num_beams = beam_scorer.num_beams
+        num_beam_groups = beam_scorer.num_beam_groups
+        num_sub_beams = num_beams // num_beam_groups
+        device = input_ids.device
+
+        batch_beam_size, cur_len = input_ids.shape
+
+        if return_dict_in_generate and output_scores:
+            beam_indices = [tuple(() for _ in range(num_sub_beams * batch_size)) for _ in range(num_beam_groups)]
+        else:
+            beam_indices = None
+
+        if num_beams * batch_size != batch_beam_size:
+            raise ValueError(
+                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+            )
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+        if return_dict_in_generate and self.config.is_encoder_decoder:
+            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+            encoder_hidden_states = (
+                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+            )
+
+        # initialise score of first beam of each group with 0 and the rest with -1e9. This ensures that the beams in
+        # the same group don't produce same tokens everytime.
+        beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device)
+        beam_scores[:, ::num_sub_beams] = 0
+        beam_scores = beam_scores.view((batch_size * num_beams,))
+
+        this_peer_finished = False  # used by synced_gpus only
+        while True:
+            if synced_gpus:
+                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+                # The following logic allows an early break if all peers finished generating their sequence
+                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
+                # send 0.0 if we finished, 1.0 otherwise
+                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+                # did all peers finish? the reduced sum will be 0.0 then
+                if this_peer_finished_flag.item() == 0.0:
+                    break
+
+            # predicted tokens in cur_len step
+            current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
+
+            # indices which will form the beams in the next time step
+            reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device)
+
+            # do one decoder step on all beams of all sentences in batch
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            if synced_gpus and this_peer_finished:
+                cur_len = cur_len + 1
+                continue  # don't waste resources running the code we don't need
+
+            if output_scores:
+                processed_score = torch.zeros_like(outputs.logits[:, -1, :])
+
+            for beam_group_idx in range(num_beam_groups):
+                group_start_idx = beam_group_idx * num_sub_beams
+                group_end_idx = min(group_start_idx + num_sub_beams, num_beams)
+                group_size = group_end_idx - group_start_idx
+
+                # indices of beams of current group among all sentences in batch
+                batch_group_indices = []
+
+                for batch_idx in range(batch_size):
+                    batch_group_indices.extend(
+                        [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)]
+                    )
+                group_input_ids = input_ids[batch_group_indices]
+
+                # select outputs of beams of current group only
+                next_token_logits = outputs.logits[batch_group_indices, -1, :]
+
+                # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
+                # cannot be generated both before and after the `nn.functional.log_softmax` operation.
+                next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
+                next_token_scores = nn.functional.log_softmax(
+                    next_token_logits, dim=-1
+                )  # (batch_size * group_size, vocab_size)
+                vocab_size = next_token_scores.shape[-1]
+
+                next_token_scores_processed = logits_processor(
+                    group_input_ids, next_token_scores, current_tokens=current_tokens, beam_group_idx=beam_group_idx
+                )
+                next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1)
+                next_token_scores = next_token_scores.expand_as(next_token_scores_processed)
+
+                if output_scores:
+                    processed_score[batch_group_indices] = next_token_scores_processed
+
+                # reshape for beam search
+                next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size)
+
+                # Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search)
+                next_token_scores, next_tokens = torch.topk(
+                    next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True
+                )
+
+                next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor")
+                next_tokens = next_tokens % vocab_size
+
+                # stateless
+                process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
+                beam_outputs = beam_scorer.process(
+                    group_input_ids,
+                    next_token_scores,
+                    next_tokens,
+                    next_indices,
+                    pad_token_id=pad_token_id,
+                    eos_token_id=eos_token_id,
+                    beam_indices=process_beam_indices,
+                )
+                beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"]
+                beam_next_tokens = beam_outputs["next_beam_tokens"]
+                beam_idx = beam_outputs["next_beam_indices"]
+
+                if return_dict_in_generate and output_scores:
+                    beam_indices[beam_group_idx] = tuple(
+                        beam_indices[beam_group_idx][beam_idx[i]] + (beam_idx[i],) for i in range(len(beam_indices[0]))
+                    )
+
+                input_ids[batch_group_indices] = group_input_ids[beam_idx]
+                group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
+                current_tokens[batch_group_indices] = group_input_ids[:, -1]
+
+                # (beam_idx // group_size) -> batch_idx
+                # (beam_idx % group_size) -> offset of idx inside the group
+                reordering_indices[batch_group_indices] = (
+                    num_beams * torch.div(beam_idx, group_size, rounding_mode="floor")
+                    + group_start_idx
+                    + (beam_idx % group_size)
+                )
+
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (processed_score,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1)
+
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+            if model_kwargs["past_key_values"] is not None:
+                model_kwargs["past_key_values"] = self._reorder_cache(
+                    model_kwargs["past_key_values"], reordering_indices
+                )
+
+            # increase cur_len
+            cur_len = cur_len + 1
+
+            if beam_scorer.is_done or stopping_criteria(input_ids, scores):
+                if not synced_gpus:
+                    break
+                else:
+                    this_peer_finished = True
+
+        final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
+        sequence_outputs = beam_scorer.finalize(
+            input_ids,
+            beam_scores,
+            next_tokens,
+            next_indices,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            max_length=stopping_criteria.max_length,
+            beam_indices=final_beam_indices,
+        )
+
+        if return_dict_in_generate:
+            if not output_scores:
+                sequence_outputs["sequence_scores"] = None
+
+            if self.config.is_encoder_decoder:
+                return BeamSearchEncoderDecoderOutput(
+                    sequences=sequence_outputs["sequences"],
+                    sequences_scores=sequence_outputs["sequence_scores"],
+                    scores=scores,
+                    beam_indices=sequence_outputs["beam_indices"],
+                    encoder_attentions=encoder_attentions,
+                    encoder_hidden_states=encoder_hidden_states,
+                    decoder_attentions=decoder_attentions,
+                    cross_attentions=cross_attentions,
+                    decoder_hidden_states=decoder_hidden_states,
+                )
+            else:
+                return BeamSearchDecoderOnlyOutput(
+                    sequences=sequence_outputs["sequences"],
+                    sequences_scores=sequence_outputs["sequence_scores"],
+                    scores=scores,
+                    beam_indices=sequence_outputs["beam_indices"],
+                    attentions=decoder_attentions,
+                    hidden_states=decoder_hidden_states,
+                )
+        else:
+            return sequence_outputs["sequences"]
+
+    def constrained_beam_search(
+        self,
+        input_ids: torch.LongTensor,
+        constrained_beam_scorer: ConstrainedBeamSearchScorer,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: Optional[bool] = None,
+        **model_kwargs,
+    ) -> Union[BeamSearchOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **constrained beam search
+        decoding** and can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        <Tip warning={true}>
+
+        In most cases, you do not need to call [`~generation.GenerationMixin.constrained_beam_search`] directly. Use
+        generate() instead. For an overview of generation strategies and code examples, check the [following
+        guide](../generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            constrained_beam_scorer (`ConstrainedBeamSearchScorer`):
+                A derived instance of [`BeamScorer`] that defines how beam hypotheses are constructed, stored and
+                sorted during generation, while satisfying a list of positive constraints. For more information, the
+                documentation of [`ConstrainedBeamSearchScorer`] should be read.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            logits_warper (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
+                to warp the prediction score distribution of the language modeling head applied before multinomial
+                sampling at each generation step.
+            max_length (`int`, *optional*, defaults to 20):
+                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
+                tokens. The maximum length of the sequence to be generated.
+            pad_token_id (`int`, *optional*):
+                The id of the *padding* token.
+            eos_token_id (`Union[int, List[int]]`, *optional*):
+                The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more details.
+            output_hidden_states (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more details.
+            output_scores (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
+            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            model_kwargs:
+                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
+                an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`generation.BeamSearchDecoderOnlyOutput`], [`~generation.BeamSearchEncoderDecoderOutput`] or
+            `torch.LongTensor`: A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.BeamSearchDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.BeamSearchEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+
+
+        Examples:
+
+        ```python
+        >>> from transformers import (
+        ...     AutoTokenizer,
+        ...     AutoModelForSeq2SeqLM,
+        ...     LogitsProcessorList,
+        ...     MinLengthLogitsProcessor,
+        ...     ConstrainedBeamSearchScorer,
+        ...     PhrasalConstraint,
+        ... )
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("t5-base")
+        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+
+        >>> encoder_input_str = "translate English to German: How old are you?"
+        >>> encoder_input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids
+
+
+        >>> # lets run beam search using 3 beams
+        >>> num_beams = 3
+        >>> # define decoder start token ids
+        >>> input_ids = torch.ones((num_beams, 1), device=model.device, dtype=torch.long)
+        >>> input_ids = input_ids * model.config.decoder_start_token_id
+
+        >>> # add encoder_outputs to model keyword arguments
+        >>> model_kwargs = {
+        ...     "encoder_outputs": model.get_encoder()(
+        ...         encoder_input_ids.repeat_interleave(num_beams, dim=0), return_dict=True
+        ...     )
+        ... }
+
+        >>> constraint_str = "Sie"
+        >>> constraint_token_ids = tokenizer.encode(constraint_str)[:-1]  # slice to remove eos token
+        >>> constraints = [PhrasalConstraint(token_ids=constraint_token_ids)]
+
+
+        >>> # instantiate beam scorer
+        >>> beam_scorer = ConstrainedBeamSearchScorer(
+        ...     batch_size=1, num_beams=num_beams, device=model.device, constraints=constraints
+        ... )
+
+        >>> # instantiate logits processors
+        >>> logits_processor = LogitsProcessorList(
+        ...     [
+        ...         MinLengthLogitsProcessor(5, eos_token_id=model.config.eos_token_id),
+        ...     ]
+        ... )
+
+        >>> outputs = model.constrained_beam_search(
+        ...     input_ids, beam_scorer, constraints=constraints, logits_processor=logits_processor, **model_kwargs
+        ... )
+
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['Wie alt sind Sie?']
+        ```"""
+        # init values
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use"
+                " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
+        if len(stopping_criteria) == 0:
+            warnings.warn("You don't have defined any stopping_criteria, this will likely loop forever", UserWarning)
+        pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+        eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.generation_config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
+        )
+        return_dict_in_generate = (
+            return_dict_in_generate
+            if return_dict_in_generate is not None
+            else self.generation_config.return_dict_in_generate
+        )
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+        if return_dict_in_generate and self.config.is_encoder_decoder:
+            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+            encoder_hidden_states = (
+                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+            )
+
+        batch_size = len(constrained_beam_scorer._beam_hyps)
+        num_beams = constrained_beam_scorer.num_beams
+
+        batch_beam_size, cur_len = input_ids.shape
+
+        if num_beams * batch_size != batch_beam_size:
+            raise ValueError(
+                f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}."
+            )
+
+        # initialise score of first beam with 0 and the rest with -1e9. This makes sure that only tokens
+        # of the first beam are considered to avoid sampling the exact same tokens across all beams.
+        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
+        beam_scores[:, 1:] = -1e9
+        beam_scores = beam_scores.view((batch_size * num_beams,))
+
+        this_peer_finished = False  # used by synced_gpus only
+        while True:
+            if synced_gpus:
+                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+                # The following logic allows an early break if all peers finished generating their sequence
+                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
+                # send 0.0 if we finished, 1.0 otherwise
+                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+                # did all peers finish? the reduced sum will be 0.0 then
+                if this_peer_finished_flag.item() == 0.0:
+                    break
+
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            if synced_gpus and this_peer_finished:
+                cur_len = cur_len + 1
+                continue  # don't waste resources running the code we don't need
+
+            next_token_logits = outputs.logits[:, -1, :]
+            # hack: adjust tokens for Marian. For Marian we have to make sure that the `pad_token_id`
+            # cannot be generated both before and after the `nn.functional.log_softmax` operation.
+            next_token_logits = self.adjust_logits_during_generation(next_token_logits, cur_len=cur_len)
+            next_token_scores = nn.functional.log_softmax(
+                next_token_logits, dim=-1
+            )  # (batch_size * num_beams, vocab_size)
+
+            next_token_scores_processed = logits_processor(input_ids, next_token_scores)
+
+            next_token_scores = next_token_scores_processed + beam_scores[:, None].expand_as(next_token_scores)
+
+            scores_for_all_vocab = next_token_scores.clone()
+
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (next_token_scores,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            # reshape for beam search
+            vocab_size = next_token_scores.shape[-1]
+            next_token_scores = next_token_scores.view(batch_size, num_beams * vocab_size)
+
+            # Sample 2 next tokens for each beam (so we have some spare tokens and match output of beam search)
+            next_token_scores, next_tokens = torch.topk(
+                next_token_scores, 2 * num_beams, dim=1, largest=True, sorted=True
+            )
+
+            next_indices = (next_tokens / vocab_size).long()
+            next_tokens = next_tokens % vocab_size
+
+            # stateless
+            beam_outputs = constrained_beam_scorer.process(
+                input_ids,
+                next_token_scores,
+                next_tokens,
+                next_indices,
+                scores_for_all_vocab,
+                pad_token_id=pad_token_id,
+                eos_token_id=eos_token_id,
+            )
+            beam_scores = beam_outputs["next_beam_scores"]
+            beam_next_tokens = beam_outputs["next_beam_tokens"]
+            beam_idx = beam_outputs["next_beam_indices"]
+
+            input_ids = torch.cat([input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1)
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+            if model_kwargs["past_key_values"] is not None:
+                model_kwargs["past_key_values"] = self._reorder_cache(model_kwargs["past_key_values"], beam_idx)
+
+            # increase cur_len
+            cur_len = cur_len + 1
+
+            if constrained_beam_scorer.is_done or stopping_criteria(input_ids, scores):
+                if not synced_gpus:
+                    break
+                else:
+                    this_peer_finished = True
+
+        sequence_outputs = constrained_beam_scorer.finalize(
+            input_ids,
+            beam_scores,
+            next_tokens,
+            next_indices,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            max_length=stopping_criteria.max_length,
+        )
+
+        if return_dict_in_generate:
+            if not output_scores:
+                sequence_outputs["sequence_scores"] = None
+            if self.config.is_encoder_decoder:
+                return BeamSearchEncoderDecoderOutput(
+                    sequences=sequence_outputs["sequences"],
+                    sequences_scores=sequence_outputs["sequence_scores"],
+                    scores=scores,
+                    encoder_attentions=encoder_attentions,
+                    encoder_hidden_states=encoder_hidden_states,
+                    decoder_attentions=decoder_attentions,
+                    cross_attentions=cross_attentions,
+                    decoder_hidden_states=decoder_hidden_states,
+                )
+            else:
+                return BeamSearchDecoderOnlyOutput(
+                    sequences=sequence_outputs["sequences"],
+                    sequences_scores=sequence_outputs["sequence_scores"],
+                    scores=scores,
+                    attentions=decoder_attentions,
+                    hidden_states=decoder_hidden_states,
+                )
+        else:
+            return sequence_outputs["sequences"]
+
+
+def top_k_top_p_filtering(
+    logits: torch.FloatTensor,
+    top_k: int = 0,
+    top_p: float = 1.0,
+    filter_value: float = -float("Inf"),
+    min_tokens_to_keep: int = 1,
+) -> torch.FloatTensor:
+    """
+    Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
+
+    Args:
+        logits: logits distribution shape (batch size, vocabulary size)
+        top_k (`int`, *optional*, defaults to 0):
+            If > 0, only keep the top k tokens with highest probability (top-k filtering)
+        top_p (`float`, *optional*, defaults to 1.0):
+            If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus
+            filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
+        min_tokens_to_keep (`int`, *optional*, defaults to 1):
+            Minimumber of tokens we keep per batch example in the output.
+
+    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
+    """
+    if top_k > 0:
+        logits = TopKLogitsWarper(top_k=top_k, filter_value=filter_value, min_tokens_to_keep=min_tokens_to_keep)(
+            None, logits
+        )
+
+    if 0 <= top_p <= 1.0:
+        logits = TopPLogitsWarper(top_p=top_p, filter_value=filter_value, min_tokens_to_keep=min_tokens_to_keep)(
+            None, logits
+        )
+
+    return logits
+
+
+def _ranking_fast(
+    context_hidden: torch.FloatTensor,
+    next_hidden: torch.FloatTensor,
+    next_top_k_probs: torch.FloatTensor,
+    alpha: float,
+    beam_width: int,
+) -> torch.FloatTensor:
+    """
+    Reranks the top_k candidates based on a degeneration penalty (cosine similarity with previous tokens), as described
+    in the paper "A Contrastive Framework for Neural Text Generation". Returns the index of the best candidate for each
+    row in the batch.
+    """
+    norm_context_hidden = context_hidden / context_hidden.norm(dim=2, keepdim=True)
+    norm_next_hidden = next_hidden / next_hidden.norm(dim=2, keepdim=True)
+    cosine_matrix = torch.matmul(norm_context_hidden, norm_next_hidden.transpose(1, 2)).squeeze(-1)  # [B*K, S]
+    degeneration_penalty, _ = torch.max(cosine_matrix, dim=-1)  # [B*K]
+    next_top_k_probs = next_top_k_probs.view(-1)  # [B*K]
+    contrastive_score = (1.0 - alpha) * next_top_k_probs - alpha * degeneration_penalty
+    contrastive_score = torch.stack(torch.split(contrastive_score, beam_width))  # [B, K]
+    _, selected_idx = contrastive_score.max(dim=-1)  # [B]
+    return selected_idx
diff --git a/main.py b/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..be562252738f314d4d1f51e09454efe9788262b8
--- /dev/null
+++ b/main.py
@@ -0,0 +1,149 @@
+import os
+import pytorch_lightning as pl
+from argparse import ArgumentParser
+from pytorch_lightning import Trainer
+import pytorch_lightning.callbacks as plc
+from pytorch_lightning.loggers import TensorBoardLogger, CSVLogger
+
+from model.model_interface import MInterface
+from data.data_interface import DInterface
+from recommender.A_SASRec_final_bce_llm import SASRec, Caser, GRU
+from SASRecModules_ori import *
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+def load_callbacks(args):
+    callbacks = []
+    callbacks.append(plc.EarlyStopping(
+        monitor='metric',
+        mode='max',
+        patience=10,
+        min_delta=0.001
+    ))
+
+    callbacks.append(plc.ModelCheckpoint(
+        monitor='metric',
+        dirpath=args.ckpt_dir,
+        filename='{epoch:02d}-{metric:.3f}',
+        save_top_k=-1,
+        mode='max',
+        save_last=True,
+        #train_time_interval=args.val_check_interval
+        every_n_epochs=1
+    ))
+    
+    if args.lr_scheduler:
+        callbacks.append(plc.LearningRateMonitor(
+            logging_interval='step'))
+    return callbacks
+
+def main(args):
+    pl.seed_everything(args.seed)
+    model = MInterface(**vars(args))
+    if args.ckpt_path:
+        ckpt = torch.load(args.ckpt_path, map_location='cpu')
+
+        model.load_state_dict(ckpt['state_dict'], strict=False)
+        print("load checkpoints from {}".format(args.ckpt_path))
+
+    data_module = DInterface(llm_tokenizer=model.llama_tokenizer,**vars(args))
+
+    args.max_steps=len(data_module.trainset) * args.max_epochs // (args.accumulate_grad_batches * args.batch_size)
+    logger = TensorBoardLogger(save_dir='./log/', name=args.log_dir)
+    args.callbacks = load_callbacks(args)
+    args.logger = logger
+    if not os.path.exists(args.ckpt_dir):
+        os.makedirs(args.ckpt_dir)
+
+    trainer = Trainer.from_argparse_args(args)
+
+    if args.auto_lr_find:
+        lr_finder=trainer.tuner.lr_find(model=model, datamodule=data_module, min_lr=1e-10, max_lr=1e-3, num_training=100)
+        fig=lr_finder.plot(suggest=True)
+        fig_path="lr_finder.png"
+        fig.savefig(fig_path)
+        print("Saving to {}".format(fig_path))
+        model.hparams.lr=lr_finder.suggestion()
+
+    if args.mode == 'train':
+        trainer.fit(model=model, datamodule=data_module)
+    else:
+        trainer.test(model=model, datamodule=data_module)
+
+
+if __name__ == '__main__':
+    torch.multiprocessing.set_start_method('spawn')
+    parser = ArgumentParser()
+
+    parser.add_argument('--accelerator', default='gpu', type=str)
+    parser.add_argument('--devices', default=-1, type=list)
+    parser.add_argument('--precision', default=16, type=int)
+    parser.add_argument('--amp_backend', default="native", type=str)
+
+    parser.add_argument('--batch_size', default=4, type=int)
+    parser.add_argument('--num_workers', default=8, type=int)
+    parser.add_argument('--seed', default=1234, type=int)
+    parser.add_argument('--lr', default=1e-4, type=float)
+    parser.add_argument('--accumulate_grad_batches', default=32, type=int)
+    parser.add_argument('--check_val_every_n_epoch', default=1, type=int)
+
+    parser.add_argument('--lr_scheduler', default='cosine', choices=['cosine'], type=str)
+    parser.add_argument('--lr_decay_min_lr', default=1e-6, type=float)
+    parser.add_argument('--lr_warmup_start_lr', default=1e-6, type=float)
+
+    parser.add_argument('--load_best', action='store_true')
+    parser.add_argument('--load_dir', default=None, type=str)
+    parser.add_argument('--load_ver', default=None, type=str)
+    parser.add_argument('--load_v_num', default=None, type=int)
+
+    parser.add_argument('--dataset', default='steam_data', type=str)
+    parser.add_argument('--data_dir', default='LLaRA_MOE/data/ref/steam', type=str)
+    parser.add_argument('--model_name', default='mlp_projector', type=str)
+    parser.add_argument('--loss', default='lm', type=str)
+    parser.add_argument('--weight_decay', default=1e-5, type=float)
+    parser.add_argument('--no_augment', action='store_true')
+    parser.add_argument('--ckpt_dir', default='LLaRA_MOE/checkpoints/steam/', type=str)
+    parser.add_argument('--log_dir', default='steam_logs', type=str)
+    
+    parser.add_argument('--rec_size', default=64, type=int)
+    parser.add_argument('--padding_item_id', default=3581, type=int)
+    parser.add_argument('--llm_path', default='meta-llama/Llama-2-7b-hf', type=str)
+    parser.add_argument('--rec_model_path', default='LLaRA_MOE/rec_model/SASRec_steam.pt', type=str)
+    parser.add_argument('--prompt_path', default='LLaRA_MOE/prompt/game.txt', type=str)
+    parser.add_argument('--output_dir', default='LLaRA_MOE/output/steam_moe/', type=str)
+    parser.add_argument('--ckpt_path', type=str)
+    parser.add_argument('--rec_embed', default="SASRec", choices=['SASRec', 'Caser','GRU'], type=str)
+
+    parser.add_argument('--aug_prob', default=0.5, type=float)
+    parser.add_argument('--mode', default='test', choices=['train', 'test'], type=str)
+    parser.add_argument('--auto_lr_find', default=False, action='store_true')
+    parser.add_argument('--metric', default='hr', choices=['hr'], type=str)
+    parser.add_argument('--max_epochs', default=5, type=int)
+    parser.add_argument('--save', default='part', choices=['part', 'all'], type=str)
+    parser.add_argument('--cans_num', default=20, type=int)
+
+    # Finetuning
+    parser.add_argument('--llm_tuning', default='moelora', choices=['lora', 'freeze','freeze_lora', 'moelora'], type=str)
+    parser.add_argument('--peft_dir', default=None, type=str)
+    parser.add_argument('--peft_config', default=None, type=str)
+    parser.add_argument('--lora_r', default=8, type=float)
+    parser.add_argument('--lora_alpha', default=32, type=float)
+    parser.add_argument('--lora_dropout', default=0.1, type=float)
+    parser.add_argument('--num_moe', default=4, type=int)
+    parser.add_argument('--gating', default='Dense', type=str)
+    
+    parser.add_argument('--local_rank', default=3, type=int)
+    
+    parser.add_argument('--if_rand', default=False, type=bool)
+    
+    parser.add_argument('--router', default='unshare', choices=['share', 'unshare'], type=str)
+
+    args = parser.parse_args()
+    
+    if 'movielens' in args.data_dir:
+        args.padding_item_id = 1682
+    elif 'steam' in args.data_dir:
+        args.padding_item_id = 3581
+    elif 'lastfm' in args.data_dir:
+        args.padding_item_id = 4606
+
+    main(args)
diff --git a/model/__init__.py b/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model/mlp_projector.py b/model/mlp_projector.py
new file mode 100644
index 0000000000000000000000000000000000000000..07f66b36b4c44a5fcaabba6d5514b523de66868e
--- /dev/null
+++ b/model/mlp_projector.py
@@ -0,0 +1,14 @@
+from torch import nn
+
+class MlpProjector(nn.Module):
+    def __init__(self, rec_size=64, llm_size=4096):
+        super().__init__()
+        self.mlp_proj = nn.Sequential(
+            nn.Linear(rec_size, llm_size),
+            nn.GELU(),
+            nn.Linear(llm_size, llm_size)
+        )
+
+    def forward(self, x):
+        x = self.mlp_proj(x)
+        return x
\ No newline at end of file
diff --git a/model/model_interface.py b/model/model_interface.py
new file mode 100644
index 0000000000000000000000000000000000000000..de6f5e43b5efbb6669ceabb18c59a2094813ffa4
--- /dev/null
+++ b/model/model_interface.py
@@ -0,0 +1,492 @@
+import inspect
+import torch
+import importlib
+from torch import nn
+from torch.nn import functional as F
+import torch.optim.lr_scheduler as lrs
+
+import pytorch_lightning as pl
+
+from transformers import LlamaForCausalLM, LlamaTokenizer
+import random
+from pandas.core.frame import DataFrame
+import os.path as op
+import os
+from optims import LinearWarmupCosineLRScheduler
+import numpy as np
+from .peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType, PeftModel, MoeLoraConfig, MoeLoraModel
+import pickle
+from .router.nlpr import LambdaLayer, ResidualBlock, GateFunction, NLPRecommendationRouter, build_router
+
+
+# from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType, PeftModel
+class MInterface(pl.LightningModule):
+    def __init__(self, 
+                 **kargs):
+        super().__init__()
+        self.save_hyperparameters()
+        self.load_llm(self.hparams.llm_path)
+        
+        if self.hparams.router == 'share':
+            self.router = build_router()
+
+        self.load_rec_model(self.hparams.rec_model_path)
+        self.load_projector()
+        self.gradient_storage = {}
+    
+    def forward(self, batch):
+        targets = batch["tokens"].input_ids.masked_fill(
+            batch["tokens"].input_ids == self.llama_tokenizer.pad_token_id, -100
+        ) # [batch_size, max_len]
+        
+        targets = targets.masked_fill((batch["tokens"].token_type_ids == 0)[:,1:], -100)
+        # targets = targets.masked_fill((batch["tokens"].token_type_ids == 0)[:,:], -100)
+        
+        input_embeds, user_embeds = self.wrap_emb(batch)
+
+        if self.hparams.router == 'share':
+            gate_weights = self.router(user_embeds)
+            outputs = self.llama_model(
+                inputs_embeds=input_embeds,
+                attention_mask=batch["tokens"].attention_mask,
+                return_dict=True,
+                labels=targets,
+                use_cache=False,
+                user_embeds=user_embeds,
+                gate_weights=gate_weights
+            )
+            return outputs
+        
+        outputs = self.llama_model(
+            inputs_embeds=input_embeds,
+            attention_mask=batch["tokens"].attention_mask,
+            return_dict=True,
+            labels=targets,
+            use_cache=False,
+            user_embeds=user_embeds
+        )
+        return outputs
+
+    def generate(self, batch,temperature=0.8,do_sample=False,num_beams=1,max_gen_length=64,min_gen_length=1,repetition_penalty=1.0,length_penalty=1.0, num_return_sequences=1):
+        input_embeds, user_embeds = self.wrap_emb(batch)
+        if self.hparams.router == 'share':
+            gate_weights = self.router(user_embeds)
+            generate_ids = self.llama_model.generate(
+                inputs_embeds=input_embeds,
+                attention_mask=batch["tokens"].attention_mask,
+                temperature=temperature,
+                do_sample=do_sample,
+                num_beams=num_beams,
+                max_new_tokens=max_gen_length,
+                min_new_tokens=min_gen_length,
+                pad_token_id=self.llama_tokenizer.pad_token_id,
+                repetition_penalty=repetition_penalty,
+                length_penalty=length_penalty,
+                num_return_sequences=num_return_sequences,
+                user_embeds=user_embeds,
+                gate_weights = gate_weights
+            )
+            output_text=self.llama_tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+            outputs=[text.strip() for text in output_text]
+            return outputs
+            
+        gate_weights = self.router(user_embeds)
+        
+        generate_ids = self.llama_model.generate(
+            inputs_embeds=input_embeds,
+            attention_mask=batch["tokens"].attention_mask,
+            temperature=temperature,
+            do_sample=do_sample,
+            num_beams=num_beams,
+            max_new_tokens=max_gen_length,
+            min_new_tokens=min_gen_length,
+            pad_token_id=self.llama_tokenizer.pad_token_id,
+            repetition_penalty=repetition_penalty,
+            length_penalty=length_penalty,
+            num_return_sequences=num_return_sequences,
+            
+            user_embeds=user_embeds, 
+            gate_weights = gate_weights
+            )
+        output_text=self.llama_tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+        outputs=[text.strip() for text in output_text]
+        return outputs
+    
+    def capture_and_store_gradients(self):
+        for name, param in self.llama_model.named_parameters():
+            if "lora" in name and param.grad is not None:
+                if name not in self.gradient_storage:
+                    self.gradient_storage[name] = []
+                self.gradient_storage[name].append(param.grad.clone().detach())
+        
+        if self.trainer.global_step % 10 == 0: 
+            self.save_gradients_to_file()
+            
+    def save_gradients_to_file(self):
+        directory = self.hparams.capture_dir
+        if not os.path.exists(directory):
+            os.makedirs(directory)
+        file_path = os.path.join(directory, f'gradients_step_{self.trainer.global_step}.pkl')
+        with open(file_path, 'wb') as f:
+            pickle.dump(self.gradient_storage, f)
+        self.gradient_storage = {} 
+            
+    def training_step(self, batch, batch_idx):
+        if self.scheduler:
+            self.scheduler.step(self.trainer.global_step, self.current_epoch, self.trainer.max_steps)
+        if batch["flag"]:
+            for name, param in self.projector.named_parameters():
+                param.requires_grad = False
+        else:
+            for name, param in self.projector.named_parameters():
+                param.requires_grad = True
+        out = self(batch)
+        loss = self.configure_loss(out)
+        self.log('loss', loss, on_step=True, on_epoch=True, prog_bar=True)
+        self.log('lr', self.scheduler.optimizer.param_groups[0]['lr'], on_step=True, on_epoch=True, prog_bar=True)
+        self.log('global_step_num', self.trainer.global_step, on_step=True, on_epoch=True, prog_bar=True)
+        
+        return loss
+            
+    def on_validation_epoch_start(self):
+        self.val_content={
+            "generate":[],
+            "real":[],
+            "cans":[],
+        }
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        generate_output = self.generate(batch)
+        output=[]
+        for i,generate in enumerate(generate_output):
+            real=batch['correct_answer'][i]
+            cans=batch['cans_name'][i]
+            generate=generate.strip().split("\n")[0]
+            output.append((generate,real,cans))
+        return output
+
+    def on_validation_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
+        for generate,real,cans in outputs:
+            self.val_content["generate"].append(generate)
+            self.val_content["real"].append(real)
+            self.val_content["cans"].append(cans)
+
+    def on_validation_epoch_end(self):
+        df=DataFrame(self.val_content)
+        if not os.path.exists(self.hparams.output_dir):
+            os.makedirs(self.hparams.output_dir)
+        df.to_csv(op.join(self.hparams.output_dir, 'valid.csv'))
+        prediction_valid_ratio,hr=self.calculate_hr1(self.val_content)
+        metric=hr*prediction_valid_ratio
+        self.log('val_prediction_valid', prediction_valid_ratio, on_step=False, on_epoch=True, prog_bar=True)
+        self.log('val_hr', hr, on_step=False, on_epoch=True, prog_bar=True)
+        self.log('metric', metric, on_step=False, on_epoch=True, prog_bar=True)
+
+    def on_test_epoch_start(self):
+        self.test_content={
+            "generate":[],
+            "real":[],
+            "cans":[],
+        }
+
+    @torch.no_grad()
+    def test_step(self, batch, batch_idx):
+        generate_output = self.generate(batch)
+        output=[]
+        for i,generate in enumerate(generate_output):
+            real=batch['correct_answer'][i]
+            cans=batch['cans_name'][i]
+            generate=generate.strip().split("\n")[0]
+            output.append((generate,real,cans))
+        return output
+    
+    def on_test_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
+        for generate,real,cans in outputs:
+            self.test_content["generate"].append(generate)
+            self.test_content["real"].append(real)
+            self.test_content["cans"].append(cans)
+
+    def on_test_epoch_end(self):
+        df=DataFrame(self.test_content)
+        if not os.path.exists(self.hparams.output_dir):
+            os.makedirs(self.hparams.output_dir)
+        df.to_csv(op.join(self.hparams.output_dir, 'test.csv'))
+        prediction_valid_ratio,hr=self.calculate_hr1(self.test_content)
+        metric=hr*prediction_valid_ratio
+
+        self.log('test_prediction_valid', prediction_valid_ratio, on_step=False, on_epoch=True, prog_bar=True)
+        self.log('test_hr', hr, on_step=False, on_epoch=True, prog_bar=True)
+        self.log('metric', metric, on_step=False, on_epoch=True, prog_bar=True)
+
+    def configure_optimizers(self):
+        if hasattr(self.hparams, 'weight_decay'):
+            weight_decay = self.hparams.weight_decay
+        else:
+            weight_decay = 0
+        optimizer = torch.optim.SGD([
+            {'params': self.projector.parameters(), 'lr': self.hparams.lr, 'weight_decay':weight_decay},
+            
+            {'params': self.router.parameters(), 'lr': self.hparams.lr * 0.3, 'weight_decay':weight_decay},
+            
+            {'params': [p for n, p in self.llama_model.named_parameters() if "gating" not in n], 'lr': self.hparams.lr},
+            # {'params': [p for n, p in self.llama_model.named_parameters() if "gating" in n], 'lr': self.hparams.lr * 1, 'weight_decay':weight_decay}
+           
+            # {'params': self.llama_model.parameters(), 'lr': self.hparams.lr},
+        ])
+        
+        
+        for i, param_group in enumerate(optimizer.param_groups):
+            print(f"Initial LR for group {i}: {param_group['lr']}")   
+            total_params = sum(p.numel() for p in param_group['params'])
+            print(f"Parameter Group {i}: {total_params} parameters")
+
+        if self.hparams.lr_scheduler is None:
+            return optimizer
+        else:
+            max_step = self.trainer.max_steps
+            warmup_steps = max_step // 20
+            print(f'max_step: {max_step}')
+            print(f'warmup_steps: {warmup_steps}')
+            if self.hparams.lr_scheduler == 'cosine':
+                
+                init_lr_list = [
+                    self.hparams.lr,  
+                    self.hparams.lr * 0.3, 
+                    self.hparams.lr * 1
+                ]
+                min_lr_list = [
+                    self.hparams.lr_decay_min_lr,  
+                    self.hparams.lr_decay_min_lr * 0.3,  
+                    self.hparams.lr_decay_min_lr * 1  
+                ]
+                warmup_start_lr_list = [
+                    self.hparams.lr_warmup_start_lr, 
+                    self.hparams.lr_warmup_start_lr * 0.3, 
+                    self.hparams.lr_warmup_start_lr * 1 
+                ]
+                self.scheduler = LinearWarmupCosineLRScheduler(
+                    optimizer=optimizer,
+                    max_step=max_step,
+                    min_lr_list=min_lr_list,
+                    init_lr_list=init_lr_list,
+                    warmup_steps=warmup_steps,
+                    warmup_start_lr_list=warmup_start_lr_list
+                )
+                                
+                
+                for i, param_group in enumerate(optimizer.param_groups):
+                    print(f"Initial LR for group {i}: {param_group['lr']}")   
+                    total_params = sum(p.numel() for p in param_group['params'])
+                    print(f"Parameter Group {i}: {total_params} parameters")
+                    
+                    
+            else:
+                self.scheduler = None
+                raise ValueError('Invalid lr_scheduler type!')
+            return optimizer
+
+    def configure_loss(self, out, labels=None):
+        loss = self.hparams.loss.lower()
+        if loss == 'lm':
+            return out.loss
+        else:
+            raise ValueError("Invalid Loss Type!")
+
+    def on_save_checkpoint(self, checkpoint):
+        if self.hparams.save == 'part':
+            checkpoint.pop('optimizer_states')
+            to_be_removed = []
+            for key, value in checkpoint['state_dict'].items():
+                try:
+                    if not self.get_parameter(key).requires_grad:
+                        to_be_removed.append(key)
+                except AttributeError:
+                    to_be_removed.append(key)
+            for key in to_be_removed:
+                checkpoint['state_dict'].pop(key)
+        elif self.hparams.save == 'all':
+            pass
+        
+    def load_llm(self, llm_path):
+        print('Loading LLAMA')
+        self.llama_tokenizer = LlamaTokenizer.from_pretrained(llm_path, use_fast=False)
+        self.llama_tokenizer.pad_token = self.llama_tokenizer.eos_token
+        self.llama_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+        self.llama_tokenizer.padding_side = "right"
+        self.llama_tokenizer.add_special_tokens({'additional_special_tokens': ['[PH]','[HistoryEmb]','[CansEmb]','[ItemEmb]']})
+        self.llama_model = LlamaForCausalLM.from_pretrained(llm_path, device_map="auto",load_in_8bit=True)
+        self.llama_model.resize_token_embeddings(len(self.llama_tokenizer))
+        if self.hparams.llm_tuning == 'lora':
+            if self.hparams.peft_dir:
+                self.llama_model = PeftModel.from_pretrained(self.llm_model, self.hparams.peft_dir, is_trainable=True)
+            else:
+                if self.hparams.peft_config:
+                    peft_config = LoraConfig(**LoraConfig.from_json_file(self.hparams.peft_config))
+                else:
+                    peft_config = LoraConfig(task_type=TaskType.CAUSAL_LM,
+                                             inference_mode=False,
+                                             r=self.hparams.lora_r,
+                                             lora_alpha=self.hparams.lora_alpha,
+                                             lora_dropout=self.hparams.lora_dropout,
+                                             target_modules=['k_proj', 'v_proj', 'q_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'])
+                self.peft_config = peft_config
+                self.llama_model = get_peft_model(self.llama_model, peft_config)
+            self.llama_model.print_trainable_parameters()
+        elif self.hparams.llm_tuning == 'freeze':
+            for name, param in self.llama_model.named_parameters():
+                param.requires_grad = False
+        elif self.hparams.llm_tuning == 'freeze_lora':
+            if self.hparams.peft_dir:
+                self.llama_model = PeftModel.from_pretrained(self.llm_model, self.hparams.peft_dir, is_trainable=True)
+            else:
+                if self.hparams.peft_config:
+                    peft_config = LoraConfig(**LoraConfig.from_json_file(self.hparams.peft_config))
+                else:
+                    peft_config = LoraConfig(task_type=TaskType.CAUSAL_LM,
+                                             inference_mode=False,
+                                             r=self.hparams.lora_r,
+                                             lora_alpha=self.hparams.lora_alpha,
+                                             lora_dropout=self.hparams.lora_dropout,
+                                             target_modules=['k_proj', 'v_proj', 'q_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'])
+                self.peft_config = peft_config
+                self.llama_model = get_peft_model(self.llama_model, peft_config)
+            for name, param in self.llama_model.named_parameters():
+                param.requires_grad = False
+            self.llama_model.print_trainable_parameters()
+        elif self.hparams.llm_tuning == 'moelora':
+            if self.hparams.peft_dir:
+                self.llama_model = PeftModel.from_pretrained(self.llm_model, self.hparams.peft_dir, is_trainable=True)
+            else:
+                if self.hparams.peft_config:
+                    peft_config = MoeLoraConfig(**MoeLoraConfig.from_json_file(self.hparams.peft_config))
+                else:
+                    peft_config = MoeLoraConfig(task_type=TaskType.CAUSAL_LM,
+                                                inference_mode=False,
+                                                r=self.hparams.lora_r,
+                                                lora_alpha=self.hparams.lora_alpha,
+                                                lora_dropout=self.hparams.lora_dropout,
+                                                target_modules=['k_proj', 'v_proj', 'q_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'],
+                                                num_moe=self.hparams.num_moe,
+                                                gating=self.hparams.gating)
+                self.peft_config = peft_config
+                self.llama_model = get_peft_model(self.llama_model, peft_config)
+                
+                """for name, param in self.llama_model.named_parameters():
+                    if "gating" not in name:
+                        param.requires_grad = False"""
+            self.llama_model.print_trainable_parameters()
+        else:
+            raise NotImplementedError()
+ 
+        print('Loading LLAMA Done')
+
+    def load_projector(self):
+        name = self.hparams.model_name
+        camel_name = ''.join([i.capitalize() for i in name.split('_')])
+        try:
+            Model = getattr(importlib.import_module(
+                '.'+name, package=__package__), camel_name)
+        except:
+            raise ValueError(
+                f'Invalid Module File Name or Invalid Class Name {name}.{camel_name}!')
+        self.projector = self.instancialize(Model, rec_size=self.hparams.rec_size, llm_size=self.llama_model.config.hidden_size)
+
+    def instancialize(self, Model, **other_args):
+        class_args = inspect.getargspec(Model.__init__).args[1:]
+        inkeys = self.hparams.keys()
+        args1 = {}
+        for arg in class_args:
+            if arg in inkeys:
+                args1[arg] = getattr(self.hparams, arg)
+        args1.update(other_args)
+        # args1: args在hparams中有的部分
+        return Model(**args1)
+
+    def load_rec_model(self, rec_model_path):
+        print('Loading Rec Model')
+        self.rec_model = torch.load(rec_model_path, map_location="cpu")
+        self.rec_model.eval()
+        for name, param in self.rec_model.named_parameters():
+            param.requires_grad = False
+        print('Loding Rec model Done')
+
+    def encode_items(self, seq):
+        if self.hparams.rec_embed=="SASRec":
+            item_rec_embs=self.rec_model.cacu_x(seq)
+        elif self.hparams.rec_embed in ['Caser','GRU']:
+            item_rec_embs=self.rec_model.item_embeddings(seq)
+        item_txt_embs=self.projector(item_rec_embs)
+        return item_txt_embs
+
+    def encode_users(self, seq, len_seq):
+        if self.hparams.rec_embed=="SASRec":
+            user_rec_embs=self.rec_model.cacul_h(seq, len_seq)
+        elif self.hparams.rec_embed in ['Caser','GRU']:
+            user_rec_embs=self.rec_model.item_embeddings(seq)
+        
+        user_txt_embs=self.projector(user_rec_embs)    
+        return user_rec_embs
+    
+    def embed_tokens(self, token_ids):
+        embeds = self.llama_model.base_model.embed_tokens(token_ids)
+        return embeds
+
+    # batch -> embeds
+    def wrap_emb(self, batch):
+        input_embeds = self.llama_model.get_input_embeddings()(batch["tokens"].input_ids)
+        
+           
+        
+        his_token_id=self.llama_tokenizer("[HistoryEmb]", return_tensors="pt",add_special_tokens=False).input_ids.item()
+        cans_token_id=self.llama_tokenizer("[CansEmb]", return_tensors="pt",add_special_tokens=False).input_ids.item()
+        item_token_id=self.llama_tokenizer("[ItemEmb]", return_tensors="pt",add_special_tokens=False).input_ids.item()
+         
+        
+        his_item_embeds = self.encode_items(batch["seq"])
+        cans_item_embeds = self.encode_items(batch["cans"])
+        item_embeds=self.encode_items(batch["item_id"])
+        
+        
+        user_embeds=self.encode_users(batch["seq"], batch["len_seq"])
+
+        for i in range(len(batch["len_seq"])):
+            if (batch["tokens"].input_ids[i]==his_token_id).nonzero().shape[0]>0:
+                idx_tensor=(batch["tokens"].input_ids[i]==his_token_id).nonzero().view(-1)
+                for idx, item_emb in zip(idx_tensor,his_item_embeds[i,:batch["len_seq"][i].item()]):
+                    input_embeds[i,idx]=item_emb
+            if (batch["tokens"].input_ids[i]==cans_token_id).nonzero().shape[0]>0:
+                idx_tensor=(batch["tokens"].input_ids[i]==cans_token_id).nonzero().view(-1)
+                for idx, item_emb in zip(idx_tensor,cans_item_embeds[i,:batch["len_cans"][i].item()]):
+                    input_embeds[i,idx]=item_emb
+            if (batch["tokens"].input_ids[i]==item_token_id).nonzero().shape[0]>0:
+                idx=(batch["tokens"].input_ids[i]==item_token_id).nonzero().item()
+                input_embeds[i,idx]=item_embeds[i]
+        
+        return input_embeds, user_embeds
+     
+    def calculate_hr1(self,eval_content):
+        correct_num=0
+        valid_num=0
+        total_num=0
+        for i,generate in enumerate(eval_content["generate"]):
+            real=eval_content["real"][i]
+            cans=eval_content["cans"][i]
+            total_num+=1
+            generate=generate.strip().lower().strip()
+            real=real.strip().lower().strip()
+            cans=[item.strip().lower().strip() for item in cans]
+            gen_cans_list=[]
+            for cans_item in cans:
+                if cans_item in generate:
+                    gen_cans_list.append(cans_item)
+            if len(gen_cans_list)==1:
+                valid_num+=1
+                if real == gen_cans_list[0]:
+                    correct_num+=1
+        valid_ratio=valid_num/total_num
+        if valid_num>0:
+            hr1=correct_num/valid_num
+        else:
+            hr1=0
+        return valid_ratio,hr1
diff --git a/model/peft/__init__.py b/model/peft/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cc94f7c2d5370276920e06dee87a1dd8a414d35
--- /dev/null
+++ b/model/peft/__init__.py
@@ -0,0 +1,61 @@
+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+__version__ = "0.4.0.dev0"
+
+from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING, get_peft_config, get_peft_model
+from .peft_model import (
+    PeftModel,
+    PeftModelForCausalLM,
+    PeftModelForSeq2SeqLM,
+    PeftModelForSequenceClassification,
+    PeftModelForTokenClassification,
+    PeftModelForQuestionAnswering,
+)
+from .tuners import (
+    AdaptionPromptConfig,
+    AdaptionPromptModel,
+    LoraConfig,
+    LoraModel,
+    AdaLoraConfig,
+    AdaLoraModel,
+    PrefixEncoder,
+    PrefixTuningConfig,
+    PromptEmbedding,
+    PromptEncoder,
+    PromptEncoderConfig,
+    PromptEncoderReparameterizationType,
+    PromptTuningConfig,
+    PromptTuningInit,
+    MoeLoraConfig,
+    MoeLoraModel,
+)
+from .utils import (
+    TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,
+    PeftConfig,
+    PeftType,
+    PromptLearningConfig,
+    TaskType,
+    bloom_model_postprocess_past_key_value,
+    get_peft_model_state_dict,
+    prepare_model_for_int8_training,
+    prepare_model_for_kbit_training,
+    set_peft_model_state_dict,
+    shift_tokens_right,
+)
diff --git a/model/peft/import_utils.py b/model/peft/import_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..875e0e2c35e7672b6e10f9327dc331a4b8324590
--- /dev/null
+++ b/model/peft/import_utils.py
@@ -0,0 +1,28 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import importlib
+
+
+def is_bnb_available():
+    return importlib.util.find_spec("bitsandbytes") is not None
+
+
+def is_bnb_4bit_available():
+    if not is_bnb_available():
+        return False
+
+    import bitsandbytes as bnb
+
+    return hasattr(bnb.nn, "Linear4bit")
diff --git a/model/peft/mapping.py b/model/peft/mapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..13e933b37bd73ecc22517d75222268822d5f3cfb
--- /dev/null
+++ b/model/peft/mapping.py
@@ -0,0 +1,134 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Dict
+
+from .peft_model import (
+    PeftModel,
+    PeftModelForCausalLM,
+    PeftModelForQuestionAnswering,
+    PeftModelForSeq2SeqLM,
+    PeftModelForSequenceClassification,
+    PeftModelForTokenClassification,
+)
+from .tuners import (
+    AdaLoraConfig,
+    AdaptionPromptConfig,
+    LoraConfig,
+    PrefixTuningConfig,
+    PromptEncoderConfig,
+    PromptTuningConfig,
+    MoeLoraConfig,
+)
+from .utils import PromptLearningConfig
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+
+    from .utils.config import PeftConfig
+
+
+MODEL_TYPE_TO_PEFT_MODEL_MAPPING = {
+    "SEQ_CLS": PeftModelForSequenceClassification,
+    "SEQ_2_SEQ_LM": PeftModelForSeq2SeqLM,
+    "CAUSAL_LM": PeftModelForCausalLM,
+    "TOKEN_CLS": PeftModelForTokenClassification,
+    "QUESTION_ANS": PeftModelForQuestionAnswering,
+}
+
+PEFT_TYPE_TO_CONFIG_MAPPING = {
+    "ADAPTION_PROMPT": AdaptionPromptConfig,
+    "PROMPT_TUNING": PromptTuningConfig,
+    "PREFIX_TUNING": PrefixTuningConfig,
+    "P_TUNING": PromptEncoderConfig,
+    "LORA": LoraConfig,
+    "ADALORA": AdaLoraConfig,
+    "MOELORA": MoeLoraConfig,
+}
+
+
+def get_peft_config(config_dict: Dict[str, Any]):
+    """
+    Returns a Peft config object from a dictionary.
+
+    Args:
+        config_dict (`Dict[str, Any]`): Dictionary containing the configuration parameters.
+    """
+
+    return PEFT_TYPE_TO_CONFIG_MAPPING[config_dict["peft_type"]](**config_dict)
+
+
+def _prepare_prompt_learning_config(peft_config: PeftConfig, model_config: Dict[str, Any]):
+    if peft_config.num_layers is None:
+        if "num_hidden_layers" in model_config:
+            num_layers = model_config["num_hidden_layers"]
+        elif "num_layers" in model_config:
+            num_layers = model_config["num_layers"]
+        elif "n_layer" in model_config:
+            num_layers = model_config["n_layer"]
+        else:
+            raise ValueError("Please specify `num_layers` in `peft_config`")
+        peft_config.num_layers = num_layers
+
+    if peft_config.token_dim is None:
+        if "hidden_size" in model_config:
+            token_dim = model_config["hidden_size"]
+        elif "n_embd" in model_config:
+            token_dim = model_config["n_embd"]
+        elif "d_model" in model_config:
+            token_dim = model_config["d_model"]
+        else:
+            raise ValueError("Please specify `token_dim` in `peft_config`")
+        peft_config.token_dim = token_dim
+
+    if peft_config.num_attention_heads is None:
+        if "num_attention_heads" in model_config:
+            num_attention_heads = model_config["num_attention_heads"]
+        elif "n_head" in model_config:
+            num_attention_heads = model_config["n_head"]
+        elif "num_heads" in model_config:
+            num_attention_heads = model_config["num_heads"]
+        elif "encoder_attention_heads" in model_config:
+            num_attention_heads = model_config["encoder_attention_heads"]
+        else:
+            raise ValueError("Please specify `num_attention_heads` in `peft_config`")
+        peft_config.num_attention_heads = num_attention_heads
+
+    if getattr(peft_config, "encoder_hidden_size", None) is None:
+        setattr(peft_config, "encoder_hidden_size", peft_config.token_dim)
+
+    return peft_config
+
+
+def get_peft_model(model: PreTrainedModel, peft_config: PeftConfig, adapter_name: str = "default") -> PeftModel:
+    """
+    Returns a Peft model object from a model and a config.
+
+    Args:
+        model ([`transformers.PreTrainedModel`]): Model to be wrapped.
+        peft_config ([`PeftConfig`]): Configuration object containing the parameters of the Peft model.
+    """
+    model_config = model.config.to_dict() if hasattr(model.config, "to_dict") else model.config
+    peft_config.base_model_name_or_path = model.__dict__.get("name_or_path", None)
+    if peft_config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys() and not isinstance(
+        peft_config, PromptLearningConfig
+    ):
+        return PeftModel(model, peft_config, adapter_name=adapter_name)
+    if isinstance(peft_config, PromptLearningConfig):
+        peft_config = _prepare_prompt_learning_config(peft_config, model_config)
+    return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config, adapter_name=adapter_name)
diff --git a/model/peft/peft_model.py b/model/peft/peft_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..2dd249025640018a98e4ea77665f875406f3cecf
--- /dev/null
+++ b/model/peft/peft_model.py
@@ -0,0 +1,1619 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import inspect
+import os
+import warnings
+from contextlib import contextmanager
+from copy import deepcopy
+from typing import Any, Dict, Optional, Union
+
+import torch
+from accelerate import dispatch_model, infer_auto_device_map
+from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules
+from accelerate.utils import get_balanced_memory
+from huggingface_hub import hf_hub_download
+from huggingface_hub.utils import EntryNotFoundError
+from safetensors.torch import load_file as safe_load_file
+from safetensors.torch import save_file as safe_save_file
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput
+from transformers.utils import PushToHubMixin
+
+from . import __version__
+from .tuners import (
+    AdaLoraModel,
+    AdaptionPromptModel,
+    LoraModel,
+    PrefixEncoder,
+    PromptEmbedding,
+    PromptEncoder,
+    MoeLoraModel,
+)
+from .utils import (
+    SAFETENSORS_WEIGHTS_NAME,
+    TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,
+    WEIGHTS_NAME,
+    PeftConfig,
+    PeftType,
+    PromptLearningConfig,
+    TaskType,
+    _set_adapter,
+    _set_trainable,
+    add_library_to_model_card,
+    get_peft_model_state_dict,
+    hub_file_exists,
+    set_peft_model_state_dict,
+    shift_tokens_right,
+)
+
+
+PEFT_TYPE_TO_MODEL_MAPPING = {
+    PeftType.LORA: LoraModel,
+    PeftType.PROMPT_TUNING: PromptEmbedding,
+    PeftType.P_TUNING: PromptEncoder,
+    PeftType.PREFIX_TUNING: PrefixEncoder,
+    PeftType.ADALORA: AdaLoraModel,
+    PeftType.ADAPTION_PROMPT: AdaptionPromptModel,
+    PeftType.MOELORA: MoeLoraModel,
+}
+
+
+class PeftModel(PushToHubMixin, torch.nn.Module):
+    """
+    Base model encompassing various Peft methods.
+
+    Args:
+        model ([`~transformers.PreTrainedModel`]): The base transformer model used for Peft.
+        peft_config ([`PeftConfig`]): The configuration of the Peft model.
+
+
+    **Attributes**:
+        - **base_model** ([`~transformers.PreTrainedModel`]) -- The base transformer model used for Peft.
+        - **peft_config** ([`PeftConfig`]) -- The configuration of the Peft model.
+        - **modules_to_save** (`list` of `str`) -- The list of sub-module names to save when
+        saving the model.
+        - **prompt_encoder** ([`PromptEncoder`]) -- The prompt encoder used for Peft if
+        using [`PromptLearningConfig`].
+        - **prompt_tokens** (`torch.Tensor`) -- The virtual prompt tokens used for Peft if
+        using [`PromptLearningConfig`].
+        - **transformer_backbone_name** (`str`) -- The name of the transformer
+        backbone in the base model if using [`PromptLearningConfig`].
+        - **word_embeddings** (`torch.nn.Embedding`) -- The word embeddings of the transformer backbone
+        in the base model if using [`PromptLearningConfig`].
+    """
+
+    def __init__(self, model: PreTrainedModel, peft_config: PeftConfig, adapter_name: str = "default"):
+        super().__init__()
+        self.base_model = model
+        self.config = self.base_model.config
+        self.modules_to_save = None
+        self.peft_config = {}
+        self.active_adapter = adapter_name
+        self.peft_type = peft_config.peft_type
+        self.base_model_torch_dtype = getattr(model, "dtype", None)
+        if not isinstance(peft_config, PromptLearningConfig):
+            self.peft_config[adapter_name] = peft_config
+            self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type](
+                self.base_model, self.peft_config, adapter_name
+            )
+            self.set_additional_trainable_modules(peft_config, adapter_name)
+        else:
+            self.add_adapter(adapter_name, peft_config)
+
+        if getattr(model, "is_gradient_checkpointing", True):
+            model = self._prepare_model_for_gradient_checkpointing(model)
+
+    def save_pretrained(self, save_directory: str, safe_serialization: bool = False, **kwargs: Any):
+        r"""
+        This function saves the adapter model and the adapter configuration files to a directory, so that it can be
+        reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`]
+        method.
+
+        Args:
+            save_directory (`str`):
+                Directory where the adapter model and configuration files will be saved (will be created if it does not
+                exist).
+            kwargs (additional keyword arguments, *optional*):
+                Additional keyword arguments passed along to the `push_to_hub` method.
+        """
+        if os.path.isfile(save_directory):
+            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
+        os.makedirs(save_directory, exist_ok=True)
+        self.create_or_update_model_card(save_directory)
+
+        for adapter_name, peft_config in self.peft_config.items():
+            # save only the trainable weights
+            output_state_dict = get_peft_model_state_dict(
+                self, state_dict=kwargs.get("state_dict", None), adapter_name=adapter_name
+            )
+            output_dir = os.path.join(save_directory, adapter_name) if adapter_name != "default" else save_directory
+            os.makedirs(output_dir, exist_ok=True)
+
+            if safe_serialization:
+                safe_save_file(
+                    output_state_dict, os.path.join(output_dir, SAFETENSORS_WEIGHTS_NAME), metadata={"format": "pt"}
+                )
+            else:
+                torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME))
+
+            # save the config and change the inference mode to `True`
+            if peft_config.base_model_name_or_path is None:
+                peft_config.base_model_name_or_path = (
+                    self.base_model.__dict__.get("name_or_path", None)
+                    if isinstance(peft_config, PromptLearningConfig)
+                    else self.base_model.model.__dict__.get("name_or_path", None)
+                )
+            inference_mode = peft_config.inference_mode
+            peft_config.inference_mode = True
+            peft_config.save_pretrained(output_dir)
+            peft_config.inference_mode = inference_mode
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        model: PreTrainedModel,
+        model_id: Union[str, os.PathLike],
+        adapter_name: str = "default",
+        is_trainable: bool = False,
+        config: Optional[PeftConfig] = None,
+        **kwargs: Any,
+    ):
+        r"""
+        Instantiate a [`LoraModel`] from a pretrained Lora configuration and weights.
+
+        Args:
+            model ([`~transformers.PreTrainedModel`]):
+                The model to be adapted. The model should be initialized with the
+                [`~transformers.PreTrainedModel.from_pretrained`] method from the 🤗 Transformers library.
+            model_id (`str` or `os.PathLike`):
+                The name of the Lora configuration to use. Can be either:
+                    - A string, the `model id` of a Lora configuration hosted inside a model repo on the Hugging Face
+                      Hub.
+                    - A path to a directory containing a Lora configuration file saved using the `save_pretrained`
+                      method (`./my_lora_config_directory/`).
+            adapter_name (`str`, *optional*, defaults to `"default"`):
+                The name of the adapter to be loaded. This is useful for loading multiple adapters.
+            is_trainable (`bool`, *optional*, defaults to `False`):
+                Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and use for
+                inference
+            config ([`~peft.PeftConfig`], *optional*):
+                The configuration object to use instead of an automatically loaded configuation. This configuration
+                object is mutually exclusive with `model_id` and `kwargs`. This is useful when configuration is already
+                loaded before calling `from_pretrained`.
+            kwargs: (`optional`):
+                Additional keyword arguments passed along to the specific Lora configuration class.
+        """
+        from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING
+
+        # load the config
+        if config is None:
+            config = PEFT_TYPE_TO_CONFIG_MAPPING[
+                PeftConfig._get_peft_type(
+                    model_id,
+                    subfolder=kwargs.get("subfolder", None),
+                    revision=kwargs.get("revision", None),
+                    cache_dir=kwargs.get("cache_dir", None),
+                )
+            ].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None), **kwargs)
+        elif isinstance(config, PeftConfig):
+            config.inference_mode = not is_trainable
+        else:
+            raise ValueError(f"The input config must be a PeftConfig, got {config.__class__}")
+
+        if (getattr(model, "hf_device_map", None) is not None) and len(
+            set(model.hf_device_map.values()).intersection({"cpu", "disk"})
+        ) > 0:
+            remove_hook_from_submodules(model)
+
+        if isinstance(config, PromptLearningConfig) and is_trainable:
+            raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
+        else:
+            config.inference_mode = not is_trainable
+
+        if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys():
+            model = cls(model, config, adapter_name)
+        else:
+            model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, adapter_name)
+        model.load_adapter(model_id, adapter_name, is_trainable=is_trainable, **kwargs)
+        return model
+
+    def _setup_prompt_encoder(self, adapter_name: str):
+        config = self.peft_config[adapter_name]
+        self.prompt_encoder = torch.nn.ModuleDict({})
+        self.prompt_tokens = {}
+        transformer_backbone = None
+        for name, module in self.base_model.named_children():
+            for param in module.parameters():
+                param.requires_grad = False
+            if isinstance(module, PreTrainedModel):
+                # Make sure to freeze Tranformers model
+                if transformer_backbone is None:
+                    transformer_backbone = module
+                    self.transformer_backbone_name = name
+
+        if config.num_transformer_submodules is None:
+            config.num_transformer_submodules = 2 if config.task_type == TaskType.SEQ_2_SEQ_LM else 1
+
+        for named_param, value in list(transformer_backbone.named_parameters()):
+            if value.shape[0] == self.base_model.config.vocab_size:
+                self.word_embeddings = transformer_backbone.get_submodule(named_param.replace(".weight", ""))
+                break
+
+        if config.peft_type == PeftType.PROMPT_TUNING:
+            prompt_encoder = PromptEmbedding(config, self.word_embeddings)
+        elif config.peft_type == PeftType.P_TUNING:
+            prompt_encoder = PromptEncoder(config)
+        elif config.peft_type == PeftType.PREFIX_TUNING:
+            prompt_encoder = PrefixEncoder(config)
+        else:
+            raise ValueError("Not supported")
+        self.prompt_encoder.update(torch.nn.ModuleDict({adapter_name: prompt_encoder}))
+        self.prompt_tokens[adapter_name] = torch.arange(
+            config.num_virtual_tokens * config.num_transformer_submodules
+        ).long()
+
+    def _prepare_model_for_gradient_checkpointing(self, model: PreTrainedModel):
+        r"""
+        Prepares the model for gradient checkpointing if necessary
+        """
+        if not (getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False)):
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
+
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+        return model
+
+    def get_prompt_embedding_to_save(self, adapter_name: str):
+        """
+        Returns the prompt embedding to save when saving the model. Only applicable when `peft_config.peft_type !=
+        PeftType.LORA`.
+        """
+        prompt_encoder = self.prompt_encoder[adapter_name]
+        prompt_tokens = (
+            self.prompt_tokens[adapter_name].unsqueeze(0).expand(1, -1).to(prompt_encoder.embedding.weight.device)
+        )
+        if self.peft_config[adapter_name].peft_type == PeftType.PREFIX_TUNING:
+            prompt_tokens = prompt_tokens[:, : self.peft_config[adapter_name].num_virtual_tokens]
+        prompt_embeddings = prompt_encoder(prompt_tokens)
+        return prompt_embeddings[0].detach().cpu()
+
+    def get_prompt(self, batch_size: int):
+        """
+        Returns the virtual prompts to use for Peft. Only applicable when `peft_config.peft_type != PeftType.LORA`.
+        """
+        peft_config = self.active_peft_config
+        prompt_encoder = self.prompt_encoder[self.active_adapter]
+        prompt_tokens = (
+            self.prompt_tokens[self.active_adapter]
+            .unsqueeze(0)
+            .expand(batch_size, -1)
+            .to(prompt_encoder.embedding.weight.device)
+        )
+        if peft_config.peft_type == PeftType.PREFIX_TUNING:
+            prompt_tokens = prompt_tokens[:, : peft_config.num_virtual_tokens]
+            if peft_config.inference_mode:
+                past_key_values = prompt_encoder.embedding.weight.repeat(batch_size, 1, 1)
+            else:
+                past_key_values = prompt_encoder(prompt_tokens)
+            if self.base_model_torch_dtype is not None:
+                past_key_values = past_key_values.to(self.base_model_torch_dtype)
+            past_key_values = past_key_values.view(
+                batch_size,
+                peft_config.num_virtual_tokens,
+                peft_config.num_layers * 2,
+                peft_config.num_attention_heads,
+                peft_config.token_dim // peft_config.num_attention_heads,
+            )
+            if peft_config.num_transformer_submodules == 2:
+                past_key_values = torch.cat([past_key_values, past_key_values], dim=2)
+            past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split(
+                peft_config.num_transformer_submodules * 2
+            )
+            if TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING.get(self.config.model_type, None) is not None:
+                post_process_fn = TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING[self.config.model_type]
+                past_key_values = post_process_fn(past_key_values)
+            return past_key_values
+        else:
+            if peft_config.inference_mode:
+                prompts = prompt_encoder.embedding.weight.repeat(batch_size, 1, 1)
+            else:
+                prompts = prompt_encoder(prompt_tokens)
+            return prompts
+
+    def print_trainable_parameters(self):
+        """
+        Prints the number of trainable parameters in the model.
+        """
+        trainable_params = 0
+        all_param = 0
+        for _, param in self.named_parameters():
+            num_params = param.numel()
+            # if using DS Zero 3 and the weights are initialized empty
+            if num_params == 0 and hasattr(param, "ds_numel"):
+                num_params = param.ds_numel
+
+            all_param += num_params
+            if param.requires_grad:
+                trainable_params += num_params
+        print(
+            f"trainable params: {trainable_params:,d} || all params: {all_param:,d} || trainable%: {100 * trainable_params / all_param}"
+        )
+
+    def __getattr__(self, name: str):
+        """Forward missing attributes to the wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            return getattr(self.base_model, name)
+
+    def forward(self, *args: Any, **kwargs: Any):
+        """
+        Forward pass of the model.
+        """
+        return self.get_base_model()(*args, **kwargs)
+
+    @contextmanager
+    def disable_adapter(self):
+        """
+        Disables the adapter module.
+        """
+        try:
+            if isinstance(self.peft_config[self.active_adapter], PromptLearningConfig):
+                old_forward = self.forward
+                self.forward = self.base_model.forward
+            else:
+                self.base_model.disable_adapter_layers()
+            yield
+        finally:
+            if isinstance(self.peft_config[self.active_adapter], PromptLearningConfig):
+                self.forward = old_forward
+            else:
+                self.base_model.enable_adapter_layers()
+
+    def get_base_model(self):
+        """
+        Returns the base model.
+        """
+        return self.base_model if isinstance(self.active_peft_config, PromptLearningConfig) else self.base_model.model
+
+    def add_adapter(self, adapter_name: str, peft_config: PeftConfig):
+        if peft_config.peft_type != self.peft_type:
+            raise ValueError(
+                f"Cannot combine adapters with different peft types. "
+                f"Found {self.peft_type} and {peft_config.peft_type}."
+            )
+        self.peft_config[adapter_name] = peft_config
+        if isinstance(peft_config, PromptLearningConfig):
+            self._setup_prompt_encoder(adapter_name)
+        else:
+            self.base_model.add_adapter(adapter_name, peft_config)
+
+        self.set_additional_trainable_modules(peft_config, adapter_name)
+
+    def set_additional_trainable_modules(self, peft_config, adapter_name):
+        if getattr(peft_config, "modules_to_save", None) is not None:
+            if self.modules_to_save is None:
+                self.modules_to_save = set(peft_config.modules_to_save)
+            else:
+                self.modules_to_save.update(peft_config.modules_to_save)
+            _set_trainable(self, adapter_name)
+
+    @classmethod
+    def _split_kwargs(cls, kwargs: Dict[str, Any]):
+        hf_hub_download_kwargs = {}
+        other_kwargs = {}
+
+        for key, value in kwargs.items():
+            if key in inspect.signature(hf_hub_download).parameters:
+                hf_hub_download_kwargs[key] = value
+            else:
+                other_kwargs[key] = value
+
+        return hf_hub_download_kwargs, other_kwargs
+
+    def load_adapter(self, model_id: str, adapter_name: str, is_trainable: bool = False, **kwargs: Any):
+        from .mapping import PEFT_TYPE_TO_CONFIG_MAPPING
+
+        hf_hub_download_kwargs, kwargs = self._split_kwargs(kwargs)
+
+        if adapter_name not in self.peft_config:
+            # load the config
+            peft_config = PEFT_TYPE_TO_CONFIG_MAPPING[
+                PeftConfig._get_peft_type(
+                    model_id,
+                    subfolder=kwargs.get("subfolder", None),
+                    revision=kwargs.get("revision", None),
+                    cache_dir=kwargs.get("cache_dir", None),
+                )
+            ].from_pretrained(
+                model_id,
+                subfolder=kwargs.get("subfolder", None),
+                revision=kwargs.get("revision", None),
+                cache_dir=kwargs.get("cache_dir", None),
+            )
+            if isinstance(peft_config, PromptLearningConfig) and is_trainable:
+                raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.")
+            else:
+                peft_config.inference_mode = not is_trainable
+            self.add_adapter(adapter_name, peft_config)
+
+        # load weights if any
+        path = os.path.join(model_id, kwargs["subfolder"]) if kwargs.get("subfolder", None) is not None else model_id
+
+        if os.path.exists(os.path.join(path, SAFETENSORS_WEIGHTS_NAME)):
+            filename = os.path.join(path, SAFETENSORS_WEIGHTS_NAME)
+            use_safetensors = True
+        elif os.path.exists(os.path.join(path, WEIGHTS_NAME)):
+            filename = os.path.join(path, WEIGHTS_NAME)
+            use_safetensors = False
+        else:
+            has_remote_safetensors_file = hub_file_exists(
+                model_id, SAFETENSORS_WEIGHTS_NAME, revision=kwargs.get("revision", None)
+            )
+            use_safetensors = has_remote_safetensors_file
+
+            if has_remote_safetensors_file:
+                # Priority 1: load safetensors weights
+                filename = hf_hub_download(
+                    model_id,
+                    SAFETENSORS_WEIGHTS_NAME,
+                    subfolder=kwargs.get("subfolder", None),
+                    **hf_hub_download_kwargs,
+                )
+            else:
+                try:
+                    filename = hf_hub_download(
+                        model_id, WEIGHTS_NAME, subfolder=kwargs.get("subfolder", None), **hf_hub_download_kwargs
+                    )
+                except EntryNotFoundError:
+                    raise ValueError(
+                        f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. "
+                        f"Please check that the file {WEIGHTS_NAME} or {SAFETENSORS_WEIGHTS_NAME} is present at {model_id}."
+                    )
+
+        if use_safetensors:
+            adapters_weights = safe_load_file(filename, device="cuda" if torch.cuda.is_available() else "cpu")
+        else:
+            adapters_weights = torch.load(
+                filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            )
+
+        # load the weights into the model
+        load_result = set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name)
+        if (
+            (getattr(self, "hf_device_map", None) is not None)
+            and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
+            and len(self.peft_config) == 1
+        ):
+            device_map = kwargs.get("device_map", "auto")
+            max_memory = kwargs.get("max_memory", None)
+            offload_dir = kwargs.get("offload_folder", None)
+            offload_index = kwargs.get("offload_index", None)
+
+            dispatch_model_kwargs = {}
+            # Safety checker for previous `accelerate` versions
+            # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
+            if "offload_index" in inspect.signature(dispatch_model).parameters:
+                dispatch_model_kwargs["offload_index"] = offload_index
+
+            no_split_module_classes = self._no_split_modules
+
+            if device_map != "sequential":
+                max_memory = get_balanced_memory(
+                    self,
+                    max_memory=max_memory,
+                    no_split_module_classes=no_split_module_classes,
+                    low_zero=(device_map == "balanced_low_0"),
+                )
+            if isinstance(device_map, str):
+                device_map = infer_auto_device_map(
+                    self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
+                )
+            dispatch_model(
+                self,
+                device_map=device_map,
+                offload_dir=offload_dir,
+                **dispatch_model_kwargs,
+            )
+            hook = AlignDevicesHook(io_same_device=True)
+            if isinstance(self.peft_config[adapter_name], PromptLearningConfig):
+                remove_hook_from_submodules(self.prompt_encoder)
+            add_hook_to_module(self.get_base_model(), hook)
+
+        # Set model in evaluation mode to deactivate Dropout modules by default
+        if not is_trainable:
+            self.eval()
+        return load_result
+
+    def set_adapter(self, adapter_name: str):
+        """
+        Sets the active adapter.
+        """
+        if adapter_name not in self.peft_config:
+            raise ValueError(f"Adapter {adapter_name} not found.")
+        self.active_adapter = adapter_name
+        if not isinstance(self.peft_config[adapter_name], PromptLearningConfig):
+            self.base_model.set_adapter(adapter_name)
+        _set_adapter(self, adapter_name)
+
+    @property
+    def active_peft_config(self):
+        return self.peft_config[self.active_adapter]
+
+    def create_or_update_model_card(self, output_dir: str):
+        """
+        Updates or create model card to include information about peft:
+        1. Adds `peft` library tag
+        2. Adds peft version
+        3. Adds quantization information if it was used
+        """
+        # Adds `peft` library tag
+        add_library_to_model_card(output_dir)
+
+        with open(os.path.join(output_dir, "README.md"), "r") as f:
+            lines = f.readlines()
+
+        quantization_config = None
+        if hasattr(self.config, "quantization_config"):
+            quantization_config = self.config.quantization_config.to_dict()
+        training_config_text = ""
+        # Adds quantization information if it was used
+        if quantization_config is not None:
+            training_config_text += "\nThe following `bitsandbytes` quantization config was used during training:\n"
+            training_config_text += "\n".join([f"- {name}: {value}" for name, value in quantization_config.items()])
+            training_config_text += "\n"
+
+        training_procedure_heading = "## Training procedure\n"
+        if training_procedure_heading in lines:
+            lines.insert(lines.index(training_procedure_heading) + 2, training_config_text)
+        else:
+            lines.append(f"{training_procedure_heading}\n{training_config_text}")
+
+        # Adds peft version
+        framework_block_heading = "### Framework versions\n"
+        if framework_block_heading in lines:
+            lines.insert(lines.index(framework_block_heading) + 2, f"- PEFT {__version__}\n")
+        else:
+            lines.append(f"{framework_block_heading}\n\n- PEFT {__version__}\n")
+
+        # write the lines back to README.md
+        with open(os.path.join(output_dir, "README.md"), "w") as f:
+            f.writelines(lines)
+
+
+class PeftModelForSequenceClassification(PeftModel):
+    """
+    Peft model for sequence classification tasks.
+
+    Args:
+        model ([`~transformers.PreTrainedModel`]): Base transformer model.
+        peft_config ([`PeftConfig`]): Peft config.
+
+    **Attributes**:
+        - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
+        - **cls_layer_name** (`str`) -- The name of the classification layer.
+
+    Example:
+
+        ```py
+        >>> from transformers import AutoModelForSequenceClassification
+        >>> from peft import PeftModelForSequenceClassification, get_peft_config
+
+        >>> config = {
+        ...     "peft_type": "PREFIX_TUNING",
+        ...     "task_type": "SEQ_CLS",
+        ...     "inference_mode": False,
+        ...     "num_virtual_tokens": 20,
+        ...     "token_dim": 768,
+        ...     "num_transformer_submodules": 1,
+        ...     "num_attention_heads": 12,
+        ...     "num_layers": 12,
+        ...     "encoder_hidden_size": 768,
+        ...     "prefix_projection": False,
+        ...     "postprocess_past_key_value_function": None,
+        ... }
+
+        >>> peft_config = get_peft_config(config)
+        >>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
+        >>> peft_model = PeftModelForSequenceClassification(model, peft_config)
+        >>> peft_model.print_trainable_parameters()
+        trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117
+        ```
+    """
+
+    def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
+        super().__init__(model, peft_config, adapter_name)
+        if self.modules_to_save is None:
+            self.modules_to_save = {"classifier", "score"}
+        else:
+            self.modules_to_save.update({"classifier", "score"})
+
+        for name, _ in self.base_model.named_children():
+            if any(module_name in name for module_name in self.modules_to_save):
+                self.cls_layer_name = name
+                break
+
+        # to make sure classifier layer is trainable
+        _set_trainable(self, adapter_name)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        peft_config = self.active_peft_config
+        if not isinstance(peft_config, PromptLearningConfig):
+            return self.base_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                labels=labels,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs,
+            )
+
+        batch_size = input_ids.shape[0]
+        if attention_mask is not None:
+            # concat prompt attention mask
+            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
+            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
+        if kwargs.get("position_ids", None) is not None:
+            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
+            kwargs["position_ids"] = None
+        kwargs.update(
+            {
+                "attention_mask": attention_mask,
+                "labels": labels,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+            }
+        )
+
+        if peft_config.peft_type == PeftType.PREFIX_TUNING:
+            return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
+        else:
+            if kwargs.get("token_type_ids", None) is not None:
+                kwargs["token_type_ids"] = torch.cat(
+                    (
+                        torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.word_embeddings.weight.device),
+                        kwargs["token_type_ids"],
+                    ),
+                    dim=1,
+                ).long()
+            if inputs_embeds is None:
+                inputs_embeds = self.word_embeddings(input_ids)
+            prompts = self.get_prompt(batch_size=batch_size)
+            prompts = prompts.to(inputs_embeds.dtype)
+            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
+            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
+
+    def _prefix_tuning_forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        batch_size = input_ids.shape[0]
+        past_key_values = self.get_prompt(batch_size)
+        fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
+        kwargs.update(
+            {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+                "inputs_embeds": inputs_embeds,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+                "past_key_values": past_key_values,
+            }
+        )
+        if "past_key_values" in fwd_params:
+            return self.base_model(labels=labels, **kwargs)
+        else:
+            transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
+            fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
+            if "past_key_values" not in fwd_params:
+                raise ValueError("Model does not support past key values which are required for prefix tuning.")
+            outputs = transformer_backbone_name(**kwargs)
+            pooled_output = outputs[1] if len(outputs) > 1 else outputs[0]
+            if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
+                pooled_output = self.base_model.dropout(pooled_output)
+            logits = self.base_model.get_submodule(self.cls_layer_name)(pooled_output)
+
+            loss = None
+            if labels is not None:
+                if self.config.problem_type is None:
+                    if self.base_model.num_labels == 1:
+                        self.config.problem_type = "regression"
+                    elif self.base_model.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                        self.config.problem_type = "single_label_classification"
+                    else:
+                        self.config.problem_type = "multi_label_classification"
+
+                if self.config.problem_type == "regression":
+                    loss_fct = MSELoss()
+                    if self.base_model.num_labels == 1:
+                        loss = loss_fct(logits.squeeze(), labels.squeeze())
+                    else:
+                        loss = loss_fct(logits, labels)
+                elif self.config.problem_type == "single_label_classification":
+                    loss_fct = CrossEntropyLoss()
+                    loss = loss_fct(logits.view(-1, self.base_model.num_labels), labels.view(-1))
+                elif self.config.problem_type == "multi_label_classification":
+                    loss_fct = BCEWithLogitsLoss()
+                    loss = loss_fct(logits, labels)
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return SequenceClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+
+
+class PeftModelForCausalLM(PeftModel):
+    """
+    Peft model for causal language modeling.
+
+    Args:
+        model ([`~transformers.PreTrainedModel`]): Base transformer model.
+        peft_config ([`PeftConfig`]): Peft config.
+
+
+    Example:
+
+        ```py
+        >>> from transformers import AutoModelForCausalLM
+        >>> from peft import PeftModelForCausalLM, get_peft_config
+
+        >>> config = {
+        ...     "peft_type": "PREFIX_TUNING",
+        ...     "task_type": "CAUSAL_LM",
+        ...     "inference_mode": False,
+        ...     "num_virtual_tokens": 20,
+        ...     "token_dim": 1280,
+        ...     "num_transformer_submodules": 1,
+        ...     "num_attention_heads": 20,
+        ...     "num_layers": 36,
+        ...     "encoder_hidden_size": 1280,
+        ...     "prefix_projection": False,
+        ...     "postprocess_past_key_value_function": None,
+        ... }
+
+        >>> peft_config = get_peft_config(config)
+        >>> model = AutoModelForCausalLM.from_pretrained("gpt2-large")
+        >>> peft_model = PeftModelForCausalLM(model, peft_config)
+        >>> peft_model.print_trainable_parameters()
+        trainable params: 1843200 || all params: 775873280 || trainable%: 0.23756456724479544
+        ```
+    """
+
+    def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
+        super().__init__(model, peft_config, adapter_name)
+        # 备份self.base_model_prepare_inputs_for_generation
+        self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
+        self.base_model._validate_model_kwargs = self.base_model_validate_model_kwargs
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        peft_config = self.active_peft_config
+        
+        # 添加
+        # print("kwargs = ", kwargs)
+        
+        if not isinstance(peft_config, PromptLearningConfig):
+            if self.base_model.config.model_type == "mpt":
+                if inputs_embeds is not None:
+                    raise AssertionError("forward in MPTForCausalLM does not support inputs_embeds")
+                return self.base_model(
+                    input_ids=input_ids,
+                    attention_mask=attention_mask,
+                    labels=labels,
+                    output_attentions=output_attentions,
+                    output_hidden_states=output_hidden_states,
+                    return_dict=return_dict,
+                    **kwargs,
+                )
+
+            return self.base_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                labels=labels,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs,
+            )
+
+        batch_size = input_ids.shape[0]
+        if attention_mask is not None:
+            # concat prompt attention mask
+            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
+            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
+
+        if kwargs.get("position_ids", None) is not None:
+            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
+            kwargs["position_ids"] = None
+        if kwargs.get("token_type_ids", None) is not None:
+            warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
+            kwargs["token_type_ids"] = None
+        kwargs.update(
+            {
+                "attention_mask": attention_mask,
+                "labels": labels,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+            }
+        )
+
+        if peft_config.peft_type == PeftType.PREFIX_TUNING:
+            past_key_values = self.get_prompt(batch_size)
+            return self.base_model(input_ids=input_ids, past_key_values=past_key_values, **kwargs)
+        else:
+            if inputs_embeds is None:
+                inputs_embeds = self.word_embeddings(input_ids)
+            # concat prompt labels
+            if labels is not None:
+                prefix_labels = torch.full((batch_size, peft_config.num_virtual_tokens), -100).to(labels.device)
+                kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
+            prompts = self.get_prompt(batch_size=batch_size)
+            prompts = prompts.to(inputs_embeds.dtype)
+            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
+            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
+
+    def generate(self, **kwargs):
+        self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
+        if hasattr(self.base_model, "model"):
+            self.base_model.model.generation_config = self.generation_config
+        else:
+            self.base_model.generation_config = self.generation_config
+        try:
+            # MoeLoRAModel.generate
+            outputs = self.base_model.generate(**kwargs)
+        except:
+            # 引发异常
+            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
+            raise
+        else:
+            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
+            return outputs
+
+    def prepare_inputs_for_generation(self, *args, **kwargs):
+        peft_config = self.active_peft_config
+        model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)
+        if isinstance(peft_config, PromptLearningConfig):
+            if model_kwargs.get("attention_mask", None) is not None:
+                prefix_attention_mask = torch.ones(
+                    model_kwargs["input_ids"].shape[0], peft_config.num_virtual_tokens
+                ).to(model_kwargs["input_ids"].device)
+                model_kwargs["attention_mask"] = torch.cat(
+                    (prefix_attention_mask, model_kwargs["attention_mask"]), dim=1
+                )
+
+            if model_kwargs.get("position_ids", None) is not None:
+                warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
+                model_kwargs["position_ids"] = None
+
+            if kwargs.get("token_type_ids", None) is not None:
+                warnings.warn(
+                    "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
+                )
+                kwargs["token_type_ids"] = None
+
+            if model_kwargs["past_key_values"] is None and peft_config.peft_type == PeftType.PREFIX_TUNING:
+                past_key_values = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
+                model_kwargs["past_key_values"] = past_key_values
+            else:
+                if model_kwargs["past_key_values"] is None:
+                    inputs_embeds = self.word_embeddings(model_kwargs["input_ids"])
+                    prompts = self.get_prompt(batch_size=model_kwargs["input_ids"].shape[0])
+                    prompts = prompts.to(inputs_embeds.dtype)
+                    model_kwargs["inputs_embeds"] = torch.cat((prompts, inputs_embeds), dim=1)
+                    model_kwargs["input_ids"] = None
+        # !!!
+        model_kwargs["user_embeds"] = None
+        return model_kwargs
+
+    # !!!
+    def base_model_validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
+        """Validates model kwargs for generation. Generate argument typos will also be caught here."""
+        pass
+        # If a `Cache` instance is passed, checks whether the model is compatible with it
+        if isinstance(model_kwargs.get("past_key_values", None), Cache) and not self._supports_cache_class:
+            raise ValueError(
+                f"{self.__class__.__name__} does not support an instance of `Cache` as `past_key_values`. Please "
+                "check the model documentation for supported cache formats."
+            )
+
+        # Excludes arguments that are handled before calling any model function
+        if self.config.is_encoder_decoder:
+            for key in ["decoder_input_ids"]:
+                model_kwargs.pop(key, None)
+
+        unused_model_args = []
+        model_args = set(inspect.signature(self.prepare_inputs_for_generation).parameters)
+        # `kwargs`/`model_kwargs` is often used to handle optional forward pass inputs like `attention_mask`. If
+        # `prepare_inputs_for_generation` doesn't accept them, then a stricter check can be made ;)
+        if "kwargs" in model_args or "model_kwargs" in model_args:
+            model_args |= set(inspect.signature(self.forward).parameters)
+
+        # Encoder-Decoder models may also need Encoder arguments from `model_kwargs`
+        if self.config.is_encoder_decoder:
+            base_model = getattr(self, self.base_model_prefix, None)
+
+            # allow encoder kwargs
+            encoder = getattr(self, "encoder", None)
+            # `MusicgenForConditionalGeneration` has `text_encoder` and `audio_encoder`.
+            # Also, it has `base_model_prefix = "encoder_decoder"` but there is no `self.encoder_decoder`
+            # TODO: A better way to handle this.
+            if encoder is None and base_model is not None:
+                encoder = getattr(base_model, "encoder", None)
+
+            if encoder is not None:
+                encoder_model_args = set(inspect.signature(encoder.forward).parameters)
+                model_args |= encoder_model_args
+
+            # allow decoder kwargs
+            decoder = getattr(self, "decoder", None)
+            if decoder is None and base_model is not None:
+                decoder = getattr(base_model, "decoder", None)
+
+            if decoder is not None:
+                decoder_model_args = set(inspect.signature(decoder.forward).parameters)
+                model_args |= {f"decoder_{x}" for x in decoder_model_args}
+
+            # allow assistant_encoder_outputs to be passed if we're doing assisted generating
+            if "assistant_encoder_outputs" in model_kwargs:
+                model_args |= {"assistant_encoder_outputs"}
+
+        for key, value in model_kwargs.items():
+            if value is not None and key not in model_args:
+                unused_model_args.append(key)
+        
+        if unused_model_args:
+            raise ValueError(
+                f"The following `model_kwargs` are not used by the model: {unused_model_args} (note: typos in the"
+                " generate arguments will also show up in this list)"
+            )
+
+class PeftModelForSeq2SeqLM(PeftModel):
+    """
+    Peft model for sequence-to-sequence language modeling.
+
+    Args:
+        model ([`~transformers.PreTrainedModel`]): Base transformer model.
+        peft_config ([`PeftConfig`]): Peft config.
+
+
+    Example:
+
+        ```py
+        >>> from transformers import AutoModelForSeq2SeqLM
+        >>> from peft import PeftModelForSeq2SeqLM, get_peft_config
+
+        >>> config = {
+        ...     "peft_type": "LORA",
+        ...     "task_type": "SEQ_2_SEQ_LM",
+        ...     "inference_mode": False,
+        ...     "r": 8,
+        ...     "target_modules": ["q", "v"],
+        ...     "lora_alpha": 32,
+        ...     "lora_dropout": 0.1,
+        ...     "fan_in_fan_out": False,
+        ...     "enable_lora": None,
+        ...     "bias": "none",
+        ... }
+
+        >>> peft_config = get_peft_config(config)
+        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+        >>> peft_model = PeftModelForSeq2SeqLM(model, peft_config)
+        >>> peft_model.print_trainable_parameters()
+        trainable params: 884736 || all params: 223843584 || trainable%: 0.3952474242013566
+        ```
+    """
+
+    def __init__(self, model, peft_config: PeftConfig, adapter_name="default"):
+        super().__init__(model, peft_config, adapter_name)
+        self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation
+        self.base_model_prepare_encoder_decoder_kwargs_for_generation = (
+            self.base_model._prepare_encoder_decoder_kwargs_for_generation
+        )
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        decoder_inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        peft_config = self.active_peft_config
+        if not isinstance(peft_config, PromptLearningConfig):
+            return self.base_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                decoder_input_ids=decoder_input_ids,
+                decoder_attention_mask=decoder_attention_mask,
+                decoder_inputs_embeds=decoder_inputs_embeds,
+                labels=labels,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs,
+            )
+
+        batch_size = input_ids.shape[0]
+        if decoder_attention_mask is not None:
+            # concat prompt attention mask
+            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(
+                decoder_attention_mask.device
+            )
+            decoder_attention_mask = torch.cat((prefix_attention_mask, decoder_attention_mask), dim=1)
+
+        if kwargs.get("position_ids", None) is not None:
+            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
+            kwargs["position_ids"] = None
+        if kwargs.get("token_type_ids", None) is not None:
+            warnings.warn("Token type ids are not supported for parameter efficient tuning. Ignoring token type ids")
+            kwargs["token_type_ids"] = None
+        kwargs.update(
+            {
+                "attention_mask": attention_mask,
+                "decoder_attention_mask": decoder_attention_mask,
+                "labels": labels,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+            }
+        )
+
+        if peft_config.peft_type == PeftType.PREFIX_TUNING:
+            past_key_values = self.get_prompt(batch_size)
+            return self.base_model(
+                input_ids=input_ids, decoder_input_ids=decoder_input_ids, past_key_values=past_key_values, **kwargs
+            )
+        elif peft_config.peft_type in [PeftType.PROMPT_TUNING, PeftType.P_TUNING]:
+            if inputs_embeds is None:
+                inputs_embeds = self.word_embeddings(input_ids)
+
+            if attention_mask is not None:
+                # concat prompt attention mask
+                prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(
+                    attention_mask.device
+                )
+                kwargs["attention_mask"] = torch.cat((prefix_attention_mask, attention_mask), dim=1)
+
+            prompts = self.get_prompt(batch_size=batch_size)
+            prompts = prompts.to(inputs_embeds.dtype)
+            inputs_embeds = torch.cat((prompts[:, : peft_config.num_virtual_tokens], inputs_embeds), dim=1)
+
+            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
+        else:
+            if inputs_embeds is None:
+                inputs_embeds = self.word_embeddings(input_ids)
+            if decoder_inputs_embeds is None and decoder_input_ids is None:
+                decoder_input_ids = shift_tokens_right(
+                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
+                )
+                decoder_inputs_embeds = self.word_embeddings(decoder_input_ids)
+
+            if attention_mask is not None:
+                # concat prompt attention mask
+                prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(
+                    attention_mask.device
+                )
+                kwargs["attention_mask"] = torch.cat((prefix_attention_mask, attention_mask), dim=1)
+            # concat prompt labels
+            if labels is not None:
+                if peft_config.num_transformer_submodules == 1:
+                    kwargs["labels"] = labels
+                elif peft_config.num_transformer_submodules == 2:
+                    prefix_labels = torch.full((batch_size, peft_config.num_virtual_tokens), -100).to(labels.device)
+                    kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1)
+            prompts = self.get_prompt(batch_size=batch_size)
+            prompts = prompts.to(inputs_embeds.dtype)
+            inputs_embeds = torch.cat((prompts[:, : peft_config.num_virtual_tokens], inputs_embeds), dim=1)
+            if peft_config.num_transformer_submodules == 1:
+                return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
+            elif peft_config.num_transformer_submodules == 2:
+                decoder_inputs_embeds = torch.cat(
+                    (prompts[:, peft_config.num_virtual_tokens :], decoder_inputs_embeds), dim=1
+                )
+                return self.base_model(
+                    inputs_embeds=inputs_embeds, decoder_inputs_embeds=decoder_inputs_embeds, **kwargs
+                )
+
+    def generate(self, **kwargs):
+        peft_config = self.active_peft_config
+        self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation
+        self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
+            self._prepare_encoder_decoder_kwargs_for_generation
+        )
+        try:
+            if not isinstance(peft_config, PromptLearningConfig):
+                outputs = self.base_model.generate(**kwargs)
+            else:
+                if "input_ids" not in kwargs:
+                    raise ValueError("input_ids must be provided for Peft model generation")
+                if kwargs.get("position_ids", None) is not None:
+                    warnings.warn(
+                        "Position ids are not supported for parameter efficient tuning. Ignoring position ids."
+                    )
+                    kwargs["position_ids"] = None
+                if kwargs.get("token_type_ids", None) is not None:
+                    warnings.warn(
+                        "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids"
+                    )
+                    kwargs["token_type_ids"] = None
+
+                if peft_config.peft_type == PeftType.PREFIX_TUNING:
+                    outputs = self.base_model.generate(**kwargs)
+                elif peft_config.peft_type in [PeftType.PROMPT_TUNING, PeftType.P_TUNING]:
+                    kwargs = deepcopy(kwargs)
+
+                    if "encoder_outputs" in kwargs:
+                        del kwargs["encoder_ouputs"]
+                        warnings.warn(
+                            "`encoder_outputs` should not be passed to `generate` when using prompt tuning. Ignoring it."
+                        )
+
+                    input_ids = kwargs.pop("input_ids")
+                    inputs_embeds = self.word_embeddings(input_ids)
+                    batch_size = inputs_embeds.shape[0]
+                    prompts = self.get_prompt(batch_size=batch_size)
+                    prompts = prompts.to(inputs_embeds.dtype)
+
+                    inputs_embeds = torch.cat((prompts[:, : peft_config.num_virtual_tokens], inputs_embeds), dim=1)
+                    kwargs["inputs_embeds"] = inputs_embeds
+
+                    if "attention_mask" in kwargs:
+                        prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(
+                            kwargs["attention_mask"].device
+                        )
+                        kwargs["attention_mask"] = torch.cat((prefix_attention_mask, kwargs["attention_mask"]), dim=1)
+
+                    return self.base_model.generate(**kwargs)
+                else:
+                    raise NotImplementedError
+        except:
+            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
+            self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
+                self.base_model_prepare_encoder_decoder_kwargs_for_generation
+            )
+            raise
+        else:
+            self.base_model.prepare_inputs_for_generation = self.base_model_prepare_inputs_for_generation
+            self.base_model._prepare_encoder_decoder_kwargs_for_generation = (
+                self.base_model_prepare_encoder_decoder_kwargs_for_generation
+            )
+            return outputs
+
+    def prepare_inputs_for_generation(self, *args, **kwargs):
+        peft_config = self.active_peft_config
+        model_kwargs = self.base_model_prepare_inputs_for_generation(*args, **kwargs)
+        if model_kwargs["past_key_values"] is None and peft_config.peft_type == PeftType.PREFIX_TUNING:
+            batch_size = model_kwargs["decoder_input_ids"].shape[0]
+            past_key_values = self.get_prompt(batch_size)
+            model_kwargs["past_key_values"] = past_key_values
+
+        return model_kwargs
+
+
+class PeftModelForTokenClassification(PeftModel):
+    """
+    Peft model for token classification tasks.
+
+    Args:
+        model ([`~transformers.PreTrainedModel`]): Base transformer model.
+        peft_config ([`PeftConfig`]): Peft config.
+
+    **Attributes**:
+        - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
+        - **cls_layer_name** (`str`) -- The name of the classification layer.
+
+    Example:
+
+        ```py
+        >>> from transformers import AutoModelForSequenceClassification
+        >>> from peft import PeftModelForTokenClassification, get_peft_config
+
+        >>> config = {
+        ...     "peft_type": "PREFIX_TUNING",
+        ...     "task_type": "TOKEN_CLS",
+        ...     "inference_mode": False,
+        ...     "num_virtual_tokens": 20,
+        ...     "token_dim": 768,
+        ...     "num_transformer_submodules": 1,
+        ...     "num_attention_heads": 12,
+        ...     "num_layers": 12,
+        ...     "encoder_hidden_size": 768,
+        ...     "prefix_projection": False,
+        ...     "postprocess_past_key_value_function": None,
+        ... }
+
+        >>> peft_config = get_peft_config(config)
+        >>> model = AutoModelForTokenClassification.from_pretrained("bert-base-cased")
+        >>> peft_model = PeftModelForTokenClassification(model, peft_config)
+        >>> peft_model.print_trainable_parameters()
+        trainable params: 370178 || all params: 108680450 || trainable%: 0.3406113979101117
+        ```
+    """
+
+    def __init__(self, model, peft_config: PeftConfig = None, adapter_name="default"):
+        super().__init__(model, peft_config, adapter_name)
+        if self.modules_to_save is None:
+            self.modules_to_save = {"classifier", "score"}
+        else:
+            self.modules_to_save.update({"classifier", "score"})
+
+        for name, _ in self.base_model.named_children():
+            if any(module_name in name for module_name in self.modules_to_save):
+                self.cls_layer_name = name
+                break
+
+        # to make sure classifier layer is trainable
+        _set_trainable(self, adapter_name)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        peft_config = self.active_peft_config
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if not isinstance(peft_config, PromptLearningConfig):
+            return self.base_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                labels=labels,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs,
+            )
+
+        batch_size = input_ids.shape[0]
+        if attention_mask is not None:
+            # concat prompt attention mask
+            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
+            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
+        if kwargs.get("position_ids", None) is not None:
+            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
+            kwargs["position_ids"] = None
+        kwargs.update(
+            {
+                "attention_mask": attention_mask,
+                "labels": labels,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+            }
+        )
+
+        if peft_config.peft_type == PeftType.PREFIX_TUNING:
+            return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
+        else:
+            if kwargs.get("token_type_ids", None) is not None:
+                kwargs["token_type_ids"] = torch.cat(
+                    (
+                        torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.word_embeddings.weight.device),
+                        kwargs["token_type_ids"],
+                    ),
+                    dim=1,
+                ).long()
+            if inputs_embeds is None:
+                inputs_embeds = self.word_embeddings(input_ids)
+            prompts = self.get_prompt(batch_size=batch_size)
+            prompts = prompts.to(inputs_embeds.dtype)
+            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
+            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
+
+    def _prefix_tuning_forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        batch_size = input_ids.shape[0]
+        past_key_values = self.get_prompt(batch_size)
+        fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
+        kwargs.update(
+            {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+                "inputs_embeds": inputs_embeds,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+                "past_key_values": past_key_values,
+            }
+        )
+        if "past_key_values" in fwd_params:
+            return self.base_model(labels=labels, **kwargs)
+        else:
+            transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
+            fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
+            if "past_key_values" not in fwd_params:
+                raise ValueError("Model does not support past key values which are required for prefix tuning.")
+            outputs = transformer_backbone_name(**kwargs)
+            sequence_output = outputs[0]
+            if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
+                sequence_output = self.base_model.dropout(sequence_output)
+            logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output)
+
+            loss = None
+            if labels is not None:
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+            if not return_dict:
+                output = (logits,) + outputs[2:]
+                return ((loss,) + output) if loss is not None else output
+
+            return TokenClassifierOutput(
+                loss=loss,
+                logits=logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
+
+
+class PeftModelForQuestionAnswering(PeftModel):
+    """
+    Peft model for extractive question answering.
+
+    Args:
+        model ([`~transformers.PreTrainedModel`]): Base transformer model.
+        peft_config ([`PeftConfig`]): Peft config.
+
+    **Attributes**:
+        - **config** ([`~transformers.PretrainedConfig`]) -- The configuration object of the base model.
+        - **cls_layer_name** (`str`) -- The name of the classification layer.
+
+    Example:
+
+        ```py
+        >>> from transformers import AutoModelForQuestionAnswering
+        >>> from peft import PeftModelForQuestionAnswering, get_peft_config
+
+        >>> config = {
+        ...     "peft_type": "LORA",
+        ...     "task_type": "QUESTION_ANS",
+        ...     "inference_mode": False,
+        ...     "r": 16,
+        ...     "target_modules": ["query", "value"],
+        ...     "lora_alpha": 32,
+        ...     "lora_dropout": 0.05,
+        ...     "fan_in_fan_out": False,
+        ...     "bias": "none",
+        ... }
+
+        >>> peft_config = get_peft_config(config)
+        >>> model = AutoModelForQuestionAnswering.from_pretrained("bert-base-cased")
+        >>> peft_model = PeftModelForQuestionAnswering(model, peft_config)
+        >>> peft_model.print_trainable_parameters()
+        trainable params: 592900 || all params: 108312580 || trainable%: 0.5473971721475013
+        ```
+    """
+
+    def __init__(self, model, peft_config: PeftConfig = None, adapter_name="default"):
+        super().__init__(model, peft_config, adapter_name)
+        if self.modules_to_save is None:
+            self.modules_to_save = {"qa_outputs"}
+        else:
+            self.modules_to_save.update({"qa_outputs"})
+
+        for name, _ in self.base_model.named_children():
+            if any(module_name in name for module_name in self.modules_to_save):
+                self.cls_layer_name = name
+                break
+
+        # to make sure classifier layer is trainable
+        _set_trainable(self, adapter_name)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        inputs_embeds=None,
+        start_positions=None,
+        end_positions=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        peft_config = self.active_peft_config
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if not isinstance(peft_config, PromptLearningConfig):
+            return self.base_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                start_positions=start_positions,
+                end_positions=end_positions,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=return_dict,
+                **kwargs,
+            )
+
+        batch_size = input_ids.shape[0]
+        if attention_mask is not None:
+            # concat prompt attention mask
+            prefix_attention_mask = torch.ones(batch_size, peft_config.num_virtual_tokens).to(attention_mask.device)
+            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)
+        if kwargs.get("position_ids", None) is not None:
+            warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.")
+            kwargs["position_ids"] = None
+        kwargs.update(
+            {
+                "attention_mask": attention_mask,
+                "start_positions": start_positions,
+                "end_positions": end_positions,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+            }
+        )
+
+        if peft_config.peft_type == PeftType.PREFIX_TUNING:
+            return self._prefix_tuning_forward(input_ids=input_ids, **kwargs)
+        else:
+            if kwargs.get("token_type_ids", None) is not None:
+                kwargs["token_type_ids"] = torch.cat(
+                    (
+                        torch.zeros(batch_size, peft_config.num_virtual_tokens).to(self.word_embeddings.weight.device),
+                        kwargs["token_type_ids"],
+                    ),
+                    dim=1,
+                ).long()
+            if inputs_embeds is None:
+                inputs_embeds = self.word_embeddings(input_ids)
+            prompts = self.get_prompt(batch_size=batch_size)
+            prompts = prompts.to(inputs_embeds.dtype)
+            inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1)
+            return self.base_model(inputs_embeds=inputs_embeds, **kwargs)
+
+    def _prefix_tuning_forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        start_positions=None,
+        end_positions=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        batch_size = input_ids.shape[0]
+        past_key_values = self.get_prompt(batch_size)
+        fwd_params = list(inspect.signature(self.base_model.forward).parameters.keys())
+        kwargs.update(
+            {
+                "input_ids": input_ids,
+                "attention_mask": attention_mask,
+                "inputs_embeds": inputs_embeds,
+                "output_attentions": output_attentions,
+                "output_hidden_states": output_hidden_states,
+                "return_dict": return_dict,
+                "past_key_values": past_key_values,
+            }
+        )
+        if "past_key_values" in fwd_params:
+            return self.base_model(start_positions=start_positions, end_positions=end_positions, **kwargs)
+        else:
+            transformer_backbone_name = self.base_model.get_submodule(self.transformer_backbone_name)
+            fwd_params = list(inspect.signature(transformer_backbone_name.forward).parameters.keys())
+            if "past_key_values" not in fwd_params:
+                raise ValueError("Model does not support past key values which are required for prefix tuning.")
+            outputs = transformer_backbone_name(**kwargs)
+            sequence_output = outputs[0]
+            if "dropout" in [name for name, _ in list(self.base_model.named_children())]:
+                sequence_output = self.base_model.dropout(sequence_output)
+            logits = self.base_model.get_submodule(self.cls_layer_name)(sequence_output)
+            start_logits, end_logits = logits.split(1, dim=-1)
+            start_logits = start_logits.squeeze(-1).contiguous()
+            end_logits = end_logits.squeeze(-1).contiguous()
+
+            total_loss = None
+            if start_positions is not None and end_positions is not None:
+                # If we are on multi-GPU, split add a dimension
+                if len(start_positions.size()) > 1:
+                    start_positions = start_positions.squeeze(-1)
+                if len(end_positions.size()) > 1:
+                    end_positions = end_positions.squeeze(-1)
+                # sometimes the start/end positions are outside our model inputs, we ignore these terms
+                ignored_index = start_logits.size(1)
+                start_positions = start_positions.clamp(0, ignored_index)
+                end_positions = end_positions.clamp(0, ignored_index)
+
+                loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+                start_loss = loss_fct(start_logits, start_positions)
+                end_loss = loss_fct(end_logits, end_positions)
+                total_loss = (start_loss + end_loss) / 2
+
+            if not return_dict:
+                output = (start_logits, end_logits) + outputs[2:]
+                return ((total_loss,) + output) if total_loss is not None else output
+
+            return QuestionAnsweringModelOutput(
+                loss=total_loss,
+                start_logits=start_logits,
+                end_logits=end_logits,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            )
diff --git a/model/peft/tuners/__init__.py b/model/peft/tuners/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3d5653bf165ec1a647e99b74fe8c9d9b960f78d
--- /dev/null
+++ b/model/peft/tuners/__init__.py
@@ -0,0 +1,27 @@
+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all
+
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .adaption_prompt import AdaptionPromptConfig, AdaptionPromptModel
+from .lora import LoraConfig, LoraModel
+from .adalora import AdaLoraConfig, AdaLoraModel
+from .p_tuning import PromptEncoder, PromptEncoderConfig, PromptEncoderReparameterizationType
+from .prefix_tuning import PrefixEncoder, PrefixTuningConfig
+from .prompt_tuning import PromptEmbedding, PromptTuningConfig, PromptTuningInit
+from .moelora import MoeLoraConfig, MoeLoraModel
+from .gating import GATING_TO_MODEL_MAPPING
diff --git a/model/peft/tuners/adalora.py b/model/peft/tuners/adalora.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff877adacedd92ca13601fbd829459a50afdbc1
--- /dev/null
+++ b/model/peft/tuners/adalora.py
@@ -0,0 +1,751 @@
+import re
+import warnings
+from dataclasses import dataclass, field
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.pytorch_utils import Conv1D
+
+from ..import_utils import is_bnb_4bit_available, is_bnb_available
+from ..utils import (
+    TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING,
+    PeftType,
+    _freeze_adapter,
+    _get_submodules,
+    transpose,
+)
+from .lora import (
+    LoraConfig,
+    LoraLayer,
+    LoraModel,
+    mark_only_lora_as_trainable,
+)
+
+
+if is_bnb_available():
+    import bitsandbytes as bnb
+
+
+@dataclass
+class AdaLoraConfig(LoraConfig):
+    """
+    This is the configuration class to store the configuration of a [`~peft.AdaLora`].
+
+    Args:
+        target_r (`int`): The target average rank of incremental matrix.
+        init_r (`int`): The initial rank for each incremental matrix.
+        tinit (`int`): The steps of initial fine-tuning warmup.
+        tfinal (`int`): The step of final fine-tuning.
+        deltaT (`int`): The time internval between two budget allocations.
+        beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing.
+        beta2 (`float`): The hyperparameter of EMA for undertainty quantification.
+        orth_reg_weight (`float`): The coefficient of orthogonal regularization.
+        total_step (`int`): The total training steps that should be specified before training.
+        rank_pattern (`list`): The allocated rank for each weight matrix by RankAllocator.
+    """
+
+    target_r: int = field(default=8, metadata={"help": "Target Lora matrix dimension."})
+    init_r: int = field(default=12, metadata={"help": "Intial Lora matrix dimension."})
+    tinit: int = field(default=0, metadata={"help": "The steps of initial warmup."})
+    tfinal: int = field(default=0, metadata={"help": "The steps of final warmup."})
+    deltaT: int = field(default=1, metadata={"help": "Step interval of rank allocation."})
+    beta1: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."})
+    beta2: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."})
+    orth_reg_weight: float = field(default=0.5, metadata={"help": "The orthogonal regularization coefficient."})
+    total_step: Optional[int] = field(default=None, metadata={"help": "The total training steps."})
+    rank_pattern: Optional[dict] = field(default=None, metadata={"help": "The saved rank pattern."})
+
+    def __post_init__(self):
+        self.peft_type = PeftType.ADALORA
+
+
+class AdaLoraModel(LoraModel):
+    """
+    Creates AdaLoRA (Adaptive LoRA) model from a pretrained transformers model. Paper:
+    https://openreview.net/pdf?id=lq62uWRJjiY
+
+    Args:
+        model ([`transformers.PreTrainedModel`]): The model to be adapted.
+        config ([`AdaLoraConfig`]): The configuration of the AdaLora model.
+
+    Returns:
+        `torch.nn.Module`: The AdaLora model.
+
+    Example::
+
+        >>> from transformers import AutoModelForSeq2SeqLM, LoraConfig >>> from peft import AdaLoraModel, AdaLoraConfig
+        >>> config = AdaLoraConfig(
+                peft_type="ADALORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"],
+                lora_dropout=0.01,
+            )
+        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> model = AdaLoraModel(config, model)
+
+    **Attributes**:
+        - **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted.
+        - **peft_config** ([`AdaLoraConfig`]): The configuration of the AdaLora model.
+    """
+
+    def __init__(self, model, config, adapter_name):
+        nn.Module.__init__(self)
+        self.model = model
+        self.peft_config = config
+        self.add_adapter(adapter_name, self.peft_config[adapter_name])
+
+    def add_adapter(self, adapter_name, config=None):
+        if config is not None:
+            model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config
+            config = self._prepare_adalora_config(config, model_config)
+            self.peft_config[adapter_name] = config
+        self._find_and_replace(adapter_name)
+        if len(self.peft_config) > 1 and self.peft_config[adapter_name].bias != "none":
+            raise ValueError(
+                "AdaLoraModel supports only 1 adapter with bias. When using multiple adapters, set bias to 'none' for all adapters."
+            )
+        traininable_mode_counter = 0
+        for config in self.peft_config.values():
+            if not config.inference_mode:
+                traininable_mode_counter += 1
+
+        if traininable_mode_counter > 1:
+            raise ValueError(
+                "AdaLoraModel supports only 1 trainable adapter. "
+                "When using multiple adapters, set inference_mode to True for all adapters except the one you want to train."
+            )
+
+        mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
+        if self.peft_config[adapter_name].inference_mode:
+            _freeze_adapter(self.model, adapter_name)
+        else:
+            self.trainable_adapter_name = adapter_name
+            self.rankallocator = RankAllocator(self.model, self.peft_config[adapter_name], self.trainable_adapter_name)
+
+    def _find_and_replace(self, adapter_name):
+        lora_config = self.peft_config[adapter_name]
+        loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
+        loaded_in_4bit = getattr(self.model, "is_loaded_in_4bit", False)
+
+        if (loaded_in_8bit or loaded_in_4bit) and not is_bnb_available():
+            raise ImportError(
+                "To use Lora with 8-bit quantization, please install the `bitsandbytes` package. "
+                "You can install it with `pip install bitsandbytes`."
+            )
+        is_target_modules_in_base_model = False
+        kwargs = {
+            "r": lora_config.init_r,
+            "lora_alpha": lora_config.lora_alpha,
+            "lora_dropout": lora_config.lora_dropout,
+            "fan_in_fan_out": lora_config.fan_in_fan_out,
+            "init_lora_weights": lora_config.init_lora_weights,
+        }
+        key_list = [key for key, _ in self.model.named_modules()]
+        for key in key_list:
+            if isinstance(lora_config.target_modules, str):
+                target_module_found = re.fullmatch(lora_config.target_modules, key)
+            else:
+                target_module_found = any(key.endswith(target_key) for target_key in lora_config.target_modules)
+            if target_module_found:
+                if not is_target_modules_in_base_model:
+                    is_target_modules_in_base_model = True
+                parent, target, target_name = _get_submodules(self.model, key)
+                bias = target.bias is not None
+                if isinstance(target, LoraLayer):
+                    target.update_layer(
+                        adapter_name,
+                        lora_config.init_r,
+                        lora_config.lora_alpha,
+                        lora_config.lora_dropout,
+                        lora_config.init_lora_weights,
+                    )
+                else:
+                    if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt):
+                        kwargs.update(
+                            {
+                                "has_fp16_weights": target.state.has_fp16_weights,
+                                "memory_efficient_backward": target.state.memory_efficient_backward,
+                                "threshold": target.state.threshold,
+                                "index": target.index,
+                            }
+                        )
+                        new_module = SVDLinear8bitLt(
+                            adapter_name, target.in_features, target.out_features, bias=bias, **kwargs
+                        )
+                    elif loaded_in_4bit and is_bnb_4bit_available() and isinstance(target, bnb.nn.Linear4bit):
+                        fourbit_kwargs = kwargs.copy()
+                        fourbit_kwargs.update(
+                            {
+                                "compute_dtype": target.compute_dtype,
+                                "compress_statistics": target.weight.compress_statistics,
+                                "quant_type": target.weight.quant_type,
+                            }
+                        )
+                        new_module = SVDLinear4bit(
+                            adapter_name, target.in_features, target.out_features, bias=bias, **fourbit_kwargs
+                        )
+                    else:
+                        if isinstance(target, torch.nn.Linear):
+                            in_features, out_features = target.in_features, target.out_features
+                            if kwargs["fan_in_fan_out"]:
+                                warnings.warn(
+                                    "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. "
+                                    "Setting fan_in_fan_out to False."
+                                )
+                                kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = False
+                        elif isinstance(target, Conv1D):
+                            in_features, out_features = (
+                                target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape
+                            )
+                            if not kwargs["fan_in_fan_out"]:
+                                warnings.warn(
+                                    "fan_in_fan_out is set to False but the target module is `Conv1D`. "
+                                    "Setting fan_in_fan_out to True."
+                                )
+                                kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = True
+                        else:
+                            raise ValueError(
+                                f"Target module {target} is not supported. "
+                                f"Currently, only `torch.nn.Linear` and `Conv1D` are supported."
+                            )
+                        new_module = SVDLinear(adapter_name, in_features, out_features, bias=bias, **kwargs)
+
+                    self._replace_module(parent, target_name, new_module, target)
+        if not is_target_modules_in_base_model:
+            raise ValueError(
+                f"Target modules {lora_config.target_modules} not found in the base model. "
+                f"Please check the target modules and try again."
+            )
+
+    def __getattr__(self, name: str):
+        """Forward missing attributes to the wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            return getattr(self.model, name)
+
+    def forward(self, *args, **kwargs):
+        outputs = self.model.forward(*args, **kwargs)
+
+        # Calculate the orthogonal regularization
+        orth_reg_weight = self.peft_config[self.trainable_adapter_name].orth_reg_weight
+        assert orth_reg_weight > 0
+
+        if hasattr(outputs, "loss"):
+            regu_loss = 0
+            num_param = 0
+            for n, p in self.model.named_parameters():
+                if ("lora_A" in n or "lora_B" in n) and self.trainable_adapter_name in n:
+                    para_cov = p @ p.T if "lora_A" in n else p.T @ p
+                    I = torch.eye(*para_cov.size(), out=torch.empty_like(para_cov))
+                    I.requires_grad = False
+                    num_param += 1
+                    regu_loss += torch.norm(para_cov - I, p="fro")
+            if num_param > 0:
+                regu_loss = regu_loss / num_param
+            else:
+                regu_loss = 0
+            outputs.loss += orth_reg_weight * regu_loss
+        return outputs
+
+    def resize_modules_by_rank_pattern(self, rank_pattern, adapter_name):
+        lora_config = self.peft_config[adapter_name]
+        for name, rank_idx in rank_pattern.items():
+            if isinstance(rank_idx, list):
+                rank = sum(rank_idx)
+            elif isinstance(rank_idx, torch.Tensor):
+                rank_idx = rank_idx.view(-1)
+                rank = rank_idx.sum().item()
+            else:
+                raise ValueError("Unexcepted type of rank_idx")
+            key = ".".join(name.split(".")[0:-2]) if adapter_name in name else ".".join(name.split(".")[0:-1])
+            _, target, _ = _get_submodules(self.model, key)
+            lora_E_weights = target.lora_E[adapter_name][rank_idx]
+            lora_A_weights = target.lora_A[adapter_name][rank_idx]
+            lora_B_weights = target.lora_B[adapter_name][:, rank_idx]
+            ranknum = target.ranknum[adapter_name]
+            target.update_layer(
+                adapter_name,
+                rank,
+                lora_config.lora_alpha,
+                lora_config.lora_dropout,
+                lora_config.init_lora_weights,
+            )
+            with torch.no_grad():
+                if rank > 0:
+                    target.lora_E[adapter_name].copy_(lora_E_weights)
+                    target.lora_A[adapter_name].copy_(lora_A_weights)
+                    target.lora_B[adapter_name].copy_(lora_B_weights)
+                    # The scaling is exactly as the previous
+                    target.ranknum[adapter_name].copy_(ranknum)
+
+    def resize_state_dict_by_rank_pattern(self, rank_pattern, state_dict, adapter_name):
+        for name, rank_idx in rank_pattern.items():
+            rank = sum(rank_idx)
+            prefix = ".".join(name.split(".")[0:-2]) if adapter_name in name else ".".join(name.split(".")[0:-1])
+            for layer in ["lora_E", "lora_A", "lora_B"]:
+                key = f"base_model.model.{prefix}.{layer}.{adapter_name}"
+                if layer != "lora_B":
+                    state_dict[key] = (
+                        state_dict[key][rank_idx] if rank != state_dict[key].shape[0] else state_dict[key]
+                    )
+                else:
+                    state_dict[key] = (
+                        state_dict[key][:, rank_idx] if rank != state_dict[key].shape[1] else state_dict[key]
+                    )
+        return state_dict
+
+    def update_and_allocate(self, global_step):
+        lora_config = self.peft_config[self.trainable_adapter_name]
+        # Update the importance score and allocate the budget
+        if global_step < lora_config.total_step - lora_config.tfinal:
+            _, rank_pattern = self.rankallocator.update_and_allocate(self.model, global_step)
+            if rank_pattern:
+                lora_config.rank_pattern = rank_pattern
+        # Finalize the budget allocation
+        elif global_step == lora_config.total_step - lora_config.tfinal:
+            _, rank_pattern = self.rankallocator.update_and_allocate(self.model, global_step, force_mask=True)
+            # for some reason, this freezes the trainable parameters and nothing gets updates
+            # self.resize_modules_by_rank_pattern(rank_pattern, self.trainable_adapter_name)
+            lora_config.rank_pattern = rank_pattern
+            self.rankallocator.reset_ipt()
+        # Currently using inefficient way to mask the unimportant weights using the rank pattern
+        #  due to problem mentioned above
+        elif global_step > lora_config.total_step - lora_config.tfinal:
+            self.rankallocator.mask_using_rank_pattern(self.model, lora_config.rank_pattern)
+        # Pass the function and do forward propagation
+        else:
+            return None
+
+    @staticmethod
+    def _prepare_adalora_config(peft_config, model_config):
+        if peft_config.target_modules is None:
+            if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING:
+                raise ValueError("Please specify `target_modules` in `peft_config`")
+            peft_config.target_modules = TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING[
+                model_config["model_type"]
+            ]
+        return peft_config
+
+
+class AdaLoraLayer(LoraLayer):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+    ):
+        super().__init__(in_features, out_features)
+        self.lora_E = nn.ParameterDict({})
+        self.lora_A = nn.ParameterDict({})
+        self.lora_B = nn.ParameterDict({})
+        self.ranknum = nn.ParameterDict({})
+
+    def update_layer(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights):
+        self.r[adapter_name] = r
+        self.lora_alpha[adapter_name] = lora_alpha
+        if lora_dropout > 0.0:
+            lora_dropout_layer = nn.Dropout(p=lora_dropout)
+        else:
+
+            def lora_dropout_layer(x):
+                return x
+
+        self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
+        # Actual trainable parameters
+        # Right singular vectors
+        self.lora_A.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(r, self.in_features))}))
+        # Singular values
+        self.lora_E.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(r, 1))}))
+        # Left singular vectors
+        self.lora_B.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(self.out_features, r))}))
+        # The current rank
+        self.ranknum.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(1), requires_grad=False)}))
+        self.ranknum[adapter_name].data.fill_(float(r))
+        self.ranknum[adapter_name].requires_grad = False
+        self.scaling[adapter_name] = lora_alpha if lora_alpha > 0 else float(r)
+        if init_lora_weights:
+            self.reset_lora_parameters(adapter_name)
+        self.to(self.weight.device)
+
+    def reset_lora_parameters(self, adapter_name):
+        if adapter_name in self.lora_A.keys():
+            nn.init.zeros_(self.lora_E[adapter_name])
+            nn.init.normal_(self.lora_A[adapter_name], mean=0.0, std=0.02)
+            nn.init.normal_(self.lora_B[adapter_name], mean=0.0, std=0.02)
+
+
+class SVDLinear(nn.Linear, AdaLoraLayer):
+    # SVD-based adaptation by a dense layer
+    def __init__(
+        self,
+        adapter_name: str,
+        in_features: int,
+        out_features: int,
+        r: int = 0,
+        lora_alpha: int = 1,
+        lora_dropout: float = 0.0,
+        fan_in_fan_out: bool = False,
+        **kwargs,
+    ):
+        init_lora_weights = kwargs.pop("init_lora_weights", True)
+        nn.Linear.__init__(self, in_features, out_features, **kwargs)
+        AdaLoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+        # Freezing the pre-trained weight matrix
+        self.weight.requires_grad = False
+
+        self.fan_in_fan_out = fan_in_fan_out
+        if fan_in_fan_out:
+            self.weight.data = self.weight.data.T
+
+        nn.Linear.reset_parameters(self)
+        self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+        self.active_adapter = adapter_name
+
+    def merge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if self.merged:
+            warnings.warn("Already merged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data += (
+                transpose(
+                    self.lora_B[self.active_adapter]
+                    @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]),
+                    self.fan_in_fan_out,
+                )
+                * self.scaling[self.active_adapter]
+                / (self.ranknum[self.active_adapter] + 1e-5)
+            )
+            self.merged = True
+
+    def unmerge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if not self.merged:
+            warnings.warn("Already unmerged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data -= (
+                transpose(
+                    self.lora_B[self.active_adapter]
+                    @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter])
+                )
+                * self.scaling[self.active_adapter]
+                / (self.ranknum[self.active_adapter] + 1e-5)
+            )
+            self.merged = False
+
+    def forward(self, x: torch.Tensor):
+        if self.active_adapter not in self.lora_A.keys():
+            return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+        if self.disable_adapters:
+            if self.r[self.active_adapter] > 0 and self.merged:
+                self.unmerge()
+            result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+        elif self.r[self.active_adapter] > 0 and not self.merged:
+            result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+            result += (
+                (
+                    self.lora_dropout[self.active_adapter](x)
+                    @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T
+                    @ self.lora_B[self.active_adapter].T
+                )
+                * self.scaling[self.active_adapter]
+                / (self.ranknum[self.active_adapter] + 1e-5)
+            )
+        else:
+            result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+        return result
+
+
+if is_bnb_available():
+
+    class SVDLinear8bitLt(bnb.nn.Linear8bitLt, AdaLoraLayer):
+        # Low-rank matrix for SVD-based adaptation
+        def __init__(
+            self,
+            adapter_name,
+            in_features,
+            out_features,
+            r: int = 0,
+            lora_alpha: int = 1,
+            lora_dropout: float = 0.0,
+            **kwargs,
+        ):
+            bnb.nn.Linear8bitLt.__init__(
+                self,
+                in_features,
+                out_features,
+                bias=kwargs.get("bias", True),
+                has_fp16_weights=kwargs.get("has_fp16_weights", True),
+                memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
+                threshold=kwargs.get("threshold", 0.0),
+                index=kwargs.get("index", None),
+            )
+            AdaLoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+            # Freezing the pre-trained weight matrix
+            self.weight.requires_grad = False
+
+            init_lora_weights = kwargs.pop("init_lora_weights", True)
+            self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+            self.active_adapter = adapter_name
+
+        def forward(self, x: torch.Tensor):
+            result = super().forward(x)
+
+            if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
+                return result
+            elif self.r[self.active_adapter] > 0:
+                if not torch.is_autocast_enabled():
+                    expected_dtype = result.dtype
+
+                    if x.dtype != torch.float32:
+                        x = x.float()
+                    output = (
+                        (
+                            self.lora_dropout[self.active_adapter](x)
+                            @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T
+                            @ self.lora_B[self.active_adapter].T
+                        ).to(expected_dtype)
+                        * self.scaling[self.active_adapter]
+                        / (self.ranknum[self.active_adapter] + 1e-5)
+                    )
+                else:
+                    output = (
+                        (
+                            self.lora_dropout[self.active_adapter](x)
+                            @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T
+                            @ self.lora_B[self.active_adapter].T
+                        )
+                        * self.scaling[self.active_adapter]
+                        / (self.ranknum[self.active_adapter] + 1e-5)
+                    )
+                result = result + output
+            return result
+
+
+if is_bnb_4bit_available():
+
+    class SVDLinear4bit(bnb.nn.Linear4bit, AdaLoraLayer):
+        # Low-rank matrix for SVD-based adaptation
+        def __init__(
+            self,
+            adapter_name,
+            in_features,
+            out_features,
+            r: int = 0,
+            lora_alpha: int = 1,
+            lora_dropout: float = 0.0,
+            **kwargs,
+        ):
+            bnb.nn.Linear4bit.__init__(
+                self,
+                in_features,
+                out_features,
+                bias=kwargs.get("bias", True),
+                compute_dtype=kwargs.get("compute_dtype", torch.float32),
+                compress_statistics=kwargs.get("compress_statistics", True),
+                quant_type=kwargs.get("quant_type", "nf4"),
+            )
+            AdaLoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+            # Freezing the pre-trained weight matrix
+            self.weight.requires_grad = False
+
+            init_lora_weights = kwargs.pop("init_lora_weights", True)
+            self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+            self.active_adapter = adapter_name
+
+        def forward(self, x: torch.Tensor):
+            result = super().forward(x)
+
+            if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
+                return result
+            elif self.r[self.active_adapter] > 0:
+                if not torch.is_autocast_enabled():
+                    expected_dtype = result.dtype
+
+                    if x.dtype != torch.float32:
+                        x = x.float()
+                    output = (
+                        (
+                            self.lora_dropout[self.active_adapter](x)
+                            @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T
+                            @ self.lora_B[self.active_adapter].T
+                        ).to(expected_dtype)
+                        * self.scaling[self.active_adapter]
+                        / (self.ranknum[self.active_adapter] + 1e-5)
+                    )
+                else:
+                    output = (
+                        (
+                            self.lora_dropout[self.active_adapter](x)
+                            @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T
+                            @ self.lora_B[self.active_adapter].T
+                        )
+                        * self.scaling[self.active_adapter]
+                        / (self.ranknum[self.active_adapter] + 1e-5)
+                    )
+                result = result + output
+            return result
+
+
+class RankAllocator(object):
+    """
+    The RankAllocator for AdaLoraModel. Paper: https://openreview.net/pdf?id=lq62uWRJjiY
+
+    Args:
+        config ([`AdaLoraConfig`]): The configuration of the AdaLora model.
+        model: the model that we apply AdaLoRA to.
+
+    """
+
+    def __init__(self, model, peft_config, adapter_name):
+        self.peft_config = peft_config
+        self.adapter_name = adapter_name
+        self.beta1 = peft_config.beta1
+        self.beta2 = peft_config.beta2
+        assert self.beta1 > 0 and self.beta1 < 1
+        assert self.beta2 > 0 and self.beta2 < 1
+
+        self.reset_ipt()
+        self._set_budget_scheduler(model)
+
+    def set_total_step(self, total_step):
+        self.peft_config.total_step = total_step
+
+    def reset_ipt(self):
+        self.ipt = {}
+        self.exp_avg_ipt = {}
+        self.exp_avg_unc = {}
+
+    def _set_budget_scheduler(self, model):
+        self.init_bgt = 0
+        self.name_set = set()
+        for n, p in model.named_parameters():
+            if f"lora_A.{self.adapter_name}" in n:
+                self.init_bgt += p.size(0)
+                self.name_set.add(n.replace("lora_A", "%s"))
+        self.name_set = sorted(self.name_set)
+        # The total final rank budget
+        self.target_bgt = self.peft_config.target_r * len(self.name_set)
+
+    def budget_schedule(self, step: int):
+        tinit = self.peft_config.tinit
+        tfinal = self.peft_config.tfinal
+        total_step = self.peft_config.total_step
+        # Initial warmup
+        if step <= tinit:
+            budget = self.init_bgt
+            mask_ind = False
+        # Final fine-tuning
+        elif step > total_step - tfinal:
+            budget = self.target_bgt
+            mask_ind = True
+        else:
+            # Budget decreasing with a cubic scheduler
+            mul_coeff = 1 - (step - tinit) / (total_step - tfinal - tinit)
+            budget = int((self.init_bgt - self.target_bgt) * (mul_coeff**3) + self.target_bgt)
+            mask_ind = True if step % self.peft_config.deltaT == 0 else False
+        return budget, mask_ind
+
+    def update_ipt(self, model):
+        # Update the sensitivity and uncertainty for every weight
+        for n, p in model.named_parameters():
+            if "lora_" in n and self.adapter_name in n:
+                if n not in self.ipt:
+                    self.ipt[n] = torch.zeros_like(p)
+                    self.exp_avg_ipt[n] = torch.zeros_like(p)
+                    self.exp_avg_unc[n] = torch.zeros_like(p)
+                with torch.no_grad():
+                    self.ipt[n] = (p * p.grad).abs().detach()
+                    # Sensitivity smoothing
+                    self.exp_avg_ipt[n] = self.beta1 * self.exp_avg_ipt[n] + (1 - self.beta1) * self.ipt[n]
+                    # Uncertainty quantification
+                    self.exp_avg_unc[n] = (
+                        self.beta2 * self.exp_avg_unc[n] + (1 - self.beta2) * (self.ipt[n] - self.exp_avg_ipt[n]).abs()
+                    )
+
+    def _element_score(self, n):
+        return self.exp_avg_ipt[n] * self.exp_avg_unc[n]
+
+    def _combine_ipt(self, ipt_E, ipt_AB):
+        ipt_AB = ipt_AB.sum(dim=1, keepdim=False)
+        sum_ipt = ipt_E.view(-1) + ipt_AB.view(-1)
+        return sum_ipt
+
+    def mask_to_budget(self, model, budget):
+        value_ipt = {}
+        vector_ipt = {}
+        triplet_ipt = {}
+        # Get the importance score for A, E, B
+        for n, p in model.named_parameters():
+            if f"lora_A.{self.adapter_name}" in n:
+                entry_ipt = self._element_score(n)
+                comb_ipt = torch.mean(entry_ipt, dim=1, keepdim=True)
+                name_m = n.replace("lora_A", "%s")
+                if name_m not in vector_ipt:
+                    vector_ipt[name_m] = [comb_ipt]
+                else:
+                    vector_ipt[name_m].append(comb_ipt)
+            if f"lora_B.{self.adapter_name}" in n:
+                entry_ipt = self._element_score(n)
+                comb_ipt = torch.mean(entry_ipt, dim=0, keepdim=False).view(-1, 1)
+                name_m = n.replace("lora_B", "%s")
+                if name_m not in vector_ipt:
+                    vector_ipt[name_m] = [comb_ipt]
+                else:
+                    vector_ipt[name_m].append(comb_ipt)
+            if f"lora_E.{self.adapter_name}" in n:
+                entry_ipt = self._element_score(n)
+                name_m = n.replace("lora_E", "%s")
+                value_ipt[name_m] = entry_ipt
+
+        all_score = []
+        # Calculate the score for each triplet
+        for name_m in vector_ipt:
+            ipt_E = value_ipt[name_m]
+            ipt_AB = torch.cat(vector_ipt[name_m], dim=1)
+            sum_ipt = self._combine_ipt(ipt_E, ipt_AB)
+            name_E = name_m % "lora_E"
+            triplet_ipt[name_E] = sum_ipt.view(-1, 1)
+            all_score.append(sum_ipt.view(-1))
+
+        # Get the threshold by ranking ipt
+        mask_threshold = torch.kthvalue(
+            torch.cat(all_score),
+            k=self.init_bgt - budget,
+        )[0].item()
+
+        rank_pattern = {}
+        # Mask the unimportant triplets
+        with torch.no_grad():
+            for n, p in model.named_parameters():
+                if f"lora_E.{self.adapter_name}" in n:
+                    p.masked_fill_(triplet_ipt[n] <= mask_threshold, 0.0)
+                    rank_pattern[n] = (~(triplet_ipt[n] <= mask_threshold)).view(-1).tolist()
+        return rank_pattern
+
+    def update_and_allocate(self, model, global_step, force_mask=False):
+        # # Update the importance score and allocate the budget
+        if global_step < self.peft_config.total_step - self.peft_config.tfinal:
+            self.update_ipt(model)
+        budget, mask_ind = self.budget_schedule(global_step)
+        # Allocate the budget according to importance scores
+        if mask_ind or force_mask:
+            rank_pattern = self.mask_to_budget(model, budget)
+        else:
+            rank_pattern = None
+        return budget, rank_pattern
+
+    def mask_using_rank_pattern(self, model, rank_pattern):
+        # Mask the unimportant triplets
+        is_adapter_name_truncated = False
+        if self.adapter_name not in next(iter(rank_pattern.keys())):
+            is_adapter_name_truncated = True
+
+        with torch.no_grad():
+            for n, p in model.named_parameters():
+                if f"lora_E.{self.adapter_name}" in n:
+                    key = n if not is_adapter_name_truncated else n.replace(f".{self.adapter_name}", "")
+                    mask = torch.Tensor(rank_pattern[key]).unsqueeze(-1).to(p.device)
+                    p.masked_fill_(~mask.bool(), 0.0)
diff --git a/model/peft/tuners/adaption_prompt.py b/model/peft/tuners/adaption_prompt.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ce7d8b7fa9c19fb0a69904762551555e972b7ac
--- /dev/null
+++ b/model/peft/tuners/adaption_prompt.py
@@ -0,0 +1,368 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections import namedtuple
+from dataclasses import dataclass, field
+from typing import Dict, List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from peft.utils.config import PeftConfig, PeftType
+from peft.utils.other import _freeze_adapter, _get_submodules
+
+
+def llama_rotate_half(x: torch.Tensor) -> torch.Tensor:
+    """
+    Rotate half the hidden dims of the input.
+
+    This function was duplicated verbatim from:
+    https://github.com/huggingface/transformers/blob/1de8ce9ee1191ba761a593ac15d9ccbf5851bfc5/src/transformers/models/llama/modeling_llama.py#L126
+
+    This was done to eliminate the Llama transformers implementation as a dependency of this file. Note that some other
+    functions were also adapted from the transformers implementation but were modified.
+    """
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def llama_apply_rotary_pos_emb(q, cos, sin, position_ids):
+    """
+    Apply rotary position embedding to query states in the Llama model.
+
+    This function was adapted from:
+    https://github.com/huggingface/transformers/blob/1de8ce9ee1191ba761a593ac15d9ccbf5851bfc5/src/transformers/models/llama/modeling_llama.py#L133
+
+    It was modified to remove unnecessary processing of key states.
+    """
+    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
+    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
+    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    q_embed = (q * cos) + (llama_rotate_half(q) * sin)
+    return q_embed
+
+
+def llama_compute_query_states(model: nn.Module, **kwargs) -> torch.Tensor:
+    """
+    Compute query states for Llama models specifically.
+
+    They need to be recomputed as the forward() method of the original LlamaModel in the transformers library does not
+    return them. See the related discussion in the PR: https://github.com/huggingface/peft/pull/268
+    """
+    hidden_states = kwargs.get("hidden_states")
+    position_ids = kwargs.get("position_ids")
+    past_key_value = kwargs.get("past_key_value")
+    bsz, q_len, _ = hidden_states.size()
+    query_states = model.q_proj(hidden_states).view(bsz, q_len, model.num_heads, model.head_dim).transpose(1, 2)
+    value_states = model.v_proj(hidden_states).view(bsz, q_len, model.num_heads, model.head_dim).transpose(1, 2)
+
+    seq_len = q_len
+    if past_key_value is not None:
+        seq_len += past_key_value[0].shape[-2]
+    cos, sin = model.rotary_emb(value_states, seq_len=seq_len)
+
+    return llama_apply_rotary_pos_emb(query_states, cos, sin, position_ids)
+
+
+# Contains the config that is specific to a transformers model type.
+ModelTypeConfig = namedtuple(
+    "ModelTypeConfig", ["compute_query_states", "target_modules", "k_proj_layer", "v_proj_layer", "o_proj_layer"]
+)
+# Mapping of transformers model types to their specific configuration.
+TRANSFORMERS_MODEL_CONFIG = {
+    "llama": ModelTypeConfig(
+        compute_query_states=llama_compute_query_states,
+        target_modules="self_attn",
+        k_proj_layer="k_proj",
+        v_proj_layer="v_proj",
+        o_proj_layer="o_proj",
+    ),
+}
+
+
+def is_adaption_prompt_trainable(params: str) -> bool:
+    """Return True if module is trainable under adaption prompt fine-tuning."""
+    return params.split(".")[-1].startswith("adaption_")
+
+
+@dataclass
+class AdaptionPromptConfig(PeftConfig):
+    """Stores the configuration of an [`AdaptionPromptModel`]."""
+
+    target_modules: str = field(
+        default=None, metadata={"help": "Name of the attention submodules to insert adaption prompts into."}
+    )
+    adapter_len: int = field(default=None, metadata={"help": "Number of adapter tokens to insert"})
+    adapter_layers: int = field(default=None, metadata={"help": "Number of adapter layers (from the top)"})
+
+    def __post_init__(self):
+        self.peft_type = PeftType.ADAPTION_PROMPT
+
+
+def prepare_config(
+    peft_config: AdaptionPromptConfig,
+    model,
+) -> AdaptionPromptConfig:
+    """Prepare the config based on the llama model type."""
+    if model.config.model_type not in TRANSFORMERS_MODEL_CONFIG:
+        raise ValueError("Unsupported model type for adaption prompt: '{model.config.model_type}'.")
+
+    model_config = TRANSFORMERS_MODEL_CONFIG[model.config.model_type]
+
+    if peft_config.target_modules is None:
+        peft_config.target_modules = model_config.target_modules
+
+    return peft_config
+
+
+class AdaptionPromptModel(nn.Module):
+    """
+    Implements adaption prompts as described in https://arxiv.org/pdf/2303.16199.pdf.
+
+    The top L attention modules are replaced with AdaptedAttention modules that wrap the original ones, but insert
+    trainable prompts with gates (for zero init).
+
+    Notes on the multi-adapter pattern:
+    - We store the states of different adapters by keeping a dictionary of AdaptedAttention modules indexed by adapter
+      name.
+    - Every time we switch adapters, we remove the modules of the currently active adapter from the model, store them
+      in the dictionary, and replace them with the modules of the new adapter.
+    - To avoid duplicated and potentially inconsistent state, the currently active adapter is always removed from the
+      dictionary.
+    - Disabling the adapter would also result in the modules being removed from the model.
+    """
+
+    def __init__(self, model, configs: Dict, adapter_name: str):
+        super().__init__()
+        self.model = model
+        # Store adapter configs by name.
+        self._configs: Dict[str, AdaptionPromptConfig] = {}
+        # Store lists of the parents of the affected attention modules by adapter name.
+        # We keep references to the parents so we can swap the adapters in-and-out of the model.
+        self._parents: Dict[str, List[nn.Module]] = {}
+        # Store lists of cached AdaptedAttention modules by name.
+        self._cached_adapters: Dict[str, List] = {}
+        # The name of the currently active adapter.
+        self._active_adapter = None
+        # Whether the adapter is enabled.
+        self._enabled = True
+        self.forward = self.model.forward
+        self.add_adapter(adapter_name, configs[adapter_name])
+        self._mark_only_adaption_prompts_as_trainable()
+
+    def add_adapter(self, adapter_name: str, config: AdaptionPromptConfig) -> None:
+        """Add an adapter with the given name and config."""
+        config = prepare_config(config, self.model)
+        if adapter_name in self._configs:
+            raise ValueError(f"Adapter with name '{adapter_name}' already exists.")
+
+        parents = []
+        for name, _ in self.model.named_modules():
+            if name.endswith(config.target_modules):
+                par, _, _ = _get_submodules(self.model, name)
+                parents.append(par)
+        if len(parents) < config.adapter_layers:
+            raise ValueError(
+                f"Config specifies more adapter layers '{config.adapter_layers}'"
+                f" than the model has '{len(parents)}'."
+            )
+        # Note that if the target modules are not in Sequential, ModuleList, or
+        # some other PyTorch ordered container, the behavior is undefined as we
+        # assume here that the order of the modules is the same as the order of
+        # the transformer decoder layers.
+        parents = parents[-config.adapter_layers :]
+        self._parents[adapter_name] = parents
+
+        # It is only None during initialization.
+        # If it is disabled, we don't have to remove the modules.
+        if self._active_adapter is not None and self._enabled:
+            self._remove_adapted_attentions(self._active_adapter)
+        self._active_adapter = adapter_name
+        self._configs[adapter_name] = config
+        self._create_adapted_attentions(config, parents)
+        if not self._enabled:
+            self._remove_adapted_attentions(self._active_adapter)
+
+        if config.inference_mode:
+            _freeze_adapter(self.model, adapter_name)
+
+    def set_adapter(self, adapter_name: str) -> None:
+        """Set the model to use the adapter with the given name."""
+        if self._active_adapter == adapter_name:
+            return
+        if adapter_name not in self._configs:
+            raise ValueError(f"Adapter with name '{adapter_name}' does not exist.")
+
+        if self._enabled:
+            self._remove_adapted_attentions(self._active_adapter)
+            self._set_adapted_attentions(adapter_name)
+
+        self._active_adapter = adapter_name
+
+    def enable_adapter_layers(self):
+        """Enable adapter layers by swapping in cached AdaptedAttention modules."""
+        self._enabled = True
+        self._set_adapted_attentions(self._active_adapter)
+
+    def disable_adapter_layers(self):
+        """Disable adapter layers by swapping out AdaptedAttention modules."""
+        self._enabled = False
+        self._remove_adapted_attentions(self._active_adapter)
+
+    def _create_adapted_attentions(self, config: AdaptionPromptConfig, parents: List[nn.Module]) -> None:
+        """Wrap LlamaAttention modules with newly created AdaptedAttention modules."""
+        for par in parents:
+            attn = AdaptedAttention(
+                model_type=self.model.config.model_type,
+                adapter_len=config.adapter_len,
+                model=getattr(par, config.target_modules),
+            )
+            setattr(par, config.target_modules, attn)
+
+    def _set_adapted_attentions(self, adapter_name: str) -> None:
+        """Replace LlamaAttention modules with cached AdaptedAttention modules."""
+        cached = self._cached_adapters[adapter_name]
+        del self._cached_adapters[adapter_name]
+        config = self._configs[adapter_name]
+        for i, par in enumerate(self._parents[adapter_name]):
+            setattr(par, config.target_modules, cached[i])
+
+    def _remove_adapted_attentions(self, adapter_name: str) -> None:
+        """Remove AdaptedAttention modules from the model and store them in the cache."""
+        config = self._configs[adapter_name]
+        adapted_attentions = []
+        for par in self._parents[adapter_name]:
+            attn = getattr(par, config.target_modules)
+            adapted_attentions.append(attn)
+            setattr(par, config.target_modules, attn.model)
+        self._cached_adapters[adapter_name] = adapted_attentions
+
+    def _mark_only_adaption_prompts_as_trainable(self) -> None:
+        """Freeze all parameters of the model except the adaption prompts."""
+        for n, p in self.model.named_parameters():
+            if not is_adaption_prompt_trainable(n):
+                p.requires_grad = False
+
+    def __getattr__(self, name: str):
+        """Forward missing attributes to the wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            # This is necessary as e.g. causal models have various methods that we
+            # don't want to re-implement here.
+            return getattr(self.model, name)
+
+
+class AdaptedAttention(nn.Module):
+    """This module wraps a LLamaAttention module and injects adaption prompts."""
+
+    def __init__(self, model_type: str, adapter_len: int, model):
+        """
+        Initialize object.
+
+        Args:
+            model_type: The transformer model type. This is used to retrieve the right method to
+                compute query states.
+            adapter_len: The length of the adaption prompt to insert.
+            model: The original transformer attention module that is being wrapped.
+        """
+        assert not isinstance(model, AdaptedAttention)
+        super().__init__()
+        self.model_type = model_type
+        self.model = model
+        self.adapter_len = adapter_len
+        # Assume all parameters of the attention model we are wrapping are on the same device.
+        device = next(model.parameters()).device
+        # Don't think this was specified in the paper, but we follow the official repo which used an Embedding
+        # which initializes the tokens with standard normal values.
+        # https://github.com/ZrrSkywalker/LLaMA-Adapter/blob/41c3546fe1997ab8a65809dc8d8f9252b19d9faf/llama/model.py#L234
+        # (bsz, adapter_len, hidden_size)
+        target_dtype = (
+            model.q_proj.weight.dtype if model.q_proj.weight.dtype not in [torch.int8, torch.uint8] else torch.float32
+        )
+        self.adaption_prompt = nn.Parameter(
+            torch.empty(1, adapter_len, self.model.hidden_size, device=device, dtype=target_dtype).normal_()
+        )
+        # Initialize the gate to 0 as this is "zero-init".
+        self.adaption_gate = nn.Parameter(torch.zeros(1, device=device, dtype=target_dtype))
+
+    def forward(self, **kwargs):
+        """
+        Forward pass for the adapter which wraps the original LlamaAttention module.
+
+        "Official" paper implementation:
+        https://github.com/ZrrSkywalker/LLaMA-Adapter/blob/41c3546fe1997ab8a65809dc8d8f9252b19d9faf/llama/model.py#L141
+
+        Args:
+            kwargs: See the original LlamaAttention module.
+        """
+        if kwargs.get("output_attention", False):
+            raise NotImplementedError("output_attention is not currently supported.")
+
+        output, _, past_key_value = self.model(**kwargs)
+        bsz = output.shape[0]
+        q_len = output.shape[1]
+        embed_dim = output.shape[2]
+        k_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].k_proj_layer
+        v_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].v_proj_layer
+        o_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].o_proj_layer
+
+        if k_proj_layer == v_proj_layer:
+            _, key, value = getattr(self.model, k_proj_layer)(self.adaption_prompt).split(embed_dim, dim=2)
+        else:
+            key = getattr(self.model, k_proj_layer)(self.adaption_prompt)
+            value = getattr(self.model, v_proj_layer)(self.adaption_prompt)
+        # (bsz, num_heads, adapter_len, head_dim)
+        adapter_k = (
+            key.view(1, self.adapter_len, self.model.num_heads, self.model.head_dim)
+            .repeat(bsz, 1, 1, 1)
+            .transpose(1, 2)
+        )
+        # (bsz, num_heads, adapter_len, head_dim)
+        adapter_v = (
+            value.view(1, self.adapter_len, self.model.num_heads, self.model.head_dim)
+            .repeat(bsz, 1, 1, 1)
+            .transpose(1, 2)
+        )
+
+        # Recompute query states.
+        compute_query_states = TRANSFORMERS_MODEL_CONFIG[self.model_type].compute_query_states
+        # (bsz, num_heads, q_len, head_dim)
+        query_states = compute_query_states(model=self.model, **kwargs)
+
+        previous_dtype = query_states.dtype
+        # (bsz, num_heads, q_len, adapter_len)
+        scores = torch.matmul(query_states, adapter_k.transpose(2, 3).to(previous_dtype)) / math.sqrt(
+            self.model.head_dim
+        )
+        # Upcast attention to fp32
+        # (bsz, num_heads, q_len, adapter_len)
+        scores = self.adaption_gate * F.softmax(scores, dim=-1, dtype=torch.float32).to(previous_dtype)
+        # (bsz, q_len, num_heads * head_dim)
+        adapter_output = torch.matmul(scores, adapter_v).transpose(1, 2).reshape(bsz, q_len, -1)
+        # (bsz, q_len, hidden_size)
+        if o_proj_layer is not None:
+            adapter_output = getattr(self.model, o_proj_layer)(adapter_output)
+
+        # Add adaption prompt output to original output.
+        output = output + adapter_output
+
+        # Restore original dtype.
+        output = output.to(previous_dtype)
+        return output, None, past_key_value
diff --git a/model/peft/tuners/debug_utils.py b/model/peft/tuners/debug_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fcfe26c39ee6a328bc0ecd10b132af0e6dece80
--- /dev/null
+++ b/model/peft/tuners/debug_utils.py
@@ -0,0 +1,64 @@
+import os
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+from typing import Optional
+
+class DebugUtils:
+    def __init__(self, pic_dir: str):
+        self.pic_dir = pic_dir
+        if not os.path.exists(self.pic_dir):
+            os.makedirs(self.pic_dir)
+
+    def check_orthogonal(self, tensor: torch.Tensor) -> float:
+        if tensor.dim() > 2:
+            raise ValueError("Tensor dimension must be 1 or 2.")
+
+        if tensor.dim() == 1:
+            return 0.0  # 1D tensor is not applicable for orthogonality
+
+        if tensor.dim() == 2:
+            num_rows, num_dims = tensor.shape
+            dot_products = torch.mm(tensor, tensor.t())
+            norms_squared = torch.diag(dot_products)
+            sum_abs_dot_products = torch.sum(torch.abs(dot_products - torch.diag(norms_squared)))
+            max_sum = (num_rows * (num_rows - 1)) / 2
+            orthogonality_score = 1.0 - (sum_abs_dot_products / max_sum)
+            return orthogonality_score.item()
+
+    def plot_tensor(self, tensor: torch.Tensor, pic_name: str, x_axis: Optional[str], y_axis: Optional[str], plot_number: bool = False) -> None:
+        if tensor.dim() > 2:
+            raise ValueError("Tensor dimensions must be 2 or lower for plotting.")
+        
+        fig, ax = plt.subplots()
+
+        if tensor.dim() == 1:
+            tensor_shape = tensor.shape[0]
+            ax.set_yticks([])
+            ax.set_xticks(np.arange(tensor_shape))
+            color_matrix = np.ones((1, tensor_shape, 3)) * [0.7, 0.7, 1.0]
+            ax.imshow(color_matrix, aspect='equal')
+            for i in range(1, tensor_shape):
+                ax.axvline(x=i - 0.5, color='black', linewidth=1)
+            if x_axis:
+                ax.set_xlabel(x_axis)
+        else:
+            heatmap = ax.imshow(tensor, cmap='YlOrRd', aspect='auto')
+
+            if plot_number:
+                for i in range(tensor.shape[0]):
+                    for j in range(tensor.shape[1]):
+                        ax.text(j, i, f'{tensor[i, j]:.3f}', ha='center', va='center', color='black')
+
+            if y_axis:
+                ax.set_ylabel(y_axis)
+            if x_axis:
+                ax.set_xlabel(x_axis)
+
+            plt.colorbar(heatmap, ax=ax, orientation='vertical')
+
+        plt.savefig(os.path.join(self.pic_dir, pic_name))
+        plt.close()
+
+
+debuge_utils = DebugUtils("./picture")
\ No newline at end of file
diff --git a/model/peft/tuners/gating.py b/model/peft/tuners/gating.py
new file mode 100644
index 0000000000000000000000000000000000000000..268048200b84fbfccff16d4cbc48761f05710acc
--- /dev/null
+++ b/model/peft/tuners/gating.py
@@ -0,0 +1,124 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+class Dense(nn.Module):
+    def __init__(self, dim: int, num_moe: int) -> None:
+        super().__init__()
+        self.dim = 64
+        self.num_moe = num_moe
+        self.linear_layer = nn.Linear(self.dim, num_moe, bias=False)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x):
+        logits = self.linear_layer(x)
+        probs = self.softmax(logits)
+        return probs
+
+class topK(nn.Module):
+    def __init__(self, dim: int, num_moe: int) -> None:
+        super().__init__()
+        self.dim = 64
+        self.num_moe = num_moe
+        self.linear_layer = nn.Linear(self.dim, num_moe, bias=False)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, topk=1):
+        logits = self.linear_layer(x)
+        probs = self.softmax(logits)
+        # 使用topk来选择最高的k个概率
+        topk_values, topk_indices = torch.topk(probs, k=topk, dim=-1)
+        # 创建一个初始值全为负无穷的张量，形状与probs相同
+        topk_probs = torch.full_like(probs, float('-inf'))
+        # 使用scatter填充topk的概率值
+        topk_probs = topk_probs.scatter_(-1, topk_indices, topk_values)
+        # 应用softmax确保top k值的和为1
+        topk_probs = self.softmax(topk_probs)
+        return topk_probs
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, num_moe: int, hidden_dim: int = 128) -> None:
+        super().__init__()
+        self.dim = 64
+        self.num_moe = num_moe
+        # 添加多层感知机结构
+        self.linear_layer1 = nn.Linear(self.dim, hidden_dim)
+        self.activation = nn.GELU()  # 使用GELU激活函数
+        self.linear_layer2 = nn.Linear(hidden_dim, self.num_moe)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x):
+        x = self.linear_layer1(x)
+        x = self.activation(x)
+        logits = self.linear_layer2(x)
+        probs = self.softmax(logits)
+        return probs
+    
+    
+class Noise(nn.Module):
+    def __init__(self, dim: int, num_moe: int, noise_std: float = 0.1) -> None:
+        super().__init__()
+        self.dim = 64
+        self.num_moe = num_moe
+        self.noise_std = noise_std
+        self.linear_layer = nn.Linear(self.dim, num_moe, bias=False)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x):
+        logits = self.linear_layer(x)
+        
+        # 添加噪声
+        noise = torch.randn_like(logits) * self.noise_std
+        logits = logits + noise
+        
+        probs = self.softmax(logits)
+        return probs
+    
+class MLP_noise(nn.Module):
+    def __init__(self, dim: int, num_moe: int, hidden_dim: int = 128, noise_std: float = 0.1) -> None:
+        super().__init__()
+        self.dim = 64
+        self.num_moe = num_moe
+        self.noise_std = noise_std
+        self.linear1 = nn.Linear(self.dim, hidden_dim, bias=False)
+        self.relu = nn.ReLU()
+        self.linear2 = nn.Linear(hidden_dim, num_moe, bias=False)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x):
+        hidden = self.linear1(x)
+        hidden = self.relu(hidden)
+        logits = self.linear2(hidden)
+        
+        # 添加噪声
+        noise = torch.randn_like(logits) * self.noise_std
+        logits = logits + noise
+        
+        probs = self.softmax(logits)
+        return probs
+
+    
+class Drop(nn.Module):
+    def __init__(self, dim: int, num_moe: int, dropout_rate: float = 0.1) -> None:
+        super().__init__()
+        self.dim = 64
+        self.num_moe = num_moe
+        self.linear_layer = nn.Linear(self.dim, num_moe, bias=False)
+        self.dropout = nn.Dropout(dropout_rate)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x):
+        logits = self.linear_layer(x)
+        # 添加Dropout
+        logits = self.dropout(logits)
+        probs = self.softmax(logits)
+        return probs
+    
+GATING_TO_MODEL_MAPPING = {
+    "Dense": Dense,
+    "topK": topK,
+    "MLP": MLP,
+    "Drop": Drop,
+    "MLP_noise": MLP_noise,
+    "Noise": Noise,
+}
\ No newline at end of file
diff --git a/model/peft/tuners/lora.py b/model/peft/tuners/lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..09f410989abcbde7a09948cf00ef58bb70b8a24f
--- /dev/null
+++ b/model/peft/tuners/lora.py
@@ -0,0 +1,1033 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+import re
+import warnings
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.pytorch_utils import Conv1D
+
+from ..import_utils import is_bnb_4bit_available, is_bnb_available
+from ..utils import (
+    COMMON_LAYERS_PATTERN,
+    TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
+    ModulesToSaveWrapper,
+    PeftConfig,
+    PeftType,
+    _freeze_adapter,
+    _get_submodules,
+    transpose,
+)
+
+
+if is_bnb_available():
+    import bitsandbytes as bnb
+
+
+@dataclass
+class LoraConfig(PeftConfig):
+    """
+    This is the configuration class to store the configuration of a [`LoraModel`].
+
+    Args:
+        r (`int`): Lora attention dimension.
+        target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
+        lora_alpha (`int`): The alpha parameter for Lora scaling.
+        lora_dropout (`float`): The dropout probability for Lora layers.
+        fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out).
+        For example, gpt-2 uses `Conv1D` which stores weights like (fan_in, fan_out) and hence this should be set to `True`.:
+        bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only'
+        modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
+            and saved in the final checkpoint.
+        layers_to_transform (`Union[List[int],int]`):
+            The layer indexes to transform, if this argument is specified, it will apply the LoRA transformations on
+            the layer indexes that are specified in this list. If a single integer is passed, it will apply the LoRA
+            transformations on the layer at this index.
+        layers_pattern (`str`):
+            The layer pattern name, used only if `layers_to_transform` is different from `None` and if the layer
+            pattern is not in the common layers pattern.
+    """
+
+    r: int = field(default=8, metadata={"help": "Lora attention dimension"})
+    target_modules: Optional[Union[List[str], str]] = field(
+        default=None,
+        metadata={
+            "help": "List of module names or regex expression of the module names to replace with Lora."
+            "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
+        },
+    )
+    lora_alpha: int = field(default=8, metadata={"help": "Lora alpha"})
+    lora_dropout: float = field(default=0.0, metadata={"help": "Lora dropout"})
+    fan_in_fan_out: bool = field(
+        default=False,
+        metadata={"help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)"},
+    )
+    bias: str = field(default="none", metadata={"help": "Bias type for Lora. Can be 'none', 'all' or 'lora_only'"})
+    modules_to_save: Optional[List[str]] = field(
+        default=None,
+        metadata={
+            "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. "
+            "For example, in Sequence Classification or Token Classification tasks, "
+            "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
+        },
+    )
+    init_lora_weights: bool = field(
+        default=True,
+        metadata={"help": "Whether to initialize the weights of the Lora layers."},
+    )
+    layers_to_transform: Optional[Union[List, int]] = field(
+        default=None,
+        metadata={
+            "help": "The layer indexes to transform, is this argument is specified, PEFT will transform only the layers indexes that are specified inside this list. If a single integer is passed, PEFT will transform only the layer at this index."
+        },
+    )
+    layers_pattern: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The layer pattern name, used only if `layers_to_transform` is different to None and if the layer pattern is not in the common layers pattern."
+        },
+    )
+
+    def __post_init__(self):
+        self.peft_type = PeftType.LORA
+
+
+class LoraModel(torch.nn.Module):
+    """
+    Creates Low Rank Adapter (Lora) model from a pretrained transformers model.
+
+    Args:
+        model ([`~transformers.PreTrainedModel`]): The model to be adapted.
+        config ([`LoraConfig`]): The configuration of the Lora model.
+
+    Returns:
+        `torch.nn.Module`: The Lora model.
+
+    Example:
+
+        ```py
+        >>> from transformers import AutoModelForSeq2SeqLM
+        >>> from peft import LoraModel, LoraConfig
+
+        >>> config = LoraConfig(
+        ...     peft_type="LORA",
+        ...     task_type="SEQ_2_SEQ_LM",
+        ...     r=8,
+        ...     lora_alpha=32,
+        ...     target_modules=["q", "v"],
+        ...     lora_dropout=0.01,
+        ... )
+
+        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+        >>> lora_model = LoraModel(config, model)
+        ```
+
+        ```py
+        >>> import transformers
+        >>> from peft import LoraConfig, PeftModel, get_peft_model, prepare_model_for_int8_training
+
+        >>> target_modules = ["q_proj", "k_proj", "v_proj", "out_proj", "fc_in", "fc_out", "wte"]
+        >>> config = LoraConfig(
+        ...     r=4, lora_alpha=16, target_modules=target_modules, lora_dropout=0.1, bias="none", task_type="CAUSAL_LM"
+        ... )
+
+        >>> model = transformers.GPTJForCausalLM.from_pretrained(
+        ...     "kakaobrain/kogpt",
+        ...     revision="KoGPT6B-ryan1.5b-float16",  # or float32 version: revision=KoGPT6B-ryan1.5b
+        ...     pad_token_id=tokenizer.eos_token_id,
+        ...     use_cache=False,
+        ...     device_map={"": rank},
+        ...     torch_dtype=torch.float16,
+        ...     load_in_8bit=True,
+        ... )
+        >>> model = prepare_model_for_int8_training(model)
+        >>> lora_model = get_peft_model(model, config)
+        ```
+
+    **Attributes**:
+        - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
+        - **peft_config** ([`LoraConfig`]): The configuration of the Lora model.
+    """
+
+    def __init__(self, model, config, adapter_name):
+        super().__init__()
+        self.model = model
+        self.forward = self.model.forward
+        self.peft_config = config
+        self.add_adapter(adapter_name, self.peft_config[adapter_name])
+
+    def add_adapter(self, adapter_name, config=None):
+        if config is not None:
+            model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config
+            config = self._prepare_lora_config(config, model_config)
+            self.peft_config[adapter_name] = config
+        self._find_and_replace(adapter_name)
+        if len(self.peft_config) > 1 and self.peft_config[adapter_name].bias != "none":
+            raise ValueError(
+                "LoraModel supports only 1 adapter with bias. When using multiple adapters, set bias to 'none' for all adapters."
+            )
+        mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
+        if self.peft_config[adapter_name].inference_mode:
+            _freeze_adapter(self.model, adapter_name)
+
+    def _check_quantization_dependency(self):
+        loaded_in_4bit = getattr(self.model, "is_loaded_in_4bit", False)
+        loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
+        if (loaded_in_4bit or loaded_in_8bit) and not is_bnb_available():
+            raise ImportError(
+                "To use Lora with 8-bit or 4-bit quantization, please install the `bitsandbytes` package. "
+                "You can install it with `pip install bitsandbytes`."
+            )
+
+    def _check_target_module_exists(self, lora_config, key):
+        if isinstance(lora_config.target_modules, str):
+            target_module_found = re.fullmatch(lora_config.target_modules, key)
+        else:
+            target_module_found = any(key.endswith(target_key) for target_key in lora_config.target_modules)
+            is_using_layer_indexes = getattr(lora_config, "layers_to_transform", None) is not None
+            layer_indexing_pattern = getattr(lora_config, "layers_pattern", None)
+
+            if is_using_layer_indexes and target_module_found:
+                layers_pattern = COMMON_LAYERS_PATTERN if layer_indexing_pattern is None else layer_indexing_pattern
+                layers_pattern = [layers_pattern] if isinstance(layers_pattern, str) else layers_pattern
+
+                for pattern in layers_pattern:
+                    layer_index = re.match(f".*.{pattern}\.(\d+)\.*", key)
+                    if layer_index is not None:
+                        layer_index = int(layer_index.group(1))
+                        if isinstance(lora_config.layers_to_transform, int):
+                            target_module_found = layer_index == lora_config.layers_to_transform
+                        else:
+                            target_module_found = layer_index in lora_config.layers_to_transform
+
+                        break
+                    else:
+                        target_module_found = False
+        return target_module_found
+
+    def _create_new_module(self, lora_config, adapter_name, target):
+        bias = hasattr(target, "bias") and target.bias is not None
+        kwargs = {
+            "r": lora_config.r,
+            "lora_alpha": lora_config.lora_alpha,
+            "lora_dropout": lora_config.lora_dropout,
+            "fan_in_fan_out": lora_config.fan_in_fan_out,
+            "init_lora_weights": lora_config.init_lora_weights,
+        }
+        loaded_in_4bit = getattr(self.model, "is_loaded_in_4bit", False)
+        loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
+
+        if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt):
+            eightbit_kwargs = kwargs.copy()
+            eightbit_kwargs.update(
+                {
+                    "has_fp16_weights": target.state.has_fp16_weights,
+                    "memory_efficient_backward": target.state.memory_efficient_backward,
+                    "threshold": target.state.threshold,
+                    "index": target.index,
+                }
+            )
+            new_module = Linear8bitLt(
+                adapter_name, target.in_features, target.out_features, bias=bias, **eightbit_kwargs
+            )
+        elif loaded_in_4bit and is_bnb_4bit_available() and isinstance(target, bnb.nn.Linear4bit):
+            fourbit_kwargs = kwargs.copy()
+            fourbit_kwargs.update(
+                {
+                    "compute_dtype": target.compute_dtype,
+                    "compress_statistics": target.weight.compress_statistics,
+                    "quant_type": target.weight.quant_type,
+                }
+            )
+            new_module = Linear4bit(adapter_name, target.in_features, target.out_features, bias=bias, **fourbit_kwargs)
+        elif isinstance(target, torch.nn.Embedding):
+            embedding_kwargs = kwargs.copy()
+            embedding_kwargs.pop("fan_in_fan_out", None)
+            in_features, out_features = target.num_embeddings, target.embedding_dim
+            new_module = Embedding(adapter_name, in_features, out_features, **embedding_kwargs)
+        elif isinstance(target, torch.nn.Conv2d):
+            out_channels, in_channels = target.weight.size()[:2]
+            kernel_size = target.weight.size()[2:]
+            stride = target.stride
+            padding = target.padding
+            new_module = Conv2d(adapter_name, in_channels, out_channels, kernel_size, stride, padding, **kwargs)
+        else:
+            if isinstance(target, torch.nn.Linear):
+                in_features, out_features = target.in_features, target.out_features
+                if kwargs["fan_in_fan_out"]:
+                    warnings.warn(
+                        "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. "
+                        "Setting fan_in_fan_out to False."
+                    )
+                    kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = False
+            elif isinstance(target, Conv1D):
+                in_features, out_features = (
+                    target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape
+                )
+                if not kwargs["fan_in_fan_out"]:
+                    warnings.warn(
+                        "fan_in_fan_out is set to False but the target module is `Conv1D`. "
+                        "Setting fan_in_fan_out to True."
+                    )
+                    kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = True
+            else:
+                raise ValueError(
+                    f"Target module {target} is not supported. "
+                    f"Currently, only `torch.nn.Linear` and `Conv1D` are supported."
+                )
+            new_module = Linear(adapter_name, in_features, out_features, bias=bias, **kwargs)
+
+        return new_module
+
+    def _find_and_replace(self, adapter_name):
+        lora_config = self.peft_config[adapter_name]
+        self._check_quantization_dependency()
+        is_target_modules_in_base_model = False
+        key_list = [key for key, _ in self.model.named_modules()]
+
+        for key in key_list:
+            if not self._check_target_module_exists(lora_config, key):
+                continue
+
+            is_target_modules_in_base_model = True
+            parent, target, target_name = _get_submodules(self.model, key)
+
+            if isinstance(target, LoraLayer) and isinstance(target, torch.nn.Conv2d):
+                target.update_layer_conv2d(
+                    adapter_name,
+                    lora_config.r,
+                    lora_config.lora_alpha,
+                    lora_config.lora_dropout,
+                    lora_config.init_lora_weights,
+                )
+            elif isinstance(target, LoraLayer):
+                target.update_layer(
+                    adapter_name,
+                    lora_config.r,
+                    lora_config.lora_alpha,
+                    lora_config.lora_dropout,
+                    lora_config.init_lora_weights,
+                )
+            else:
+                new_module = self._create_new_module(lora_config, adapter_name, target)
+                self._replace_module(parent, target_name, new_module, target)
+
+        if not is_target_modules_in_base_model:
+            raise ValueError(
+                f"Target modules {lora_config.target_modules} not found in the base model. "
+                f"Please check the target modules and try again."
+            )
+
+    def _replace_module(self, parent_module, child_name, new_module, old_module):
+        setattr(parent_module, child_name, new_module)
+        new_module.weight = old_module.weight
+        if hasattr(old_module, "bias"):
+            if old_module.bias is not None:
+                new_module.bias = old_module.bias
+
+        if getattr(old_module, "state", None) is not None:
+            new_module.state = old_module.state
+            new_module.to(old_module.weight.device)
+
+        # dispatch to correct device
+        for name, module in new_module.named_modules():
+            if "lora_" in name:
+                module.to(old_module.weight.device)
+            if "ranknum" in name:
+                module.to(old_module.weight.device)
+
+    def __getattr__(self, name: str):
+        """Forward missing attributes to the wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            return getattr(self.model, name)
+
+    def get_peft_config_as_dict(self, inference: bool = False):
+        config_dict = {}
+        for key, value in self.peft_config.items():
+            config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()}
+            if inference:
+                config["inference_mode"] = True
+        config_dict[key] = config
+        return config
+
+    def _set_adapter_layers(self, enabled=True):
+        for module in self.model.modules():
+            if isinstance(module, LoraLayer):
+                module.disable_adapters = False if enabled else True
+
+    def enable_adapter_layers(self):
+        self._set_adapter_layers(enabled=True)
+
+    def disable_adapter_layers(self):
+        self._set_adapter_layers(enabled=False)
+
+    def set_adapter(self, adapter_name):
+        for module in self.model.modules():
+            if isinstance(module, LoraLayer):
+                if module.merged:
+                    warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.")
+                    module.unmerge()
+                module.active_adapter = adapter_name
+
+    def merge_adapter(self):
+        for module in self.model.modules():
+            if isinstance(module, LoraLayer):
+                module.merge()
+
+    def unmerge_adapter(self):
+        for module in self.model.modules():
+            if isinstance(module, LoraLayer):
+                module.unmerge()
+
+    @staticmethod
+    def _prepare_lora_config(peft_config, model_config):
+        if peft_config.target_modules is None:
+            if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING:
+                raise ValueError("Please specify `target_modules` in `peft_config`")
+            peft_config.target_modules = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model_config["model_type"]]
+        return peft_config
+
+    def merge_and_unload(self):
+        r"""
+        This method merges the LoRa layers into the base model. This is needed if someone wants to use the base model
+        as a standalone model.
+        """
+        if getattr(self.config, "model_type", None) == "gpt2":
+            raise ValueError("GPT2 models are not supported for merging LORA layers")
+
+        if getattr(self.model, "is_loaded_in_8bit", False) or getattr(self.model, "is_loaded_in_4bit", False):
+            raise ValueError("Cannot merge LORA layers when the model is loaded in 8-bit mode")
+
+        key_list = [key for key, _ in self.model.named_modules() if "lora" not in key]
+        for key in key_list:
+            try:
+                parent, target, target_name = _get_submodules(self.model, key)
+            except AttributeError:
+                continue
+            if isinstance(target, LoraLayer):
+                if isinstance(target, nn.Embedding):
+                    new_module = torch.nn.Embedding(target.in_features, target.out_features)
+                elif isinstance(target, nn.Conv2d):
+                    new_module = torch.nn.Conv2d(
+                        target.in_channels,
+                        target.out_channels,
+                        kernel_size=target.kernel_size,
+                        stride=target.stride,
+                        padding=target.padding,
+                        dilation=target.dilation,
+                    )
+                else:
+                    bias = target.bias is not None
+                    new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)
+                target.merge()
+                self._replace_module(parent, target_name, new_module, target)
+
+            # save any additional trainable modules part of `modules_to_save`
+            if isinstance(target, ModulesToSaveWrapper):
+                setattr(parent, target_name, target.modules_to_save[target.active_adapter])
+
+        return self.model
+
+    def add_weighted_adapter(self, adapters, weights, adapter_name):
+        if len({self.peft_config[adapter].r for adapter in adapters}) != 1:
+            raise ValueError("All adapters must have the same r value")
+        self.peft_config[adapter_name] = self.peft_config[adapters[0]]
+        self.peft_config[adapter_name].lora_alpha = self.peft_config[adapters[0]].r
+        self._find_and_replace(adapter_name)
+        mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
+        _freeze_adapter(self.model, adapter_name)
+        key_list = [key for key, _ in self.model.named_modules() if "lora" not in key]
+        for key in key_list:
+            _, target, _ = _get_submodules(self.model, key)
+            if isinstance(target, LoraLayer):
+                if adapter_name in target.lora_A:
+                    target.lora_A[adapter_name].weight.data = target.lora_A[adapter_name].weight.data * 0.0
+                    target.lora_B[adapter_name].weight.data = target.lora_B[adapter_name].weight.data * 0.0
+                    for adapter, weight in zip(adapters, weights):
+                        if adapter not in target.lora_A:
+                            continue
+                        target.lora_A[adapter_name].weight.data += (
+                            target.lora_A[adapter].weight.data * weight * target.scaling[adapter]
+                        )
+                        target.lora_B[adapter_name].weight.data += target.lora_B[adapter].weight.data * weight
+
+                elif adapter_name in target.lora_embedding_A:
+                    target.lora_embedding_A[adapter_name].data = target.lora_embedding_A[adapter_name].data * 0.0
+                    target.lora_embedding_B[adapter_name].data = target.lora_embedding_B[adapter_name].data * 0.0
+                    for adapter, weight in zip(adapters, weights):
+                        if adapter not in target.lora_embedding_A:
+                            continue
+                        target.lora_embedding_A[adapter_name].data += (
+                            target.lora_embedding_A[adapter].data * weight * target.scaling[adapter]
+                        )
+                        target.lora_embedding_B[adapter_name].data += target.lora_embedding_B[adapter].data * weight
+
+
+# Below code is based on https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
+# and modified to work with PyTorch FSDP
+
+
+#  ------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  ------------------------------------------------------------------------------------------
+
+
+# had to adapt it for `lora_only` to work
+def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None:
+    for n, p in model.named_parameters():
+        if "lora_" not in n:
+            p.requires_grad = False
+    if bias == "none":
+        return
+    elif bias == "all":
+        for n, p in model.named_parameters():
+            if "bias" in n:
+                p.requires_grad = True
+    elif bias == "lora_only":
+        for m in model.modules():
+            if isinstance(m, LoraLayer) and hasattr(m, "bias") and m.bias is not None:
+                m.bias.requires_grad = True
+    else:
+        raise NotImplementedError
+
+
+class LoraLayer:
+    def __init__(self, in_features: int, out_features: int, **kwargs):
+        self.r = {}
+        self.lora_alpha = {}
+        self.scaling = {}
+        self.lora_dropout = nn.ModuleDict({})
+        self.lora_A = nn.ModuleDict({})
+        self.lora_B = nn.ModuleDict({})
+        # For Embedding layer
+        self.lora_embedding_A = nn.ParameterDict({})
+        self.lora_embedding_B = nn.ParameterDict({})
+        # Mark the weight as unmerged
+        self.merged = False
+        self.disable_adapters = False
+        self.in_features = in_features
+        self.out_features = out_features
+        self.kwargs = kwargs
+
+    def update_layer(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights):
+        self.r[adapter_name] = r
+        self.lora_alpha[adapter_name] = lora_alpha
+        if lora_dropout > 0.0:
+            lora_dropout_layer = nn.Dropout(p=lora_dropout)
+        else:
+            lora_dropout_layer = nn.Identity()
+
+        self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
+        # Actual trainable parameters
+        if r > 0:
+            self.lora_A.update(nn.ModuleDict({adapter_name: nn.Linear(self.in_features, r, bias=False)}))
+            self.lora_B.update(nn.ModuleDict({adapter_name: nn.Linear(r, self.out_features, bias=False)}))
+            self.scaling[adapter_name] = lora_alpha / r
+        if init_lora_weights:
+            self.reset_lora_parameters(adapter_name)
+        self.to(self.weight.device)
+
+    def update_layer_conv2d(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights):
+        self.r[adapter_name] = r
+        self.lora_alpha[adapter_name] = lora_alpha
+        if lora_dropout > 0.0:
+            lora_dropout_layer = nn.Dropout(p=lora_dropout)
+        else:
+            lora_dropout_layer = nn.Identity()
+
+        self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
+        # Actual trainable parameters
+        if r > 0:
+            kernel_size = self.kwargs["kernel_size"]
+            stride = self.kwargs["stride"]
+            padding = self.kwargs["padding"]
+            self.lora_A.update(
+                nn.ModuleDict({adapter_name: nn.Conv2d(self.in_features, r, kernel_size, stride, padding, bias=False)})
+            )
+            self.lora_B.update(
+                nn.ModuleDict({adapter_name: nn.Conv2d(r, self.out_features, (1, 1), (1, 1), bias=False)})
+            )
+            self.scaling[adapter_name] = lora_alpha / r
+        if init_lora_weights:
+            self.reset_lora_parameters(adapter_name)
+        self.to(self.weight.device)
+
+    def update_layer_embedding(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights):
+        self.r[adapter_name] = r
+        self.lora_alpha[adapter_name] = lora_alpha
+        if lora_dropout > 0.0:
+            lora_dropout_layer = nn.Dropout(p=lora_dropout)
+        else:
+            lora_dropout_layer = nn.Identity()
+
+        self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
+        # Actual trainable parameters
+        if r > 0:
+            self.lora_embedding_A.update(
+                nn.ParameterDict({adapter_name: nn.Parameter(self.weight.new_zeros((r, self.in_features)))})
+            )
+            self.lora_embedding_B.update(
+                nn.ParameterDict({adapter_name: nn.Parameter(self.weight.new_zeros((self.out_features, r)))})
+            )
+            self.scaling[adapter_name] = lora_alpha / r
+        if init_lora_weights:
+            self.reset_lora_parameters(adapter_name)
+        self.to(self.weight.device)
+
+    def reset_lora_parameters(self, adapter_name):
+        if adapter_name in self.lora_A.keys():
+            # initialize A the same way as the default for nn.Linear and B to zero
+            nn.init.kaiming_uniform_(self.lora_A[adapter_name].weight, a=math.sqrt(5))
+            nn.init.zeros_(self.lora_B[adapter_name].weight)
+        if adapter_name in self.lora_embedding_A.keys():
+            # initialize a the same way as the default for nn.linear and b to zero
+            nn.init.zeros_(self.lora_embedding_A[adapter_name])
+            nn.init.normal_(self.lora_embedding_B[adapter_name])
+
+
+class Linear(nn.Linear, LoraLayer):
+    # Lora implemented in a dense layer
+    def __init__(
+        self,
+        adapter_name: str,
+        in_features: int,
+        out_features: int,
+        r: int = 0,
+        lora_alpha: int = 1,
+        lora_dropout: float = 0.0,
+        fan_in_fan_out: bool = False,  # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
+        **kwargs,
+    ):
+        init_lora_weights = kwargs.pop("init_lora_weights", True)
+
+        nn.Linear.__init__(self, in_features, out_features, **kwargs)
+        LoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+        # Freezing the pre-trained weight matrix
+        self.weight.requires_grad = False
+
+        self.fan_in_fan_out = fan_in_fan_out
+        if fan_in_fan_out:
+            self.weight.data = self.weight.data.T
+
+        nn.Linear.reset_parameters(self)
+        self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+        self.active_adapter = adapter_name
+
+    def merge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if self.merged:
+            warnings.warn("Already merged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data += (
+                transpose(
+                    self.lora_B[self.active_adapter].weight @ self.lora_A[self.active_adapter].weight,
+                    self.fan_in_fan_out,
+                )
+                * self.scaling[self.active_adapter]
+            )
+            self.merged = True
+
+    def unmerge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if not self.merged:
+            warnings.warn("Already unmerged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data -= (
+                transpose(
+                    self.lora_B[self.active_adapter].weight @ self.lora_A[self.active_adapter].weight,
+                    self.fan_in_fan_out,
+                )
+                * self.scaling[self.active_adapter]
+            )
+            self.merged = False
+
+    def forward(self, x: torch.Tensor):
+        previous_dtype = x.dtype
+        if self.active_adapter not in self.lora_A.keys():
+            return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+        if self.disable_adapters:
+            if self.r[self.active_adapter] > 0 and self.merged:
+                self.unmerge()
+            result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+        elif self.r[self.active_adapter] > 0 and not self.merged:
+            result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+
+            x = x.to(self.lora_A[self.active_adapter].weight.dtype)
+
+            result += (
+                self.lora_B[self.active_adapter](
+                    self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
+                )
+                * self.scaling[self.active_adapter]
+            )
+        else:
+            result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+
+        result = result.to(previous_dtype)
+
+        return result
+
+
+class Embedding(nn.Embedding, LoraLayer):
+    # LoRA implemented in a Embedding layer
+    def __init__(
+        self,
+        adapter_name: str,
+        num_embeddings: int,
+        embedding_dim: int,
+        r: int = 0,
+        lora_alpha: int = 1,
+        lora_dropout: float = 0.0,
+        **kwargs,
+    ):
+        init_lora_weights = kwargs.pop("init_lora_weights", True)
+
+        nn.Embedding.__init__(self, num_embeddings, embedding_dim, **kwargs)
+        LoraLayer.__init__(self, in_features=num_embeddings, out_features=embedding_dim)
+
+        self.weight.requires_grad = False
+
+        nn.Embedding.reset_parameters(self)
+        self.update_layer_embedding(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+        self.active_adapter = adapter_name
+
+    def unmerge(self, mode: bool = True):
+        if not self.merged:
+            warnings.warn("Already unmerged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data -= (
+                transpose(
+                    self.lora_embedding_B[self.active_adapter] @ self.lora_embedding_A[self.active_adapter], True
+                )
+                * self.scaling[self.active_adapter]
+            )
+            self.merged = False
+
+    def merge(self):
+        if self.merged:
+            warnings.warn("Already merged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data += (
+                transpose(
+                    self.lora_embedding_B[self.active_adapter] @ self.lora_embedding_A[self.active_adapter], True
+                )
+                * self.scaling[self.active_adapter]
+            )
+            self.merged = True
+
+    def forward(self, x: torch.Tensor):
+        if self.disable_adapters:
+            if self.r[self.active.adapter] > 0 and self.merged:
+                self.weight.data -= (
+                    transpose(
+                        self.lora_embedding_B[self.active_adapter].weight
+                        @ self.lora_embedding_A[self.active_adapter].weight,
+                        True,
+                    )
+                    * self.scaling[self.active_adapter]
+                )
+                self.merged = False
+            return nn.Embedding.forward(self, x)
+
+        elif self.r[self.active_adapter] > 0 and not self.merged:
+            result = nn.Embedding.forward(self, x)
+            if self.r[self.active_adapter] > 0:
+                after_A = F.embedding(
+                    x,
+                    self.lora_embedding_A[self.active_adapter].T,
+                    self.padding_idx,
+                    self.max_norm,
+                    self.norm_type,
+                    self.scale_grad_by_freq,
+                    self.sparse,
+                )
+                result += (after_A @ self.lora_embedding_B[self.active_adapter].T) * self.scaling[self.active_adapter]
+            return result
+        else:
+            return nn.Embedding.forward(self, x)
+
+
+class Conv2d(nn.Conv2d, LoraLayer):
+    # Lora implemented in a conv2d layer
+    def __init__(
+        self,
+        adapter_name: str,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Union[int, Tuple[int]],
+        stride: Union[int, Tuple[int]] = 1,
+        padding: Union[int, Tuple[int]] = 0,
+        r: int = 0,
+        lora_alpha: int = 1,
+        lora_dropout: float = 0.0,
+        **kwargs,
+    ):
+        init_lora_weights = kwargs.pop("init_lora_weights", True)
+
+        nn.Conv2d.__init__(self, in_channels, out_channels, kernel_size, stride, padding)
+        LoraLayer.__init__(
+            self,
+            in_features=in_channels,
+            out_features=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+        )
+        # Freezing the pre-trained weight matrix
+        self.weight.requires_grad = False
+
+        nn.Conv2d.reset_parameters(self)
+        self.update_layer_conv2d(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+        self.active_adapter = adapter_name
+
+    def merge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if self.merged:
+            warnings.warn("Already merged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            # https://github.com/bmaltais/kohya_ss/blob/feb6728762a8f463d15ba936d189d4c3abfaa1ab/networks/lora.py#L117
+            if self.weight.size()[2:4] == (1, 1):
+                # conv2d 1x1
+                self.weight.data += (
+                    self.lora_B[self.active_adapter].weight.squeeze(3).squeeze(2)
+                    @ self.lora_A[self.active_adapter].weight.squeeze(3).squeeze(2)
+                ).unsqueeze(2).unsqueeze(3) * self.scaling[self.active_adapter]
+            else:
+                # conv2d 3x3
+                self.weight.data += (
+                    F.conv2d(
+                        self.lora_A[self.active_adapter].weight.permute(1, 0, 2, 3),
+                        self.lora_B[self.active_adapter].weight,
+                    ).permute(1, 0, 2, 3)
+                    * self.scaling[self.active_adapter]
+                )
+            self.merged = True
+
+    def unmerge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if not self.merged:
+            warnings.warn("Already unmerged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            if self.weight.size()[2:4] == (1, 1):
+                # conv2d 1x1
+                self.weight.data -= (
+                    self.lora_B[self.active_adapter].weight.squeeze(3).squeeze(2)
+                    @ self.lora_A[self.active_adapter].weight.squeeze(3).squeeze(2)
+                ).unsqueeze(2).unsqueeze(3) * self.scaling[self.active_adapter]
+            else:
+                # conv2d 3x3
+                self.weight.data += (
+                    F.conv2d(
+                        self.lora_A[self.active_adapter].weight.permute(1, 0, 2, 3),
+                        self.lora_B[self.active_adapter].weight,
+                    ).permute(1, 0, 2, 3)
+                    * self.scaling[self.active_adapter]
+                )
+            self.merged = False
+
+    def forward(self, x: torch.Tensor):
+        previous_dtype = x.dtype
+
+        if self.active_adapter not in self.lora_A.keys():
+            return F.conv2d(
+                x,
+                self.weight,
+                bias=self.bias,
+                stride=self.stride,
+                padding=self.padding,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+        if self.disable_adapters:
+            if self.r[self.active_adapter] > 0 and self.merged:
+                self.unmerge()
+            result = F.conv2d(
+                x,
+                self.weight,
+                bias=self.bias,
+                stride=self.stride,
+                padding=self.padding,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+        elif self.r[self.active_adapter] > 0 and not self.merged:
+            result = F.conv2d(
+                x,
+                self.weight,
+                bias=self.bias,
+                stride=self.stride,
+                padding=self.padding,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+
+            x = x.to(self.lora_A[self.active_adapter].weight.dtype)
+
+            result += (
+                self.lora_B[self.active_adapter](
+                    self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
+                )
+                * self.scaling[self.active_adapter]
+            )
+        else:
+            result = F.conv2d(
+                x,
+                self.weight,
+                bias=self.bias,
+                stride=self.stride,
+                padding=self.padding,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+
+        result = result.to(previous_dtype)
+
+        return result
+
+
+if is_bnb_available():
+
+    class Linear8bitLt(bnb.nn.Linear8bitLt, LoraLayer):
+        # Lora implemented in a dense layer
+        def __init__(
+            self,
+            adapter_name,
+            in_features,
+            out_features,
+            r: int = 0,
+            lora_alpha: int = 1,
+            lora_dropout: float = 0.0,
+            **kwargs,
+        ):
+            bnb.nn.Linear8bitLt.__init__(
+                self,
+                in_features,
+                out_features,
+                bias=kwargs.get("bias", True),
+                has_fp16_weights=kwargs.get("has_fp16_weights", True),
+                memory_efficient_backward=kwargs.get("memory_efficient_backward", False),
+                threshold=kwargs.get("threshold", 0.0),
+                index=kwargs.get("index", None),
+            )
+            LoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+
+            # Freezing the pre-trained weight matrix
+            self.weight.requires_grad = False
+            init_lora_weights = kwargs.pop("init_lora_weights", True)
+            self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+            self.active_adapter = adapter_name
+
+        def forward(self, x: torch.Tensor):
+            result = super().forward(x)
+
+            if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
+                return result
+            elif self.r[self.active_adapter] > 0:
+                if not torch.is_autocast_enabled():
+                    expected_dtype = result.dtype
+
+                    if x.dtype != torch.float32:
+                        x = x.float()
+                    output = (
+                        self.lora_B[self.active_adapter](
+                            self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
+                        ).to(expected_dtype)
+                        * self.scaling[self.active_adapter]
+                    )
+                else:
+                    output = (
+                        self.lora_B[self.active_adapter](
+                            self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
+                        )
+                        * self.scaling[self.active_adapter]
+                    )
+                result += output
+            return result
+
+    if is_bnb_4bit_available():
+
+        class Linear4bit(bnb.nn.Linear4bit, LoraLayer):
+            # Lora implemented in a dense layer
+            def __init__(
+                self,
+                adapter_name,
+                in_features,
+                out_features,
+                r: int = 0,
+                lora_alpha: int = 1,
+                lora_dropout: float = 0.0,
+                **kwargs,
+            ):
+                bnb.nn.Linear4bit.__init__(
+                    self,
+                    in_features,
+                    out_features,
+                    bias=kwargs.get("bias", True),
+                    compute_dtype=kwargs.get("compute_dtype", torch.float32),
+                    compress_statistics=kwargs.get("compress_statistics", True),
+                    quant_type=kwargs.get("quant_type", "nf4"),
+                )
+                LoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+
+                # Freezing the pre-trained weight matrix
+                self.weight.requires_grad = False
+
+                init_lora_weights = kwargs.pop("init_lora_weights", True)
+                self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+                self.active_adapter = adapter_name
+
+            def forward(self, x: torch.Tensor):
+                result = super().forward(x)
+
+                if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
+                    return result
+                elif self.r[self.active_adapter] > 0:
+                    result = result.clone()
+                    if not torch.is_autocast_enabled():
+                        expected_dtype = result.dtype
+                        x = x.to(self.lora_A[self.active_adapter].weight.dtype)
+                        output = (
+                            self.lora_B[self.active_adapter](
+                                self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
+                            ).to(expected_dtype)
+                            * self.scaling[self.active_adapter]
+                        )
+                    else:
+                        output = (
+                            self.lora_B[self.active_adapter](
+                                self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
+                            )
+                            * self.scaling[self.active_adapter]
+                        )
+                    result += output
+                return result
diff --git a/model/peft/tuners/moelora.py b/model/peft/tuners/moelora.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5f1b7f9b9313052d7bf7e7953ceb51110b3a0e0
--- /dev/null
+++ b/model/peft/tuners/moelora.py
@@ -0,0 +1,1164 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import traceback
+import pickle
+import math
+import re
+import warnings
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from typing import List, Optional, Tuple, Union
+import itertools
+import copy
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.pytorch_utils import Conv1D
+from .gating import GATING_TO_MODEL_MAPPING
+
+from ..import_utils import is_bnb_4bit_available, is_bnb_available
+from ..utils import (
+    COMMON_LAYERS_PATTERN,
+    TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
+    ModulesToSaveWrapper,
+    PeftConfig,
+    PeftType,
+    _freeze_adapter,
+    _get_submodules,
+    transpose,
+)
+
+if is_bnb_available():
+    import bitsandbytes as bnb
+
+
+@dataclass
+class MoeLoraConfig(PeftConfig):
+    """
+    This is the configuration class to store the configuration of a [`MoeLoraModel`].
+
+    Args:
+        r (`int`): Lora attention dimension.
+        target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to.
+        lora_alpha (`int`): The alpha parameter for Lora scaling.
+        lora_dropout (`float`): The dropout probability for Lora layers.
+        fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out).
+        For example, gpt-2 uses `Conv1D` which stores weights like (fan_in, fan_out) and hence this should be set to `True`.:
+        bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only'
+        modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable
+            and saved in the final checkpoint.
+        layers_to_transform (`Union[List[int],int]`):
+            The layer indexes to transform, if this argument is specified, it will apply the LoRA transformations on
+            the layer indexes that are specified in this list. If a single integer is passed, it will apply the LoRA
+            transformations on the layer at this index.
+        layers_pattern (`str`):
+            The layer pattern name, used only if `layers_to_transform` is different from `None` and if the layer
+            pattern is not in the common layers pattern.
+    """
+
+    r: int = field(default=8, metadata={"help": "Lora attention dimension"})
+    num_moe: int = field(default=8, metadata={"help": "Num experts of MoeLora"})
+    gating: str = field(default="Standard", metadata={"help": "Select the gating network for each MoeLora"})
+    target_modules: Optional[Union[List[str], str]] = field(
+        default=None,
+        metadata={
+            "help": "List of module names or regex expression of the module names to replace with Lora."
+                    "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' "
+        },
+    )
+    lora_alpha: int = field(default=8, metadata={"help": "Lora alpha"})
+    lora_dropout: float = field(default=0.0, metadata={"help": "Lora dropout"})
+    fan_in_fan_out: bool = field(
+        default=False,
+        metadata={"help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)"},
+    )
+    bias: str = field(default="none", metadata={"help": "Bias type for Lora. Can be 'none', 'all' or 'lora_only'"})
+    modules_to_save: Optional[List[str]] = field(
+        default=None,
+        metadata={
+            "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. "
+                    "For example, in Sequence Classification or Token Classification tasks, "
+                    "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
+        },
+    )
+    init_lora_weights: bool = field(
+        default=True,
+        metadata={"help": "Whether to initialize the weights of the Lora layers."},
+    )
+    layers_to_transform: Optional[Union[List, int]] = field(
+        default=None,
+        metadata={
+            "help": "The layer indexes to transform, is this argument is specified, PEFT will transform only the layers indexes that are specified inside this list. If a single integer is passed, PEFT will transform only the layer at this index."
+        },
+    )
+    layers_pattern: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The layer pattern name, used only if `layers_to_transform` is different to None and if the layer pattern is not in the common layers pattern."
+        },
+    )
+
+    def __post_init__(self):
+        self.peft_type = PeftType.MOELORA
+
+
+class MoeLoraModel(torch.nn.Module):
+    """
+    Creates Low Rank Adapter (Lora) model from a pretrained transformers model.
+
+    Args:
+        model ([`~transformers.PreTrainedModel`]): The model to be adapted.
+        config ([`LoraConfig`]): The configuration of the Lora model.
+
+    Returns:
+        `torch.nn.Module`: The Lora model.
+
+    Example:
+
+        ```py
+        >>> from transformers import AutoModelForSeq2SeqLM
+        >>> from moelora import MoeLoraModel, MoeLoraConfig
+
+        >>> config = LoraConfig(
+        ...     peft_type="LORA",
+        ...     task_type="SEQ_2_SEQ_LM",
+        ...     r=8,
+        ...     lora_alpha=32,
+        ...     target_modules=["q", "v"],
+        ...     lora_dropout=0.01,
+        ... )
+
+        >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
+        >>> lora_model = MoeLoraModel(config, model)
+        ```
+
+        ```py
+        >>> import transformers
+        >>> from peft import LoraConfig, PeftModel, get_peft_model, prepare_model_for_int8_training
+
+        >>> target_modules = ["q_proj", "k_proj", "v_proj", "out_proj", "fc_in", "fc_out", "wte"]
+        >>> config = LoraConfig(
+        ...     r=4, lora_alpha=16, target_modules=target_modules, lora_dropout=0.1, bias="none", task_type="CAUSAL_LM"
+        ... )
+
+        >>> model = transformers.GPTJForCausalLM.from_pretrained(
+        ...     "kakaobrain/kogpt",
+        ...     revision="KoGPT6B-ryan1.5b-float16",  # or float32 version: revision=KoGPT6B-ryan1.5b
+        ...     pad_token_id=tokenizer.eos_token_id,
+        ...     use_cache=False,
+        ...     device_map={"": rank},
+        ...     torch_dtype=torch.float16,
+        ...     load_in_8bit=True,
+        ... )
+        >>> model = prepare_model_for_int8_training(model)
+        >>> lora_model = get_peft_model(model, config)
+        ```
+
+    **Attributes**:
+        - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted.
+        - **peft_config** ([`LoraConfig`]): The configuration of the Lora model.
+    """
+
+    def __init__(self, model, config, adapter_name):
+        super().__init__()
+        self.model = model
+
+        # self.forward = self.model.forward
+        
+        
+        self.self_model_generate = self.model.generate
+        self.model.generate = self.generate
+    
+        """self.self_model_forward = self.model.forward
+        self.model.forward = self.forward"""
+        
+        self.peft_config = config
+        self.global_user_embeds = []
+        self.gate_weights = []
+        self.add_adapter(adapter_name, self.peft_config[adapter_name])
+    
+    def forward(self, **kwargs):        
+        self.global_user_embeds.clear()
+        user_embeds = kwargs['user_embeds']
+        self.global_user_embeds.extend([user_embeds])
+        
+        self.gate_weights.clear()
+        gate_weights = kwargs['gate_weights']
+        self.gate_weights.extend([gate_weights])
+        
+        return self.model(**kwargs)
+        # return self.self_model_forward(**kwargs)
+    
+    def generate(self, **kwargs):
+        self.global_user_embeds.clear()
+        user_embeds = kwargs['user_embeds']
+        self.global_user_embeds.extend([user_embeds])
+        
+        self.gate_weights.clear()
+        gate_weights = kwargs['gate_weights']
+        # print("generate, gate_weights:", gate_weights)
+        self.gate_weights.extend([gate_weights])
+        
+        return self.self_model_generate(**kwargs)
+    
+    def add_adapter(self, adapter_name, config=None):
+        if config is not None:
+            model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config
+            config = self._prepare_moelora_config(config, model_config)
+            self.peft_config[adapter_name] = config
+        self._find_and_replace(adapter_name)
+        if len(self.peft_config) > 1 and self.peft_config[adapter_name].bias != "none":
+            raise ValueError(
+                "MoeLoraModel supports only 1 adapter with bias. When using multiple adapters, set bias to 'none' for all adapters."
+            )
+        mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
+        if self.peft_config[adapter_name].inference_mode:
+            _freeze_adapter(self.model, adapter_name)
+
+    def _check_quantization_dependency(self):
+        loaded_in_4bit = getattr(self.model, "is_loaded_in_4bit", False)
+        loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
+        if (loaded_in_4bit or loaded_in_8bit) and not is_bnb_available():
+            raise ImportError(
+                "To use Lora with 8-bit or 4-bit quantization, please install the `bitsandbytes` package. "
+                "You can install it with `pip install bitsandbytes`."
+            )
+
+    def _check_target_module_exists(self, moelora_config, key):
+        if isinstance(moelora_config.target_modules, str):
+            target_module_found = re.fullmatch(moelora_config.target_modules, key)
+        else:
+            target_module_found = any(key.endswith(target_key) for target_key in moelora_config.target_modules)
+            is_using_layer_indexes = getattr(moelora_config, "layers_to_transform", None) is not None
+            layer_indexing_pattern = getattr(moelora_config, "layers_pattern", None)
+
+            if is_using_layer_indexes and target_module_found:
+                layers_pattern = COMMON_LAYERS_PATTERN if layer_indexing_pattern is None else layer_indexing_pattern
+                layers_pattern = [layers_pattern] if isinstance(layers_pattern, str) else layers_pattern
+
+                for pattern in layers_pattern:
+                    layer_index = re.match(f".*.{pattern}\.(\d+)\.*", key)
+                    if layer_index is not None:
+                        layer_index = int(layer_index.group(1))
+                        if isinstance(moelora_config.layers_to_transform, int):
+                            target_module_found = layer_index == moelora_config.layers_to_transform
+                        else:
+                            target_module_found = layer_index in moelora_config.layers_to_transform
+
+                        break
+                    else:
+                        target_module_found = False
+        return target_module_found
+
+    def _create_new_module(self, moelora_config, adapter_name, target, **kwargs):
+        bias = hasattr(target, "bias") and target.bias is not None
+        kwargs = {
+            "r": moelora_config.r,
+
+            "num_moe": moelora_config.num_moe,
+            "gating": moelora_config.gating,
+            "global_user_embeds": self.global_user_embeds,
+            
+            "gate_weights": self.gate_weights,
+            
+            "lora_alpha": moelora_config.lora_alpha,
+            "lora_dropout": moelora_config.lora_dropout,
+            "fan_in_fan_out": moelora_config.fan_in_fan_out,
+            "init_lora_weights": moelora_config.init_lora_weights,
+        }
+        loaded_in_4bit = getattr(self.model, "is_loaded_in_4bit", False)
+        loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False)
+
+        if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt):
+            eightbit_kwargs = kwargs.copy()
+            eightbit_kwargs.update(
+                {
+                    "has_fp16_weights": target.state.has_fp16_weights,
+                    "memory_efficient_backward": target.state.memory_efficient_backward,
+                    "threshold": target.state.threshold,
+                    "index": target.index,
+                }
+            )
+            eightbit_kwargs.pop('fan_in_fan_out', None)
+            eightbit_kwargs.pop('init_lora_weights', None)
+            new_module = Linear8bitLt(
+                adapter_name, target.in_features, target.out_features, bias=bias, **eightbit_kwargs
+            )
+        elif loaded_in_4bit and is_bnb_4bit_available() and isinstance(target, bnb.nn.Linear4bit):
+            fourbit_kwargs = kwargs.copy()
+            fourbit_kwargs.update(
+                {
+                    "compute_dtype": target.compute_dtype,
+                    "compress_statistics": target.weight.compress_statistics,
+                    "quant_type": target.weight.quant_type,
+                }
+            )
+            new_module = Linear4bit(adapter_name, target.in_features, target.out_features, bias=bias, **fourbit_kwargs)
+        elif isinstance(target, torch.nn.Embedding):
+            embedding_kwargs = kwargs.copy()
+            embedding_kwargs.pop("fan_in_fan_out", None)
+            in_features, out_features = target.num_embeddings, target.embedding_dim
+            new_module = Embedding(adapter_name, in_features, out_features, **embedding_kwargs)
+        elif isinstance(target, torch.nn.Conv2d):
+            out_channels, in_channels = target.weight.size()[:2]
+            kernel_size = target.weight.size()[2:]
+            stride = target.stride
+            padding = target.padding
+            new_module = Conv2d(adapter_name, in_channels, out_channels, kernel_size, stride, padding, **kwargs)
+        else:
+            if isinstance(target, torch.nn.Linear):
+                in_features, out_features = target.in_features, target.out_features
+                if kwargs["fan_in_fan_out"]:
+                    warnings.warn(
+                        "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. "
+                        "Setting fan_in_fan_out to False."
+                    )
+                    kwargs["fan_in_fan_out"] = moelora_config.fan_in_fan_out = False
+            elif isinstance(target, Conv1D):
+                in_features, out_features = (
+                    target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape
+                )
+                if not kwargs["fan_in_fan_out"]:
+                    warnings.warn(
+                        "fan_in_fan_out is set to False but the target module is `Conv1D`. "
+                        "Setting fan_in_fan_out to True."
+                    )
+                    kwargs["fan_in_fan_out"] = moelora_config.fan_in_fan_out = True
+            else:
+                raise ValueError(
+                    f"Target module {target} is not supported. "
+                    f"Currently, only `torch.nn.Linear` and `Conv1D` are supported."
+                )
+            new_module = Linear(adapter_name, in_features, out_features, bias=bias, **kwargs)
+
+        return new_module
+
+    def _find_and_replace(self, adapter_name):
+        moelora_config = self.peft_config[adapter_name]
+        self._check_quantization_dependency()
+        is_target_modules_in_base_model = False
+        key_list = [key for key, _ in self.model.named_modules()]
+
+        for key in key_list:
+            if not self._check_target_module_exists(moelora_config, key):
+                continue
+
+            is_target_modules_in_base_model = True
+            parent, target, target_name = _get_submodules(self.model, key)
+
+            if isinstance(target, MoeLoraLayer) and isinstance(target, torch.nn.Conv2d):
+                target.update_layer_conv2d(
+                    adapter_name,
+                    moelora_config.r,
+                    moelora_config.lora_alpha,
+                    moelora_config.lora_dropout,
+                    moelora_config.init_lora_weights,
+                )
+            elif isinstance(target, MoeLoraLayer):
+                target.update_layer(
+                    adapter_name,
+                    moelora_config.r,
+                    moelora_config.num_moe,
+                    moelora_config.gating,
+                    moelora_config.lora_alpha,
+                    moelora_config.lora_dropout,
+                    moelora_config.init_lora_weights,
+                )
+            else:
+                new_module = self._create_new_module(moelora_config, adapter_name, target)
+                self._replace_module(parent, target_name, new_module, target)
+
+        if not is_target_modules_in_base_model:
+            raise ValueError(
+                f"Target modules {moelora_config.target_modules} not found in the base model. "
+                f"Please check the target modules and try again."
+            )
+
+    def _replace_module(self, parent_module, child_name, new_module, old_module):
+        setattr(parent_module, child_name, new_module)
+        new_module.weight = old_module.weight
+        if hasattr(old_module, "bias"):
+            if old_module.bias is not None:
+                new_module.bias = old_module.bias
+
+        if getattr(old_module, "state", None) is not None:
+            new_module.state = old_module.state
+            new_module.to(old_module.weight.device)
+
+        # dispatch to correct device
+        for name, module in new_module.named_modules():
+            if "lora_" in name:
+                module.to(old_module.weight.device)
+            
+            if "gating" in name:
+                module.to(old_module.weight.device)
+
+            if "ranknum" in name:
+                module.to(old_module.weight.device)
+
+    def __getattr__(self, name: str):
+        """Forward missing attributes to the wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            return getattr(self.model, name)
+
+    def get_peft_config_as_dict(self, inference: bool = False):
+        config_dict = {}
+        for key, value in self.peft_config.items():
+            config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()}
+            if inference:
+                config["inference_mode"] = True
+        config_dict[key] = config
+        return config
+
+    def _set_adapter_layers(self, enabled=True):
+        for module in self.model.modules():
+            if isinstance(module, MoeLoraLayer):
+                module.disable_adapters = False if enabled else True
+
+    def enable_adapter_layers(self):
+        self._set_adapter_layers(enabled=True)
+
+    def disable_adapter_layers(self):
+        self._set_adapter_layers(enabled=False)
+
+    def set_adapter(self, adapter_name):
+        for module in self.model.modules():
+            if isinstance(module, MoeLoraLayer):
+                if module.merged:
+                    warnings.warn("Adapter cannot be set when the model is merged. Unmerging the model first.")
+                    module.unmerge()
+                module.active_adapter = adapter_name
+
+    def merge_adapter(self):
+        for module in self.model.modules():
+            if isinstance(module, MoeLoraLayer):
+                module.merge()
+
+    def unmerge_adapter(self):
+        for module in self.model.modules():
+            if isinstance(module, MoeLoraLayer):
+                module.unmerge()
+
+    @staticmethod
+    def _prepare_moelora_config(peft_config, model_config):
+        if peft_config.target_modules is None:
+            if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING:
+                raise ValueError("Please specify `target_modules` in `peft_config`")
+            peft_config.target_modules = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model_config["model_type"]]
+        return peft_config
+
+    def merge_and_unload(self):
+        r"""
+        This method merges the LoRa layers into the base model. This is needed if someone wants to use the base model
+        as a standalone model.
+        """
+        if getattr(self.config, "model_type", None) == "gpt2":
+            raise ValueError("GPT2 models are not supported for merging LORA layers")
+
+        if getattr(self.model, "is_loaded_in_8bit", False) or getattr(self.model, "is_loaded_in_4bit", False):
+            raise ValueError("Cannot merge LORA layers when the model is loaded in 8-bit mode")
+
+        key_list = [key for key, _ in self.model.named_modules() if "lora" not in key]
+        for key in key_list:
+            try:
+                parent, target, target_name = _get_submodules(self.model, key)
+            except AttributeError:
+                continue
+            if isinstance(target, MoeLoraLayer):
+                if isinstance(target, nn.Embedding):
+                    new_module = torch.nn.Embedding(target.in_features, target.out_features)
+                elif isinstance(target, nn.Conv2d):
+                    new_module = torch.nn.Conv2d(
+                        target.in_channels,
+                        target.out_channels,
+                        kernel_size=target.kernel_size,
+                        stride=target.stride,
+                        padding=target.padding,
+                        dilation=target.dilation,
+                    )
+                else:
+                    bias = target.bias is not None
+                    new_module = torch.nn.Linear(target.in_features, target.out_features, bias=bias)
+                target.merge()
+                self._replace_module(parent, target_name, new_module, target)
+
+            # save any additional trainable modules part of `modules_to_save`
+            if isinstance(target, ModulesToSaveWrapper):
+                setattr(parent, target_name, target.modules_to_save[target.active_adapter])
+
+        return self.model
+
+    def add_weighted_adapter(self, adapters, weights, adapter_name):
+        if len({self.peft_config[adapter].r for adapter in adapters}) != 1:
+            raise ValueError("All adapters must have the same r value")
+        self.peft_config[adapter_name] = self.peft_config[adapters[0]]
+        self.peft_config[adapter_name].lora_alpha = self.peft_config[adapters[0]].r
+        self._find_and_replace(adapter_name)
+        mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias)
+        _freeze_adapter(self.model, adapter_name)
+        key_list = [key for key, _ in self.model.named_modules() if "lora" not in key]
+        for key in key_list:
+            _, target, _ = _get_submodules(self.model, key)
+            if isinstance(target, MoeLoraLayer):
+                if adapter_name in target.lora_A:
+                    target.lora_A[adapter_name].weight.data = target.lora_A[adapter_name].weight.data * 0.0
+                    target.lora_B[adapter_name].weight.data = target.lora_B[adapter_name].weight.data * 0.0
+                    for adapter, weight in zip(adapters, weights):
+                        if adapter not in target.lora_A:
+                            continue
+                        target.lora_A[adapter_name].weight.data += (
+                                target.lora_A[adapter].weight.data * weight * target.scaling[adapter]
+                        )
+                        target.lora_B[adapter_name].weight.data += target.lora_B[adapter].weight.data * weight
+
+                elif adapter_name in target.lora_embedding_A:
+                    target.lora_embedding_A[adapter_name].data = target.lora_embedding_A[adapter_name].data * 0.0
+                    target.lora_embedding_B[adapter_name].data = target.lora_embedding_B[adapter_name].data * 0.0
+                    for adapter, weight in zip(adapters, weights):
+                        if adapter not in target.lora_embedding_A:
+                            continue
+                        target.lora_embedding_A[adapter_name].data += (
+                                target.lora_embedding_A[adapter].data * weight * target.scaling[adapter]
+                        )
+                        target.lora_embedding_B[adapter_name].data += target.lora_embedding_B[adapter].data * weight
+
+
+# Below code is based on https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
+# and modified to work with PyTorch FSDP
+
+
+#  ------------------------------------------------------------------------------------------
+#  Copyright (c) Microsoft Corporation. All rights reserved.
+#  Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
+#  ------------------------------------------------------------------------------------------
+
+
+# had to adapt it for `lora_only` to work
+def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None:
+    for n, p in model.named_parameters():
+        if "lora_" not in n and "gating" not in n:
+            p.requires_grad = False
+    if bias == "none":
+        return
+    elif bias == "all":
+        for n, p in model.named_parameters():
+            if "bias" in n:
+                p.requires_grad = True
+    elif bias == "lora_only":
+        for m in model.modules():
+            if isinstance(m, MoeLoraLayer) and hasattr(m, "bias") and m.bias is not None:
+                m.bias.requires_grad = True
+    else:
+        raise NotImplementedError
+
+
+class MoeLoraLayer:
+    def __init__(self, in_features: int, out_features: int, **kwargs):
+        self.r = {}
+        
+        self.num_moe = {}
+        
+        self.lora_alpha = {}
+        self.scaling = {}
+        self.lora_dropout = nn.ModuleDict({})
+        self.lora_A = nn.ModuleDict({})
+        self.lora_B = nn.ModuleDict({})
+        
+        self.gating = nn.ModuleDict({})
+        
+        # For Embedding layer
+        self.lora_embedding_A = nn.ParameterDict({})
+        self.lora_embedding_B = nn.ParameterDict({})
+        # Mark the weight as unmerged
+        self.merged = False
+        self.disable_adapters = False
+        self.in_features = in_features
+        self.out_features = out_features
+        self.kwargs = kwargs
+
+    def update_layer(self, adapter_name, r, num_moe, gating, lora_alpha, lora_dropout,
+                     init_lora_weights):
+        self.r[adapter_name] = r
+        
+        self.num_moe[adapter_name] = num_moe
+        
+        self.lora_alpha[adapter_name] = lora_alpha
+        
+        self.gating.update(
+            nn.ModuleDict({adapter_name: GATING_TO_MODEL_MAPPING[gating](num_moe=num_moe, dim=self.in_features)}))
+        self.gating[adapter_name].to(self.weight.device)
+        
+        if lora_dropout > 0.0:
+            lora_dropout_layer = nn.Dropout(p=lora_dropout)
+        else:
+            lora_dropout_layer = nn.Identity()
+
+        self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
+        # Actual trainable parameters
+        if r > 0:
+            self.lora_A.update(nn.ModuleDict({adapter_name: nn.Linear(self.in_features, r, bias=False)}))  # TODO
+            self.lora_B.update(nn.ModuleDict({adapter_name: nn.Linear(r, self.out_features, bias=False)}))
+            self.scaling[adapter_name] = lora_alpha / (r // num_moe)
+        if init_lora_weights:
+            self.reset_lora_parameters(adapter_name)
+        self.to(self.weight.device)
+
+    def update_layer_conv2d(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights):
+        self.r[adapter_name] = r
+        self.lora_alpha[adapter_name] = lora_alpha
+        if lora_dropout > 0.0:
+            lora_dropout_layer = nn.Dropout(p=lora_dropout)
+        else:
+            lora_dropout_layer = nn.Identity()
+
+        self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
+        # Actual trainable parameters
+        if r > 0:
+            kernel_size = self.kwargs["kernel_size"]
+            stride = self.kwargs["stride"]
+            padding = self.kwargs["padding"]
+            self.lora_A.update(
+                nn.ModuleDict({adapter_name: nn.Conv2d(self.in_features, r, kernel_size, stride, padding, bias=False)})
+            )
+            self.lora_B.update(
+                nn.ModuleDict({adapter_name: nn.Conv2d(r, self.out_features, (1, 1), (1, 1), bias=False)})
+            )
+            self.scaling[adapter_name] = lora_alpha / r
+        if init_lora_weights:
+            self.reset_lora_parameters(adapter_name)
+        self.to(self.weight.device)
+
+    def update_layer_embedding(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights):
+        self.r[adapter_name] = r
+        self.lora_alpha[adapter_name] = lora_alpha
+        if lora_dropout > 0.0:
+            lora_dropout_layer = nn.Dropout(p=lora_dropout)
+        else:
+            lora_dropout_layer = nn.Identity()
+
+        self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer}))
+        # Actual trainable parameters
+        if r > 0:
+            self.lora_embedding_A.update(
+                nn.ParameterDict({adapter_name: nn.Parameter(self.weight.new_zeros((r, self.in_features)))})
+            )
+            self.lora_embedding_B.update(
+                nn.ParameterDict({adapter_name: nn.Parameter(self.weight.new_zeros((self.out_features, r)))})
+            )
+            self.scaling[adapter_name] = lora_alpha / r
+        if init_lora_weights:
+            self.reset_lora_parameters(adapter_name)
+        self.to(self.weight.device)
+
+    def reset_lora_parameters(self, adapter_name):
+        if adapter_name in self.lora_A.keys():
+            # initialize A the same way as the default for nn.Linear and B to zero
+            nn.init.kaiming_uniform_(self.lora_A[adapter_name].weight, a=math.sqrt(5))
+            nn.init.zeros_(self.lora_B[adapter_name].weight)
+        if adapter_name in self.lora_embedding_A.keys():
+            # initialize a the same way as the default for nn.linear and b to zero
+            nn.init.zeros_(self.lora_embedding_A[adapter_name])
+            nn.init.normal_(self.lora_embedding_B[adapter_name])
+
+
+class Linear(nn.Linear, MoeLoraLayer):
+    # Lora implemented in a dense layer
+    def __init__(
+            self,
+            adapter_name: str,
+            in_features: int,
+            out_features: int,
+            r: int = 0,
+            
+            # moe额外参数
+            num_moe: int = 0,
+            gating: str = "",
+            global_user_embeds: List = [],
+            gate_weights: List = [],
+            
+            lora_alpha: int = 1,
+            lora_dropout: float = 0.0,
+            fan_in_fan_out: bool = False,
+            # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
+            **kwargs,
+    ):
+        init_lora_weights = kwargs.pop("init_lora_weights", True)
+        
+        nn.Linear.__init__(self, in_features, out_features, **kwargs)
+        MoeLoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+        # Freezing the pre-trained weight matrix冻结预训练参数
+        self.weight.requires_grad = False
+        self.gating.requires_grad_ = True
+        self.fan_in_fan_out = fan_in_fan_out
+        if fan_in_fan_out:
+            self.weight.data = self.weight.data.T
+        nn.Linear.reset_parameters(self)
+        self.update_layer(adapter_name, r, num_moe, gating, lora_alpha, lora_dropout, init_lora_weights)
+        self.active_adapter = adapter_name
+        self.global_user_embeds = global_user_embeds
+        self.gate_weights = gate_weights
+
+    def merge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if self.merged:
+            warnings.warn("Already merged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data += (
+                    transpose(
+                        self.lora_B[self.active_adapter].weight @ self.lora_A[self.active_adapter].weight,
+                        self.fan_in_fan_out,
+                    )
+                    * self.scaling[self.active_adapter]
+            )
+            self.merged = True
+
+    def unmerge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if not self.merged:
+            warnings.warn("Already unmerged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data -= (
+                    transpose(
+                        self.lora_B[self.active_adapter].weight @ self.lora_A[self.active_adapter].weight,
+                        self.fan_in_fan_out,
+                    )
+                    * self.scaling[self.active_adapter]
+            )
+            self.merged = False
+    
+    def calculate_B(self, A_out):
+        batch_size, seq_len, n, r = A_out.size()
+        weight = self.lora_B[self.active_adapter].weight.t().reshape(n, r, -1)
+        return torch.einsum('ijkl, klm->ijkm', A_out, weight)
+
+
+    def forward(self, x: torch.Tensor, **kwargs):
+        flag = False
+        # user_embeds = self.global_user_embeds[0]
+        gate_weights = self.gate_weights[0]
+        previous_dtype = x.dtype
+        batch_size, seq_len, _ = x.size()
+        
+        try:
+            if self.active_adapter not in self.lora_A.keys():
+                return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+            if self.disable_adapters:
+                if self.r[self.active_adapter] > 0 and self.merged:
+                    self.unmerge()
+                result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+            elif self.r[self.active_adapter] > 0 and not self.merged:
+                result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+                x = x.to(self.lora_A[self.active_adapter].weight.dtype)
+                    
+                A_out = self.lora_A[self.active_adapter](
+                            self.lora_dropout[self.active_adapter](x)
+                            ).reshape(batch_size, seq_len, self.num_moe[self.active_adapter], -1)
+                B_out = self.calculate_B(A_out)
+                
+                # Gate = self.gating[self.active_adapter](user_embeds).unsqueeze(-1)
+                Gate = gate_weights.unsqueeze(-1)
+                
+                
+                result += ((B_out * Gate).sum(dim=-2) * self.scaling[self.active_adapter])
+                
+                
+                gate_weights_squeezed = Gate.squeeze(-1)  
+                gate_weights_split = gate_weights_squeezed.split(1, dim=0) 
+                gate_weight_vectors = [x.squeeze() for x in gate_weights_split] 
+                        
+            else:
+                result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias)
+
+            
+            result = result.to(previous_dtype)
+
+            return result
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return torch.zeros_like(x)  # 返回一个与输入张量相同形状的全零张量
+
+
+class Embedding(nn.Embedding, MoeLoraLayer):
+    # LoRA implemented in a Embedding layer
+    def __init__(
+            self,
+            adapter_name: str,
+            num_embeddings: int,
+            embedding_dim: int,
+            r: int = 0,
+            lora_alpha: int = 1,
+            lora_dropout: float = 0.0,
+            **kwargs,
+    ):
+        init_lora_weights = kwargs.pop("init_lora_weights", True)
+
+        nn.Embedding.__init__(self, num_embeddings, embedding_dim, **kwargs)
+        MoeLoraLayer.__init__(self, in_features=num_embeddings, out_features=embedding_dim)
+
+        self.weight.requires_grad = False
+
+        nn.Embedding.reset_parameters(self)
+        self.update_layer_embedding(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+        self.active_adapter = adapter_name
+
+    def unmerge(self, mode: bool = True):
+        if not self.merged:
+            warnings.warn("Already unmerged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data -= (
+                    transpose(
+                        self.lora_embedding_B[self.active_adapter] @ self.lora_embedding_A[self.active_adapter], True
+                    )
+                    * self.scaling[self.active_adapter]
+            )
+            self.merged = False
+
+    def merge(self):
+        if self.merged:
+            warnings.warn("Already merged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            self.weight.data += (
+                    transpose(
+                        self.lora_embedding_B[self.active_adapter] @ self.lora_embedding_A[self.active_adapter], True
+                    )
+                    * self.scaling[self.active_adapter]
+            )
+            self.merged = True
+
+    def forward(self, x: torch.Tensor):
+        if self.disable_adapters:
+            if self.r[self.active.adapter] > 0 and self.merged:
+                self.weight.data -= (
+                        transpose(
+                            self.lora_embedding_B[self.active_adapter].weight
+                            @ self.lora_embedding_A[self.active_adapter].weight,
+                            True,
+                        )
+                        * self.scaling[self.active_adapter]
+                )
+                self.merged = False
+            return nn.Embedding.forward(self, x)
+
+        elif self.r[self.active_adapter] > 0 and not self.merged:
+            result = nn.Embedding.forward(self, x)
+            if self.r[self.active_adapter] > 0:
+                after_A = F.embedding(
+                    x,
+                    self.lora_embedding_A[self.active_adapter].T,
+                    self.padding_idx,
+                    self.max_norm,
+                    self.norm_type,
+                    self.scale_grad_by_freq,
+                    self.sparse,
+                )
+                result += (after_A @ self.lora_embedding_B[self.active_adapter].T) * self.scaling[self.active_adapter]
+            return result
+        else:
+            return nn.Embedding.forward(self, x)
+
+
+class Conv2d(nn.Conv2d, MoeLoraLayer):
+    # Lora implemented in a conv2d layer
+    def __init__(
+            self,
+            adapter_name: str,
+            in_channels: int,
+            out_channels: int,
+            kernel_size: Union[int, Tuple[int]],
+            stride: Union[int, Tuple[int]] = 1,
+            padding: Union[int, Tuple[int]] = 0,
+            r: int = 0,
+            lora_alpha: int = 1,
+            lora_dropout: float = 0.0,
+            **kwargs,
+    ):
+        init_lora_weights = kwargs.pop("init_lora_weights", True)
+
+        nn.Conv2d.__init__(self, in_channels, out_channels, kernel_size, stride, padding)
+        MoeLoraLayer.__init__(
+            self,
+            in_features=in_channels,
+            out_features=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+        )
+        # Freezing the pre-trained weight matrix
+        self.weight.requires_grad = False
+
+        nn.Conv2d.reset_parameters(self)
+        self.update_layer_conv2d(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+        self.active_adapter = adapter_name
+
+    def merge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if self.merged:
+            warnings.warn("Already merged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            # https://github.com/bmaltais/kohya_ss/blob/feb6728762a8f463d15ba936d189d4c3abfaa1ab/networks/lora.py#L117
+            if self.weight.size()[2:4] == (1, 1):
+                # conv2d 1x1
+                self.weight.data += (
+                                            self.lora_B[self.active_adapter].weight.squeeze(3).squeeze(2)
+                                            @ self.lora_A[self.active_adapter].weight.squeeze(3).squeeze(2)
+                                    ).unsqueeze(2).unsqueeze(3) * self.scaling[self.active_adapter]
+            else:
+                # conv2d 3x3
+                self.weight.data += (
+                        F.conv2d(
+                            self.lora_A[self.active_adapter].weight.permute(1, 0, 2, 3),
+                            self.lora_B[self.active_adapter].weight,
+                        ).permute(1, 0, 2, 3)
+                        * self.scaling[self.active_adapter]
+                )
+            self.merged = True
+
+    def unmerge(self):
+        if self.active_adapter not in self.lora_A.keys():
+            return
+        if not self.merged:
+            warnings.warn("Already unmerged. Nothing to do.")
+            return
+        if self.r[self.active_adapter] > 0:
+            if self.weight.size()[2:4] == (1, 1):
+                # conv2d 1x1
+                self.weight.data -= (
+                                            self.lora_B[self.active_adapter].weight.squeeze(3).squeeze(2)
+                                            @ self.lora_A[self.active_adapter].weight.squeeze(3).squeeze(2)
+                                    ).unsqueeze(2).unsqueeze(3) * self.scaling[self.active_adapter]
+            else:
+                # conv2d 3x3
+                self.weight.data += (
+                        F.conv2d(
+                            self.lora_A[self.active_adapter].weight.permute(1, 0, 2, 3),
+                            self.lora_B[self.active_adapter].weight,
+                        ).permute(1, 0, 2, 3)
+                        * self.scaling[self.active_adapter]
+                )
+            self.merged = False
+
+    def forward(self, x: torch.Tensor):
+        previous_dtype = x.dtype
+
+        if self.active_adapter not in self.lora_A.keys():
+            return F.conv2d(
+                x,
+                self.weight,
+                bias=self.bias,
+                stride=self.stride,
+                padding=self.padding,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+        if self.disable_adapters:
+            if self.r[self.active_adapter] > 0 and self.merged:
+                self.unmerge()
+            result = F.conv2d(
+                x,
+                self.weight,
+                bias=self.bias,
+                stride=self.stride,
+                padding=self.padding,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+        elif self.r[self.active_adapter] > 0 and not self.merged:
+            result = F.conv2d(
+                x,
+                self.weight,
+                bias=self.bias,
+                stride=self.stride,
+                padding=self.padding,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+
+            x = x.to(self.lora_A[self.active_adapter].weight.dtype)
+
+            result += (
+                    self.lora_B[self.active_adapter](
+                        self.lora_A[self.active_adapter](self.lora_dropout[self.active_adapter](x))
+                    )
+                    * self.scaling[self.active_adapter]
+            )
+        else:
+            result = F.conv2d(
+                x,
+                self.weight,
+                bias=self.bias,
+                stride=self.stride,
+                padding=self.padding,
+                dilation=self.dilation,
+                groups=self.groups,
+            )
+
+        result = result.to(previous_dtype)
+
+        return result
+
+
+if is_bnb_available():
+
+    class Linear8bitLt(bnb.nn.Linear8bitLt, MoeLoraLayer):
+        # Lora implemented in a dense layer
+        def __init__(
+                self,
+                adapter_name: str,
+                in_features: int,
+                out_features: int,
+                r: int = 0,
+                
+                # moe额外参数
+                num_moe: int = 0,
+                gating: str = "",
+                global_user_embeds: List = [],
+                loss_fn: str = "",
+                
+                lora_alpha: int = 1,
+                lora_dropout: float = 0.0,
+                **kwargs,
+        ):
+            kwargs.setdefault('bias', True)
+            bnb.nn.Linear8bitLt.__init__(
+                self,
+                in_features,
+                out_features,
+                # **kwargs
+            )
+            MoeLoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+
+            # Freezing the pre-trained weight matrix
+            self.weight.requires_grad = False
+            init_lora_weights = kwargs.pop("init_lora_weights", True)
+            # self.update_layer(adapter_name, r, num_moe, gating, loss_fn, lora_alpha, lora_dropout, init_lora_weights)
+            # bnb.nn.Linear8bitLt.reset_parameters(self)
+            
+            self.update_layer(adapter_name, r, num_moe, gating, lora_alpha, lora_dropout, init_lora_weights)
+            self.active_adapter = adapter_name
+            self.global_user_embeds = global_user_embeds
+        
+        def calculate_B(self, A_out):
+            # batch_size, seq_len, self.num_moe[self.active_adapter], -1
+            batch_size, seq_len, n, r = A_out.size()
+            weight = self.lora_B[self.active_adapter].weight.t().reshape(n, r, -1)
+            return torch.einsum('ijkl, klm->ijkm', A_out, weight)
+
+        def forward(self, x: torch.Tensor):
+            user_embeds = self.global_user_embeds[0]
+            result = super().forward(x)
+            
+            batch_size, seq_len, _ = x.size()
+            
+            if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
+                return result
+            elif self.r[self.active_adapter] > 0:
+                if not torch.is_autocast_enabled():
+                    expected_dtype = result.dtype
+
+                    if x.dtype != torch.float32:
+                        x = x.float()
+                    A_out = self.lora_A[self.active_adapter](
+                                self.lora_dropout[self.active_adapter](x)
+                                ).reshape(batch_size, seq_len, self.num_moe[self.active_adapter], -1)
+                    B_out = self.calculate_B(A_out)
+                    Gate = self.gating[self.active_adapter](user_embeds).unsqueeze(-1)
+                    output = ((B_out * Gate).sum(dim=-2).to(expected_dtype) * self.scaling[self.active_adapter])
+                        
+                else:
+                    A_out = self.lora_A[self.active_adapter](
+                        self.lora_dropout[self.active_adapter](x)
+                        ).reshape(batch_size, seq_len, self.num_moe[self.active_adapter], -1)
+                    B_out = self.calculate_B(A_out)
+                    Gate = self.gating[self.active_adapter](user_embeds).unsqueeze(-1)
+                    output = ((B_out * Gate).sum(dim=-2) * self.scaling[self.active_adapter])
+                    
+                result += output
+            return result
+
+
+    if is_bnb_4bit_available():
+
+        class Linear4bit(bnb.nn.Linear4bit, MoeLoraLayer):
+            # Lora implemented in a dense layer
+            def __init__(
+                    self,
+                    adapter_name,
+                    in_features,
+                    out_features,
+                    r: int = 0,
+                    lora_alpha: int = 1,
+                    lora_dropout: float = 0.0,
+                    **kwargs,
+            ):
+                bnb.nn.Linear4bit.__init__(
+                    self,
+                    in_features,
+                    out_features,
+                    bias=kwargs.get("bias", True),
+                    compute_dtype=kwargs.get("compute_dtype", torch.float32),
+                    compress_statistics=kwargs.get("compress_statistics", True),
+                    quant_type=kwargs.get("quant_type", "nf4"),
+                )
+                MoeLoraLayer.__init__(self, in_features=in_features, out_features=out_features)
+
+                # Freezing the pre-trained weight matrix
+                self.weight.requires_grad = False
+
+                init_lora_weights = kwargs.pop("init_lora_weights", True)
+                self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights)
+                self.active_adapter = adapter_name
+
+            def forward(self, x: torch.Tensor):
+                result = super().forward(x)
+                user_embeds = self.global_user_embeds[0]
+
+                if self.disable_adapters or self.active_adapter not in self.lora_A.keys():
+                    return result
+                elif self.r[self.active_adapter] > 0:
+                    result = result.clone()
+                    if not torch.is_autocast_enabled():
+                        expected_dtype = result.dtype
+                        x = x.to(self.lora_A[self.active_adapter].weight.dtype)
+                        batch_size, seq_len, _ = x.size()
+                        A_out = self.lora_A[self.active_adapter](
+                                self.lora_dropout[self.active_adapter](x)
+                                ).reshape(batch_size, seq_len, self.num_moe[self.active_adapter], -1)
+                        B_out = self.calculate_B(A_out)
+                        Gate = self.gating[self.active_adapter](user_embeds).unsqueeze(-1)
+                        output = ((B_out * Gate).sum(dim=-2).to(expected_dtype) * self.scaling[self.active_adapter])
+                    else:
+                        x = x.to(self.lora_A[self.active_adapter].weight.dtype)
+                        batch_size, seq_len, _ = x.size()
+                        A_out = self.lora_A[self.active_adapter](
+                                self.lora_dropout[self.active_adapter](x)
+                                ).reshape(batch_size, seq_len, self.num_moe[self.active_adapter], -1)
+                        B_out = self.calculate_B(A_out)
+                        Gate = self.gating[self.active_adapter](user_embeds).unsqueeze(-1)
+                        output = ((B_out * Gate).sum(dim=-2) * self.scaling[self.active_adapter])
+                    result += output
+                return result
diff --git a/model/peft/tuners/p_tuning.py b/model/peft/tuners/p_tuning.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a272f357d13215bb08f1ab1f2f734a94e327529
--- /dev/null
+++ b/model/peft/tuners/p_tuning.py
@@ -0,0 +1,170 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import enum
+import warnings
+from dataclasses import dataclass, field
+from typing import Union
+
+import torch
+
+from ..utils import PeftType, PromptLearningConfig
+
+
+class PromptEncoderReparameterizationType(str, enum.Enum):
+    MLP = "MLP"
+    LSTM = "LSTM"
+
+
+@dataclass
+class PromptEncoderConfig(PromptLearningConfig):
+    """
+    This is the configuration class to store the configuration of a [`PromptEncoder`].
+
+    Args:
+        encoder_reparameterization_type (Union[[`PromptEncoderReparameterizationType`], `str`]):
+            The type of reparameterization to use.
+        encoder_hidden_size (`int`): The hidden size of the prompt encoder.
+        encoder_num_layers (`int`): The number of layers of the prompt encoder.
+        encoder_dropout (`float`): The dropout probability of the prompt encoder.
+    """
+
+    encoder_reparameterization_type: Union[str, PromptEncoderReparameterizationType] = field(
+        default=PromptEncoderReparameterizationType.MLP,
+        metadata={"help": "How to reparameterize the prompt encoder"},
+    )
+    encoder_hidden_size: int = field(
+        default=None,
+        metadata={"help": "The hidden size of the prompt encoder"},
+    )
+    encoder_num_layers: int = field(
+        default=2,
+        metadata={"help": "The number of layers of the prompt encoder"},
+    )
+    encoder_dropout: float = field(
+        default=0.0,
+        metadata={"help": "The dropout of the prompt encoder"},
+    )
+
+    def __post_init__(self):
+        self.peft_type = PeftType.P_TUNING
+
+
+# Based on https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/modules/common/prompt_encoder.py
+# with some refactor
+class PromptEncoder(torch.nn.Module):
+    """
+    The prompt encoder network that is used to generate the virtual token embeddings for p-tuning.
+
+    Args:
+        config ([`PromptEncoderConfig`]): The configuration of the prompt encoder.
+
+    Example:
+
+    ```py
+    >>> from peft import PromptEncoder, PromptEncoderConfig
+
+    >>> config = PromptEncoderConfig(
+    ...     peft_type="P_TUNING",
+    ...     task_type="SEQ_2_SEQ_LM",
+    ...     num_virtual_tokens=20,
+    ...     token_dim=768,
+    ...     num_transformer_submodules=1,
+    ...     num_attention_heads=12,
+    ...     num_layers=12,
+    ...     encoder_reparameterization_type="MLP",
+    ...     encoder_hidden_size=768,
+    ... )
+
+    >>> prompt_encoder = PromptEncoder(config)
+    ```
+
+    **Attributes**:
+        - **embedding** (`torch.nn.Embedding`) -- The embedding layer of the prompt encoder.
+        - **mlp_head** (`torch.nn.Sequential`) -- The MLP head of the prompt encoder if `inference_mode=False`.
+        - **lstm_head** (`torch.nn.LSTM`) -- The LSTM head of the prompt encoder if `inference_mode=False` and
+        `encoder_reparameterization_type="LSTM"`.
+        - **token_dim** (`int`) -- The hidden embedding dimension of the base transformer model.
+        - **input_size** (`int`) -- The input size of the prompt encoder.
+        - **output_size** (`int`) -- The output size of the prompt encoder.
+        - **hidden_size** (`int`) -- The hidden size of the prompt encoder.
+        - **total_virtual_tokens** (`int`): The total number of virtual tokens of the
+        prompt encoder.
+        - **encoder_type** (Union[[`PromptEncoderReparameterizationType`], `str`]): The encoder type of the prompt
+          encoder.
+
+
+    Input shape: (`batch_size`, `total_virtual_tokens`)
+
+    Output shape: (`batch_size`, `total_virtual_tokens`, `token_dim`)
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.token_dim = config.token_dim
+        self.input_size = self.token_dim
+        self.output_size = self.token_dim
+        self.hidden_size = config.encoder_hidden_size
+        self.total_virtual_tokens = config.num_virtual_tokens * config.num_transformer_submodules
+        self.encoder_type = config.encoder_reparameterization_type
+
+        # embedding
+        self.embedding = torch.nn.Embedding(self.total_virtual_tokens, self.token_dim)
+        if not config.inference_mode:
+            if self.encoder_type == PromptEncoderReparameterizationType.LSTM:
+                lstm_dropout = config.encoder_dropout
+                num_layers = config.encoder_num_layers
+                # LSTM
+                self.lstm_head = torch.nn.LSTM(
+                    input_size=self.input_size,
+                    hidden_size=self.hidden_size,
+                    num_layers=num_layers,
+                    dropout=lstm_dropout,
+                    bidirectional=True,
+                    batch_first=True,
+                )
+
+                self.mlp_head = torch.nn.Sequential(
+                    torch.nn.Linear(self.hidden_size * 2, self.hidden_size * 2),
+                    torch.nn.ReLU(),
+                    torch.nn.Linear(self.hidden_size * 2, self.output_size),
+                )
+
+            elif self.encoder_type == PromptEncoderReparameterizationType.MLP:
+                warnings.warn(
+                    f"for {self.encoder_type}, the `encoder_num_layers` is ignored. Exactly 2 MLP layers are used."
+                )
+                layers = [
+                    torch.nn.Linear(self.input_size, self.hidden_size),
+                    torch.nn.ReLU(),
+                    torch.nn.Linear(self.hidden_size, self.hidden_size),
+                    torch.nn.ReLU(),
+                    torch.nn.Linear(self.hidden_size, self.output_size),
+                ]
+                self.mlp_head = torch.nn.Sequential(*layers)
+
+            else:
+                raise ValueError("Prompt encoder type not recognized. Please use one of MLP (recommended) or LSTM.")
+
+    def forward(self, indices):
+        input_embeds = self.embedding(indices)
+        if self.encoder_type == PromptEncoderReparameterizationType.LSTM:
+            output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0])
+        elif self.encoder_type == PromptEncoderReparameterizationType.MLP:
+            output_embeds = self.mlp_head(input_embeds)
+        else:
+            raise ValueError("Prompt encoder type not recognized. Please use one of MLP (recommended) or LSTM.")
+
+        return output_embeds
diff --git a/model/peft/tuners/prefix_tuning.py b/model/peft/tuners/prefix_tuning.py
new file mode 100644
index 0000000000000000000000000000000000000000..d18000e0fb919236d4a7a3f3f949aa90aef30340
--- /dev/null
+++ b/model/peft/tuners/prefix_tuning.py
@@ -0,0 +1,109 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from dataclasses import dataclass, field
+
+import torch
+
+from ..utils import PeftType, PromptLearningConfig
+
+
+@dataclass
+class PrefixTuningConfig(PromptLearningConfig):
+    """
+    This is the configuration class to store the configuration of a [`PrefixEncoder`].
+
+    Args:
+        encoder_hidden_size (`int`): The hidden size of the prompt encoder.
+        prefix_projection (`bool`): Whether to project the prefix embeddings.
+    """
+
+    encoder_hidden_size: int = field(
+        default=None,
+        metadata={"help": "The hidden size of the encoder"},
+    )
+    prefix_projection: bool = field(
+        default=False,
+        metadata={"help": "Whether to project the prefix tokens"},
+    )
+
+    def __post_init__(self):
+        self.peft_type = PeftType.PREFIX_TUNING
+
+
+# Based on https://github.com/THUDM/P-tuning-v2/blob/main/model/prefix_encoder.py
+# with some refactor
+class PrefixEncoder(torch.nn.Module):
+    r"""
+    The `torch.nn` model to encode the prefix.
+
+    Args:
+        config ([`PrefixTuningConfig`]): The configuration of the prefix encoder.
+
+    Example:
+
+    ```py
+    >>> from peft import PrefixEncoder, PrefixTuningConfig
+
+    >>> config = PrefixTuningConfig(
+    ...     peft_type="PREFIX_TUNING",
+    ...     task_type="SEQ_2_SEQ_LM",
+    ...     num_virtual_tokens=20,
+    ...     token_dim=768,
+    ...     num_transformer_submodules=1,
+    ...     num_attention_heads=12,
+    ...     num_layers=12,
+    ...     encoder_hidden_size=768,
+    ... )
+    >>> prefix_encoder = PrefixEncoder(config)
+    ```
+
+    **Attributes**:
+        - **embedding** (`torch.nn.Embedding`) -- The embedding layer of the prefix encoder.
+        - **transform** (`torch.nn.Sequential`) -- The two-layer MLP to transform the prefix embeddings if
+          `prefix_projection` is `True`.
+        - **prefix_projection** (`bool`) -- Whether to project the prefix embeddings.
+
+    Input shape: (`batch_size`, `num_virtual_tokens`)
+
+    Output shape: (`batch_size`, `num_virtual_tokens`, `2*layers*hidden`)
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.prefix_projection = config.prefix_projection
+        token_dim = config.token_dim
+        num_layers = config.num_layers
+        encoder_hidden_size = config.encoder_hidden_size
+        num_virtual_tokens = config.num_virtual_tokens
+        if self.prefix_projection and not config.inference_mode:
+            # Use a two-layer MLP to encode the prefix
+            self.embedding = torch.nn.Embedding(num_virtual_tokens, token_dim)
+            self.transform = torch.nn.Sequential(
+                torch.nn.Linear(token_dim, encoder_hidden_size),
+                torch.nn.Tanh(),
+                torch.nn.Linear(encoder_hidden_size, num_layers * 2 * token_dim),
+            )
+        else:
+            self.embedding = torch.nn.Embedding(num_virtual_tokens, num_layers * 2 * token_dim)
+
+    def forward(self, prefix: torch.Tensor):
+        if self.prefix_projection:
+            prefix_tokens = self.embedding(prefix)
+            past_key_values = self.transform(prefix_tokens)
+        else:
+            past_key_values = self.embedding(prefix)
+        return past_key_values
diff --git a/model/peft/tuners/prompt_tuning.py b/model/peft/tuners/prompt_tuning.py
new file mode 100644
index 0000000000000000000000000000000000000000..6880ff7d0c7e2c5f375a19c85b62e2355ae2b1da
--- /dev/null
+++ b/model/peft/tuners/prompt_tuning.py
@@ -0,0 +1,130 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import enum
+import math
+from dataclasses import dataclass, field
+from typing import Optional, Union
+
+import torch
+
+from ..utils import PeftType, PromptLearningConfig
+
+
+class PromptTuningInit(str, enum.Enum):
+    TEXT = "TEXT"
+    RANDOM = "RANDOM"
+
+
+@dataclass
+class PromptTuningConfig(PromptLearningConfig):
+    """
+    This is the configuration class to store the configuration of a [`PromptEmbedding`].
+
+    Args:
+        prompt_tuning_init (Union[[`PromptTuningInit`], `str`]): The initialization of the prompt embedding.
+        prompt_tuning_init_text (`str`, *optional*):
+            The text to initialize the prompt embedding. Only used if `prompt_tuning_init` is `TEXT`.
+        tokenizer_name_or_path (`str`, *optional*):
+            The name or path of the tokenizer. Only used if `prompt_tuning_init` is `TEXT`.
+    """
+
+    prompt_tuning_init: Union[PromptTuningInit, str] = field(
+        default=PromptTuningInit.RANDOM,
+        metadata={"help": "How to initialize the prompt tuning parameters"},
+    )
+    prompt_tuning_init_text: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The text to use for prompt tuning initialization. Only used if prompt_tuning_init is `TEXT`"
+        },
+    )
+    tokenizer_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The tokenizer to use for prompt tuning initialization. Only used if prompt_tuning_init is `TEXT`"
+        },
+    )
+
+    def __post_init__(self):
+        self.peft_type = PeftType.PROMPT_TUNING
+
+
+class PromptEmbedding(torch.nn.Module):
+    """
+    The model to encode virtual tokens into prompt embeddings.
+
+    Args:
+        config ([`PromptTuningConfig`]): The configuration of the prompt embedding.
+        word_embeddings (`torch.nn.Module`): The word embeddings of the base transformer model.
+
+    **Attributes**:
+        - **embedding** (`torch.nn.Embedding`) -- The embedding layer of the prompt embedding.
+
+    Example:
+
+    ```py
+    >>> from peft import PromptEmbedding, PromptTuningConfig
+
+    >>> config = PromptTuningConfig(
+    ...     peft_type="PROMPT_TUNING",
+    ...     task_type="SEQ_2_SEQ_LM",
+    ...     num_virtual_tokens=20,
+    ...     token_dim=768,
+    ...     num_transformer_submodules=1,
+    ...     num_attention_heads=12,
+    ...     num_layers=12,
+    ...     prompt_tuning_init="TEXT",
+    ...     prompt_tuning_init_text="Predict if sentiment of this review is positive, negative or neutral",
+    ...     tokenizer_name_or_path="t5-base",
+    ... )
+
+    >>> # t5_model.shared is the word embeddings of the base model
+    >>> prompt_embedding = PromptEmbedding(config, t5_model.shared)
+    ```
+
+    Input Shape: (`batch_size`, `total_virtual_tokens`)
+
+    Output Shape: (`batch_size`, `total_virtual_tokens`, `token_dim`)
+    """
+
+    def __init__(self, config, word_embeddings):
+        super().__init__()
+
+        total_virtual_tokens = config.num_virtual_tokens * config.num_transformer_submodules
+        self.embedding = torch.nn.Embedding(total_virtual_tokens, config.token_dim)
+        if config.prompt_tuning_init == PromptTuningInit.TEXT:
+            from transformers import AutoTokenizer
+
+            tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_name_or_path)
+            init_text = config.prompt_tuning_init_text
+            init_token_ids = tokenizer(init_text)["input_ids"]
+            # Trim or iterate until num_text_tokens matches total_virtual_tokens
+            num_text_tokens = len(init_token_ids)
+            if num_text_tokens > total_virtual_tokens:
+                init_token_ids = init_token_ids[:total_virtual_tokens]
+            elif num_text_tokens < total_virtual_tokens:
+                num_reps = math.ceil(total_virtual_tokens / num_text_tokens)
+                init_token_ids = init_token_ids * num_reps
+            init_token_ids = init_token_ids[:total_virtual_tokens]
+
+            word_embedding_weights = word_embeddings(torch.LongTensor(init_token_ids)).detach().clone()
+            word_embedding_weights = word_embedding_weights.to(torch.float32)
+            self.embedding.weight = torch.nn.Parameter(word_embedding_weights)
+
+    def forward(self, indices):
+        # Just get embeddings
+        prompt_embeddings = self.embedding(indices)
+        return prompt_embeddings
diff --git a/model/peft/tuners/test_moelora.py b/model/peft/tuners/test_moelora.py
new file mode 100644
index 0000000000000000000000000000000000000000..38ec8997a1193c3301c491cc565645fd8e28ed03
--- /dev/null
+++ b/model/peft/tuners/test_moelora.py
@@ -0,0 +1,97 @@
+import torch
+import unittest
+import math
+import re
+import warnings
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from typing import List, Optional, Tuple, Union
+import itertools
+import copy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.pytorch_utils import Conv1D
+from .gating import GATING_TO_MODEL_MAPPING
+
+from ..import_utils import is_bnb_4bit_available, is_bnb_available
+from ..utils import (
+    COMMON_LAYERS_PATTERN,
+    TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
+    ModulesToSaveWrapper,
+    PeftConfig,
+    PeftType,
+    _freeze_adapter,
+    _get_submodules,
+    transpose,
+)
+
+if is_bnb_available():
+    import bitsandbytes as bnb
+from moelora import *
+
+class TestMoELoRA(unittest.TestCase):
+    def setUp(self):
+        self.model = MoELoRA()  # Instantiate your MoELoRA model here
+
+    def test_forward_no_adapters(self):
+        x = torch.randn(10, 20, 30)  # Create a random input tensor
+        output = self.model.forward(x)
+        self.assertEqual(output.shape, (10, 20, 30))  # Assert the output shape is correct
+
+    def test_forward_with_adapters(self):
+        x = torch.randn(10, 20, 30)  # Create a random input tensor
+        self.model.active_adapter = 'adapter1'  # Set the active adapter
+        output = self.model.forward(x)
+        self.assertEqual(output.shape, (10, 20, 30))  # Assert the output shape is correct
+
+    def test_forward_with_global_user_embeds(self):
+        x = torch.randn(10, 20, 30)  # Create a random input tensor
+        self.model.active_adapter = 'adapter1'  # Set the active adapter
+        self.model.global_user_embeds = [torch.randn(10, 30)]  # Set the global_user_embeds
+        output = self.model.forward(x)
+        self.assertEqual(output.shape, (10, 20, 30))  # Assert the output shape is correct
+
+if __name__ == '__main__':
+    unittest.main()import torch
+import unittest
+
+class TestMoELoRA(unittest.TestCase):
+    def setUp(self):
+        self.model = MoELoRA()  # Instantiate your MoELoRA model here
+
+    def test_forward_no_adapters(self):
+        x = torch.randn(10, 20, 30)  # Create a random input tensor
+        output = self.model.forward(x)
+        self.assertEqual(output.shape, (10, 20, 30))  # Assert the output shape is correct
+
+    def test_forward_with_adapters(self):
+        x = torch.randn(10, 20, 30)  # Create a random input tensor
+        self.model.active_adapter = 'adapter1'  # Set the active adapter
+        output = self.model.forward(x)
+        self.assertEqual(output.shape, (10, 20, 30))  # Assert the output shape is correct
+
+    def test_forward_with_global_user_embeds(self):
+        x = torch.randn(10, 20, 30)  # Create a random input tensor
+        self.model.active_adapter = 'adapter1'  # Set the active adapter
+        self.model.global_user_embeds = [torch.randn(10, 30)]  # Set the global_user_embeds
+        output = self.model.forward(x)
+        self.assertEqual(output.shape, (10, 20, 30))  # Assert the output shape is correct
+
+    def test_forward_with_global_user_embeds_exception(self):
+        x = torch.randn(10, 20, 30)  # Create a random input tensor
+        self.model.active_adapter = 'adapter1'  # Set the active adapter
+        self.model.global_user_embeds = [torch.randn(5, 30)]  # Set the global_user_embeds with incompatible shape
+        output = self.model.forward(x)
+        self.assertEqual(output.shape, (10, 20, 30))  # Assert the output shape is correct
+
+    def test_forward_no_global_user_embeds(self):
+        x = torch.randn(10, 20, 30)  # Create a random input tensor
+        self.model.active_adapter = 'adapter1'  # Set the active adapter
+        self.model.global_user_embeds = []  # Set an empty global_user_embeds
+        output = self.model.forward(x)
+        self.assertEqual(output.shape, (10, 20, 30))  # Assert the output shape is correct
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/model/peft/utils/__init__.py b/model/peft/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..50e5da620bd4292c9ef0d4140233bfc627cb11f7
--- /dev/null
+++ b/model/peft/utils/__init__.py
@@ -0,0 +1,42 @@
+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all
+
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .config import PeftConfig, PeftType, PromptLearningConfig, TaskType
+from .other import (
+    TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING,
+    TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
+    TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING,
+    COMMON_LAYERS_PATTERN,
+    CONFIG_NAME,
+    WEIGHTS_NAME,
+    SAFETENSORS_WEIGHTS_NAME,
+    _set_trainable,
+    add_library_to_model_card,
+    bloom_model_postprocess_past_key_value,
+    prepare_model_for_int8_training,
+    prepare_model_for_kbit_training,
+    shift_tokens_right,
+    transpose,
+    _get_submodules,
+    _set_adapter,
+    _freeze_adapter,
+    ModulesToSaveWrapper,
+)
+from .hub_utils import hub_file_exists
+from .save_and_load import get_peft_model_state_dict, set_peft_model_state_dict
diff --git a/model/peft/utils/config.py b/model/peft/utils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccbbd5fe8e91eb7e781ec1780a9add545a71d338
--- /dev/null
+++ b/model/peft/utils/config.py
@@ -0,0 +1,222 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import enum
+import inspect
+import json
+import os
+from dataclasses import asdict, dataclass, field
+from typing import Optional, Union
+
+from huggingface_hub import hf_hub_download
+from transformers.utils import PushToHubMixin
+
+from .other import CONFIG_NAME
+
+
+class PeftType(str, enum.Enum):
+    PROMPT_TUNING = "PROMPT_TUNING"
+    P_TUNING = "P_TUNING"
+    PREFIX_TUNING = "PREFIX_TUNING"
+    LORA = "LORA"
+    ADALORA = "ADALORA"
+    ADAPTION_PROMPT = "ADAPTION_PROMPT"
+    MOELORA = "MOELORA"
+
+
+class TaskType(str, enum.Enum):
+    SEQ_CLS = "SEQ_CLS"
+    SEQ_2_SEQ_LM = "SEQ_2_SEQ_LM"
+    CAUSAL_LM = "CAUSAL_LM"
+    TOKEN_CLS = "TOKEN_CLS"
+    QUESTION_ANS = "QUESTION_ANS"
+
+
+@dataclass
+class PeftConfigMixin(PushToHubMixin):
+    r"""
+    This is the base configuration class for PEFT adapter models. It contains all the methods that are common to all
+    PEFT adapter models. This class inherits from [`~transformers.utils.PushToHubMixin`] which contains the methods to
+    push your model to the Hub. The method `save_pretrained` will save the configuration of your adapter model in a
+    directory. The method `from_pretrained` will load the configuration of your adapter model from a directory.
+
+    Args:
+        peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use.
+    """
+    peft_type: Optional[PeftType] = field(default=None, metadata={"help": "The type of PEFT model."})
+
+    @property
+    def __dict__(self):
+        return asdict(self)
+
+    def to_dict(self):
+        return self.__dict__
+
+    def save_pretrained(self, save_directory, **kwargs):
+        r"""
+        This method saves the configuration of your adapter model in a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory where the configuration will be saved.
+            kwargs (additional keyword arguments, *optional*):
+                Additional keyword arguments passed along to the [`~transformers.utils.PushToHubMixin.push_to_hub`]
+                method.
+        """
+        if os.path.isfile(save_directory):
+            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        output_dict = self.__dict__
+        output_path = os.path.join(save_directory, CONFIG_NAME)
+
+        # save it
+        with open(output_path, "w") as writer:
+            writer.write(json.dumps(output_dict, indent=2, sort_keys=True))
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, subfolder=None, **kwargs):
+        r"""
+        This method loads the configuration of your adapter model from a directory.
+
+        Args:
+            pretrained_model_name_or_path (`str`):
+                The directory or the Hub repository id where the configuration is saved.
+            kwargs (additional keyword arguments, *optional*):
+                Additional keyword arguments passed along to the child class initialization.
+        """
+        path = (
+            os.path.join(pretrained_model_name_or_path, subfolder)
+            if subfolder is not None
+            else pretrained_model_name_or_path
+        )
+
+        hf_hub_download_kwargs, class_kwargs, other_kwargs = cls._split_kwargs(kwargs)
+
+        if os.path.isfile(os.path.join(path, CONFIG_NAME)):
+            config_file = os.path.join(path, CONFIG_NAME)
+        else:
+            try:
+                config_file = hf_hub_download(
+                    pretrained_model_name_or_path, CONFIG_NAME, subfolder=subfolder, **hf_hub_download_kwargs
+                )
+            except Exception:
+                raise ValueError(f"Can't find '{CONFIG_NAME}' at '{pretrained_model_name_or_path}'")
+
+        loaded_attributes = cls.from_json_file(config_file)
+
+        config = cls(**class_kwargs)
+
+        for key, value in loaded_attributes.items():
+            if hasattr(config, key):
+                setattr(config, key, value)
+
+        return config
+
+    @classmethod
+    def from_json_file(cls, path_json_file, **kwargs):
+        r"""
+        Loads a configuration file from a json file.
+
+        Args:
+            path_json_file (`str`):
+                The path to the json file.
+        """
+        with open(path_json_file, "r") as file:
+            json_object = json.load(file)
+
+        return json_object
+
+    @classmethod
+    def _split_kwargs(cls, kwargs):
+        hf_hub_download_kwargs = {}
+        class_kwargs = {}
+        other_kwargs = {}
+
+        for key, value in kwargs.items():
+            if key in inspect.signature(hf_hub_download).parameters:
+                hf_hub_download_kwargs[key] = value
+            elif key in list(cls.__annotations__):
+                class_kwargs[key] = value
+            else:
+                other_kwargs[key] = value
+
+        return hf_hub_download_kwargs, class_kwargs, other_kwargs
+
+    @classmethod
+    def _get_peft_type(
+        cls,
+        model_id,
+        subfolder: Optional[str] = None,
+        revision: Optional[str] = None,
+        cache_dir: Optional[str] = None,
+    ):
+        path = os.path.join(model_id, subfolder) if subfolder is not None else model_id
+
+        if os.path.isfile(os.path.join(path, CONFIG_NAME)):
+            config_file = os.path.join(path, CONFIG_NAME)
+        else:
+            try:
+                config_file = hf_hub_download(
+                    model_id, CONFIG_NAME, subfolder=subfolder, revision=revision, cache_dir=cache_dir
+                )
+            except Exception:
+                raise ValueError(f"Can't find '{CONFIG_NAME}' at '{model_id}'")
+
+        loaded_attributes = cls.from_json_file(config_file)
+        return loaded_attributes["peft_type"]
+
+
+@dataclass
+class PeftConfig(PeftConfigMixin):
+    """
+    This is the base configuration class to store the configuration of a [`PeftModel`].
+
+    Args:
+        peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use.
+        task_type (Union[[`~peft.utils.config.TaskType`], `str`]): The type of task to perform.
+        inference_mode (`bool`, defaults to `False`): Whether to use the Peft model in inference mode.
+    """
+
+    base_model_name_or_path: str = field(default=None, metadata={"help": "The name of the base model to use."})
+    revision: str = field(default=None, metadata={"help": "The specific model version to use."})
+    peft_type: Union[str, PeftType] = field(default=None, metadata={"help": "Peft type"})
+    task_type: Union[str, TaskType] = field(default=None, metadata={"help": "Task type"})
+    inference_mode: bool = field(default=False, metadata={"help": "Whether to use inference mode"})
+
+
+@dataclass
+class PromptLearningConfig(PeftConfig):
+    """
+    This is the base configuration class to store the configuration of [`PrefixTuning`], [`PromptEncoder`], or
+    [`PromptTuning`].
+
+    Args:
+        num_virtual_tokens (`int`): The number of virtual tokens to use.
+        token_dim (`int`): The hidden embedding dimension of the base transformer model.
+        num_transformer_submodules (`int`): The number of transformer submodules in the base transformer model.
+        num_attention_heads (`int`): The number of attention heads in the base transformer model.
+        num_layers (`int`): The number of layers in the base transformer model.
+    """
+
+    num_virtual_tokens: int = field(default=None, metadata={"help": "Number of virtual tokens"})
+    token_dim: int = field(
+        default=None, metadata={"help": "The hidden embedding dimension of the base transformer model"}
+    )
+    num_transformer_submodules: Optional[int] = field(
+        default=None, metadata={"help": "Number of transformer submodules"}
+    )
+    num_attention_heads: Optional[int] = field(default=None, metadata={"help": "Number of attention heads"})
+    num_layers: Optional[int] = field(default=None, metadata={"help": "Number of transformer layers"})
diff --git a/model/peft/utils/hub_utils.py b/model/peft/utils/hub_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..625f4e490de4ef5e936e8786a73a9e4932b49389
--- /dev/null
+++ b/model/peft/utils/hub_utils.py
@@ -0,0 +1,29 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub import get_hf_file_metadata, hf_hub_url
+from huggingface_hub.utils import EntryNotFoundError
+
+
+def hub_file_exists(repo_id: str, filename: str, revision: str = None, repo_type: str = None) -> bool:
+    r"""
+    Checks if a file exists in a remote Hub repository.
+    """
+    url = hf_hub_url(repo_id=repo_id, filename=filename, repo_type=repo_type, revision=revision)
+    try:
+        get_hf_file_metadata(url)
+        return True
+    except EntryNotFoundError:
+        return False
diff --git a/model/peft/utils/other.py b/model/peft/utils/other.py
new file mode 100644
index 0000000000000000000000000000000000000000..971689dafb43f333c6ba57e1c0ae044623314fc7
--- /dev/null
+++ b/model/peft/utils/other.py
@@ -0,0 +1,271 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import os
+import warnings
+
+import torch
+
+
+# Add or edit model card to have `library_name: peft`
+def add_library_to_model_card(output_dir):
+    if os.path.exists(os.path.join(output_dir, "README.md")):
+        with open(os.path.join(output_dir, "README.md"), "r") as f:
+            lines = f.readlines()
+        # check if the first line is `---`
+        if len(lines) > 0 and lines[0].startswith("---"):
+            for i, line in enumerate(lines[1:]):
+                # check if line starts with `library_name`, if yes, update it
+                if line.startswith("library_name"):
+                    lines[i + 1] = "library_name: peft\n"
+                    break
+                elif line.startswith("---"):
+                    # insert `library_name: peft` before the last `---`
+                    lines.insert(i + 1, "library_name: peft\n")
+                    break
+        else:
+            lines = ["---\n", "library_name: peft\n", "---\n"] + lines
+    else:
+        lines = ["---\n", "library_name: peft\n", "---\n"]
+    # write the lines back to README.md
+    with open(os.path.join(output_dir, "README.md"), "w") as f:
+        f.writelines(lines)
+
+
+# needed for prefix-tuning of bloom model
+def bloom_model_postprocess_past_key_value(past_key_values):
+    past_key_values = torch.cat(past_key_values)
+    total_layers, batch_size, num_attention_heads, num_virtual_tokens, head_dim = past_key_values.shape
+    keys = past_key_values[: total_layers // 2]
+    keys = keys.transpose(2, 3).reshape(
+        total_layers // 2, batch_size * num_attention_heads, head_dim, num_virtual_tokens
+    )
+    values = past_key_values[total_layers // 2 :]
+    values = values.reshape(total_layers // 2, batch_size * num_attention_heads, num_virtual_tokens, head_dim)
+
+    return tuple(zip(keys, values))
+
+
+def prepare_model_for_kbit_training(model, use_gradient_checkpointing=True):
+    r"""
+    This method wraps the entire protocol for preparing a model before running a training. This includes:
+        1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm
+        head to fp32
+
+    Args:
+        model, (`transformers.PreTrainedModel`):
+            The loaded model from `transformers`
+    """
+    loaded_in_kbit = getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False)
+
+    for name, param in model.named_parameters():
+        # freeze base model's layers
+        param.requires_grad = False
+
+    # cast all non INT8 parameters to fp32
+    for param in model.parameters():
+        if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
+            param.data = param.data.to(torch.float32)
+
+    if loaded_in_kbit and use_gradient_checkpointing:
+        # For backward compatibility
+        if hasattr(model, "enable_input_require_grads"):
+            model.enable_input_require_grads()
+        else:
+
+            def make_inputs_require_grad(module, input, output):
+                output.requires_grad_(True)
+
+            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        # enable gradient checkpointing for memory efficiency
+        model.gradient_checkpointing_enable()
+
+    return model
+
+
+# For backward compatibility
+def prepare_model_for_int8_training(*args, **kwargs):
+    warnings.warn(
+        "prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.",
+        FutureWarning,
+    )
+    return prepare_model_for_kbit_training(*args, **kwargs)
+
+
+# copied from transformers.models.bart.modeling_bart
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
+    """
+    Shift input ids one token to the right.
+
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): input ids
+        pad_token_id (`int`): The id of the `padding` token.
+        decoder_start_token_id (`int`): The id of the `start` token.
+    """
+    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
+    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
+    shifted_input_ids[:, 0] = decoder_start_token_id
+
+    if pad_token_id is None:
+        raise ValueError("self.model.config.pad_token_id has to be defined.")
+    # replace possible -100 values in labels by `pad_token_id`
+    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
+
+    return shifted_input_ids
+
+
+class ModulesToSaveWrapper(torch.nn.Module):
+    def __init__(self, module_to_save, adapter_name):
+        super().__init__()
+        self.original_module = module_to_save
+        self.modules_to_save = torch.nn.ModuleDict({})
+        self.update(adapter_name)
+        self.active_adapter = adapter_name
+
+    def update(self, adapter_name):
+        self.modules_to_save.update(torch.nn.ModuleDict({adapter_name: copy.deepcopy(self.original_module)}))
+
+    def forward(self, *args, **kwargs):
+        if self.active_adapter not in self.modules_to_save:
+            return self.original_module(*args, **kwargs)
+        return self.modules_to_save[self.active_adapter](*args, **kwargs)
+
+
+def _get_submodules(model, key):
+    parent = model.get_submodule(".".join(key.split(".")[:-1]))
+    target_name = key.split(".")[-1]
+    target = model.get_submodule(key)
+    return parent, target, target_name
+
+
+def _freeze_adapter(model, adapter_name):
+    for n, p in model.named_parameters():
+        if adapter_name in n:
+            p.requires_grad = False
+
+
+def _set_trainable(model, adapter_name):
+    key_list = [key for key, _ in model.named_modules()]
+    for key in key_list:
+        target_module_found = any(key.endswith(target_key) for target_key in model.modules_to_save)
+        if target_module_found:
+            parent, target, target_name = _get_submodules(model, key)
+            if isinstance(target, ModulesToSaveWrapper):
+                target.update(adapter_name)
+            else:
+                for param in target.parameters():
+                    param.requires_grad = True
+                setattr(parent, target_name, ModulesToSaveWrapper(target, adapter_name))
+
+
+def _set_adapter(model, adapter_name):
+    for module in model.modules():
+        if isinstance(module, ModulesToSaveWrapper):
+            module.active_adapter = adapter_name
+
+
+def fsdp_auto_wrap_policy(model):
+    import functools
+    import os
+
+    from accelerate import FullyShardedDataParallelPlugin
+    from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy
+
+    from ..tuners import PrefixEncoder, PromptEmbedding, PromptEncoder
+
+    def lambda_policy_fn(module):
+        if (
+            len(list(module.named_children())) == 0
+            and getattr(module, "weight", None) is not None
+            and module.weight.requires_grad
+        ):
+            return True
+        return False
+
+    lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn)
+    transformer_wrap_policy = functools.partial(
+        transformer_auto_wrap_policy,
+        transformer_layer_cls=(
+            PrefixEncoder,
+            PromptEncoder,
+            PromptEmbedding,
+            FullyShardedDataParallelPlugin.get_module_class_from_name(
+                model, os.environ.get("FSDP_TRANSFORMER_CLS_TO_WRAP", "")
+            ),
+        ),
+    )
+
+    auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy])
+    return auto_wrap_policy
+
+
+def transpose(weight, fan_in_fan_out):
+    return weight.T if fan_in_fan_out else weight
+
+
+TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING = {
+    "t5": ["q", "v"],
+    "mt5": ["q", "v"],
+    "bart": ["q_proj", "v_proj"],
+    "gpt2": ["c_attn"],
+    "bloom": ["query_key_value"],
+    "blip-2": ["q", "v", "q_proj", "v_proj"],
+    "opt": ["q_proj", "v_proj"],
+    "gptj": ["q_proj", "v_proj"],
+    "gpt_neox": ["query_key_value"],
+    "gpt_neo": ["q_proj", "v_proj"],
+    "bert": ["query", "value"],
+    "roberta": ["query", "value"],
+    "xlm-roberta": ["query", "value"],
+    "electra": ["query", "value"],
+    "deberta-v2": ["query_proj", "value_proj"],
+    "deberta": ["in_proj"],
+    "layoutlm": ["query", "value"],
+    "llama": ["q_proj", "v_proj"],
+    "chatglm": ["query_key_value"],
+    "gpt_bigcode": ["c_attn"],
+    "mpt": ["Wqkv"],
+}
+
+COMMON_LAYERS_PATTERN = ["layers", "h", "block", "blocks"]
+
+TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING = {
+    "t5": ["q", "k", "v", "o", "wi", "wo"],
+    "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"],
+    "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
+    # "gpt2": ["c_attn"],
+    # "bloom": ["query_key_value"],
+    "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"],
+    # "gptj": ["q_proj", "v_proj"],
+    # "gpt_neox": ["query_key_value"],
+    # "gpt_neo": ["q_proj", "v_proj"],
+    # "bert": ["query", "value"],
+    "roberta": ["query", "key", "value", "dense"],
+    # "xlm-roberta": ["query", "value"],
+    # "electra": ["query", "value"],
+    "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"],
+    # "deberta": ["in_proj"],
+    # "layoutlm": ["query", "value"],
+}
+
+TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING = {
+    "bloom": bloom_model_postprocess_past_key_value,
+}
+
+WEIGHTS_NAME = "adapter_model.bin"
+SAFETENSORS_WEIGHTS_NAME = "adapter_model.safetensors"
+CONFIG_NAME = "adapter_config.json"
diff --git a/model/peft/utils/save_and_load.py b/model/peft/utils/save_and_load.py
new file mode 100644
index 0000000000000000000000000000000000000000..61b6f92e8aa5c1a9b6277fe0fac3f6c645baa0ea
--- /dev/null
+++ b/model/peft/utils/save_and_load.py
@@ -0,0 +1,137 @@
+# coding=utf-8
+# Copyright 2023-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .config import PeftType, PromptLearningConfig
+
+
+def get_peft_model_state_dict(model, state_dict=None, adapter_name="default"):
+    """
+    Get the state dict of the Peft model.
+
+    Args:
+        model ([`PeftModel`]): The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP,
+        the model should be the underlying model/unwrapped model (i.e. model.module).
+        state_dict (`dict`, *optional*, defaults to `None`):
+            The state dict of the model. If not provided, the state dict of the model
+        will be used.
+    """
+    config = model.peft_config[adapter_name]
+    if state_dict is None:
+        state_dict = model.state_dict()
+    if config.peft_type in (PeftType.LORA, PeftType.ADALORA, PeftType.MOELORA):
+        # to_return = lora_state_dict(model, bias=model.peft_config.bias)
+        # adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py`
+        # to be used directly with the state dict which is necessary when using DeepSpeed or FSDP
+        bias = config.bias
+        if bias == "none":
+            to_return = {k: state_dict[k] for k in state_dict if "lora_" in k or "gating" in k}
+        elif bias == "all":
+            to_return = {k: state_dict[k] for k in state_dict if "lora_" in k or "bias" in k or "gating" in k}
+        elif bias == "lora_only":
+            to_return = {}
+            for k in state_dict:
+                if "lora_" in k:
+                    to_return[k] = state_dict[k]
+                    bias_name = k.split("lora_")[0] + "bias"
+                    if bias_name in state_dict:
+                        to_return[bias_name] = state_dict[bias_name]
+                if "gating" in k:
+                    to_return[k] = state_dict[k]
+        else:
+            raise NotImplementedError
+        to_return = {k: v for k, v in to_return.items() if (("lora_" in k and adapter_name in k) or ("bias" in k) or ("gating" in k))}
+        if config.peft_type == PeftType.ADALORA:
+            rank_pattern = config.rank_pattern
+            if rank_pattern is not None:
+                rank_pattern = {k.replace(f".{adapter_name}", ""): v for k, v in rank_pattern.items()}
+                config.rank_pattern = rank_pattern
+                to_return = model.resize_state_dict_by_rank_pattern(rank_pattern, to_return, adapter_name)
+
+    elif config.peft_type == PeftType.ADAPTION_PROMPT:
+        to_return = {k: state_dict[k] for k in state_dict if k.split(".")[-1].startswith("adaption_")}
+    elif isinstance(config, PromptLearningConfig):
+        to_return = {}
+        if config.inference_mode:
+            prompt_embeddings = model.prompt_encoder[adapter_name].embedding.weight
+        else:
+            prompt_embeddings = model.get_prompt_embedding_to_save(adapter_name)
+        to_return["prompt_embeddings"] = prompt_embeddings
+    else:
+        raise NotImplementedError
+    if model.modules_to_save is not None:
+        for key, value in state_dict.items():
+            if any(f"{module_name}.modules_to_save.{adapter_name}" in key for module_name in model.modules_to_save):
+                to_return[key.replace("modules_to_save.", "")] = value
+
+    to_return = {k.replace(f".{adapter_name}", ""): v for k, v in to_return.items()}
+    return to_return
+
+
+def set_peft_model_state_dict(model, peft_model_state_dict, adapter_name="default"):
+    """
+    Set the state dict of the Peft model.
+
+    Args:
+        model ([`PeftModel`]): The Peft model.
+        peft_model_state_dict (`dict`): The state dict of the Peft model.
+    """
+    config = model.peft_config[adapter_name]
+    state_dict = {}
+    if model.modules_to_save is not None:
+        for key, value in peft_model_state_dict.items():
+            if any(module_name in key for module_name in model.modules_to_save):
+                for module_name in model.modules_to_save:
+                    if module_name in key:
+                        key = key.replace(module_name, f"{module_name}.modules_to_save.{adapter_name}")
+                        break
+            state_dict[key] = value
+    else:
+        state_dict = peft_model_state_dict
+
+    if config.peft_type in (PeftType.LORA, PeftType.ADALORA, PeftType.MOELORA):
+        peft_model_state_dict = {}
+        for k, v in state_dict.items():
+            if "lora_A" in k:
+                k = k.replace("lora_A", f"lora_A.{adapter_name}")
+                peft_model_state_dict[k] = v
+                # suffix = k.split("lora_")[1]
+                # if "." in suffix:
+                #     suffix_to_replace = ".".join(suffix.split(".")[1:])
+                #     k = k.replace(suffix_to_replace, f"{adapter_name}.{suffix_to_replace}")
+                # else:
+                #     k = f"{k}.{adapter_name}"
+                # peft_model_state_dict[k] = v
+            elif "lora_B" in k:
+                k = k.replace("lora_B", f"lora_B.{adapter_name}")
+                peft_model_state_dict[k] = v
+            elif "gating" in k:
+                k = k.replace("gating", f"gating.{adapter_name}")
+                peft_model_state_dict[k] = v
+            else:
+                peft_model_state_dict[k] = v
+        if config.peft_type == PeftType.ADALORA:
+            rank_pattern = config.rank_pattern
+            if rank_pattern is not None:
+                model.resize_modules_by_rank_pattern(rank_pattern, adapter_name)
+    elif isinstance(config, PromptLearningConfig) or config.peft_type == PeftType.ADAPTION_PROMPT:
+        peft_model_state_dict = state_dict
+    else:
+        raise NotImplementedError
+    load_result = model.load_state_dict(peft_model_state_dict, strict=False)
+    if isinstance(config, PromptLearningConfig):
+        model.prompt_encoder[adapter_name].embedding.load_state_dict(
+            {"weight": peft_model_state_dict["prompt_embeddings"]}, strict=True
+        )
+    return load_result
diff --git a/model/router/_init_.py b/model/router/_init_.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/model/router/cvr.py b/model/router/cvr.py
new file mode 100644
index 0000000000000000000000000000000000000000..909b998d27c76344e1ee948c6e3f7878af668e6d
--- /dev/null
+++ b/model/router/cvr.py
@@ -0,0 +1,73 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LambdaLayer(nn.Module):
+    def __init__(self, lambd):
+        super(LambdaLayer, self).__init__()
+        self.lambd = lambd
+
+    def forward(self, x):
+        return self.lambd(x)
+
+
+class Block(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, conv_layer, stride=1):
+        super(Block, self).__init__()
+        self.conv1 = conv_layer(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = conv_layer(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != planes:
+            diff = planes - in_planes
+            self.shortcut = LambdaLayer(
+                lambda x: F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, int(diff * 0.5), int((diff + 1) * 0.5)), "constant", 0))
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += self.shortcut(x)
+        out = F.relu(out)
+        return out
+
+
+class Router(nn.Module):
+    def __init__(self, block, num_blocks, num_experts=2):
+        super(Router, self).__init__()
+        self.in_planes = 16
+        self.conv_layer = nn.Conv2d
+
+        self.conv1 = nn.Conv2d(3, self.in_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(self.in_planes)
+        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
+        self.fc = nn.Linear(64, num_experts)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        planes = planes
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, self.conv_layer, stride))
+            self.in_planes = planes * block.expansion
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.avgpool(out)
+        out = out.view(out.size(0), -1)
+        out = self.fc(out)
+        return out
+
+
+def build_router(**kwargs):
+    return Router(Block, [3, 3, 3], **kwargs)
diff --git a/model/router/nlpr.py b/model/router/nlpr.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c1323c4d69bd70970afd2cbc6753e200494a224
--- /dev/null
+++ b/model/router/nlpr.py
@@ -0,0 +1,84 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LambdaLayer(nn.Module):
+    def __init__(self, lambd):
+        super(LambdaLayer, self).__init__()
+        self.lambd = lambd
+
+    def forward(self, x):
+        return self.lambd(x)
+
+
+class ResidualBlock(nn.Module):
+    expansion = 1
+    
+    def __init__(self, in_planes, planes, conv_layer, stride=1):
+        super(ResidualBlock, self).__init__()
+        self.conv1 = conv_layer(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm1d(planes)
+        self.conv2 = conv_layer(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm1d(planes)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != planes:
+            diff = planes - in_planes
+            self.shortcut = LambdaLayer(
+                lambda x: F.pad(x[:, :, ::2], (0, 0, int(diff * 0.5), int((diff + 1) * 0.5)), "constant", 0))
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += self.shortcut(x)
+        out = F.relu(out)
+        return out
+
+
+class GateFunction(nn.Module):
+    def __init__(self, input_size, output_size):
+        super(GateFunction, self).__init__()
+        self.fc = nn.Linear(input_size, output_size)
+
+    def forward(self, x):
+        return F.softmax(self.fc(x), dim=-1)
+
+
+class NLPRecommendationRouter(nn.Module):
+    def __init__(self, block, num_blocks, input_size=64, num_experts=4):
+        super(NLPRecommendationRouter, self).__init__()
+        self.in_planes = 16
+        self.conv_layer = nn.Conv1d
+
+        self.conv1 = nn.Conv1d(1, self.in_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm1d(self.in_planes)
+        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+
+        # Gate function
+        self.gate = GateFunction(input_size, num_experts)
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, self.conv_layer, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.avgpool(out)
+        out = out.view(out.size(0), -1)
+        out = self.gate(out)
+        return out.unsqueeze(1)
+
+
+def build_router(**kwargs):
+    return NLPRecommendationRouter(ResidualBlock, [3, 3, 3], input_size=64, num_experts=4)
\ No newline at end of file
diff --git a/optims.py b/optims.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d7269a6b2b1b4fb737c718b37c558d5b340e8f5
--- /dev/null
+++ b/optims.py
@@ -0,0 +1,43 @@
+import math
+
+class LinearWarmupCosineLRScheduler:
+    def __init__(
+        self,
+        optimizer,
+        min_lr_list,
+        init_lr_list,
+        warmup_steps=0,
+        warmup_start_lr_list=None,
+        **kwargs
+    ):
+        self.optimizer = optimizer
+
+        self.min_lr_list = min_lr_list
+        self.init_lr_list = init_lr_list
+        self.warmup_steps = warmup_steps
+        self.warmup_start_lr_list = warmup_start_lr_list if warmup_start_lr_list is not None else init_lr_list
+
+    def step(self, cur_step, cur_epoch, max_step):
+        for i, param_group in enumerate(self.optimizer.param_groups):
+            if cur_epoch == 0 and cur_step < self.warmup_steps:
+                lr = self.warmup_lr_schedule(cur_step, self.warmup_start_lr_list[i], self.init_lr_list[i])
+            else:
+                lr = self.cosine_lr_schedule(cur_step - self.warmup_steps, max_step - self.warmup_steps, self.init_lr_list[i], self.min_lr_list[i])
+            param_group["lr"] = lr
+            
+    def cosine_lr_schedule(self, step, max_step, init_lr, min_lr):
+        """Decay the learning rate using cosine schedule"""
+        lr = (init_lr - min_lr) * 0.5 * (1 + math.cos(math.pi * step / max_step)) + min_lr
+        return lr
+
+    def warmup_lr_schedule(self, step, init_lr, max_lr):
+        """Warmup the learning rate"""
+        lr = min(max_lr, init_lr + (max_lr - init_lr) * step / max(self.warmup_steps, 1))
+        return lr
+
+    def state_dict(self):
+        return {key: value for key, value in self.__dict__.items() if key != 'optimizer'}
+
+    def load_state_dict(self, state_dict):
+        self.__dict__.update(state_dict)
+        
\ No newline at end of file
diff --git a/prompt/artist.txt b/prompt/artist.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e7beaf03498b817b65855e0d5cb77c3a7acf2e17
--- /dev/null
+++ b/prompt/artist.txt
@@ -0,0 +1,3 @@
+This user has listened to [HistoryHere] in the previous. Please predict the next music artist this user will listen to. The artist name candidates are [CansHere]. Choose only one artist from the candidates. The answer is 
+This user has listened to [HistoryHere] in the previous. Given the following 10 music artist names, [CansHere], recommend one artist for this user to listen to next. The artist name you recommend is 
+The listening history of this user is [HistoryHere]. Recommend the next music artist for this user to listen to from the following artist name set, [CansHere]. The recommendation should contain one artist's name only. The recommendation is 
\ No newline at end of file
diff --git a/prompt/game.txt b/prompt/game.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b16b876205f4af50ac174b834299d70a3f354e8
--- /dev/null
+++ b/prompt/game.txt
@@ -0,0 +1,3 @@
+This user has played [HistoryHere] in the previous. Please predict the next game this user will play. The game title candidates are [CansHere]. Choose only one game from the candidates. The answer is 
+This user has played [HistoryHere] in the previous. Given the following 20 game titles, [CansHere], recommend one game for this user to play next. The game title you recommend is 
+The visit history of this user is [HistoryHere]. Recommend a next game for this user to play from the following game title set, [CansHere]. The recommendation should contain one game title only. The recommendation is 
\ No newline at end of file
diff --git a/prompt/movie.txt b/prompt/movie.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d25b3c014c89a8f6611f1fc19c29cc311e571052
--- /dev/null
+++ b/prompt/movie.txt
@@ -0,0 +1,3 @@
+This user has watched [HistoryHere] in the previous. Please predict the next movie this user will watch. Choose the answer from the following 20 movie titles: [CansHere]. Answer: 
+This user has watched [HistoryHere] in the previous. Given the following 20 movie titles: [CansHere], recommend one movie for this user to watch next. The movie title you recommend is: 
+The visit history of this user is: [HistoryHere]. Recommend a next movie for this user to watch from the following movie title set: [CansHere]. The recommendation should contain one movie title only. Recommendation:
\ No newline at end of file
diff --git a/prompt/read.txt b/prompt/read.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cdead876f4ef3da393dca19fb1e75391e72eaaa4
--- /dev/null
+++ b/prompt/read.txt
@@ -0,0 +1,3 @@
+This user has readed [HistoryHere] in the previous. Please predict the next book this user will play. The book title candidates are [CansHere]. Choose only one book from the candidates. The answer is 
+This user has readed [HistoryHere] in the previous. Given the following 20 book titles, [CansHere], recommend one book for this user to play next. The book title you recommend is 
+The visit history of this user is [HistoryHere]. Recommend a next book for this user to read from the following book title set, [CansHere]. The recommendation should contain one book title only. The recommendation is 
\ No newline at end of file
diff --git a/rec_model/Caser_movielens.pt b/rec_model/Caser_movielens.pt
new file mode 100644
index 0000000000000000000000000000000000000000..36bafb00933b4bf8af44c1d31fb4848a8d884f9f
--- /dev/null
+++ b/rec_model/Caser_movielens.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:851820d56564a551b98a5977167de049d4bce53386065df4053568bb4dc56d71
+size 1237633
diff --git a/rec_model/Caser_steam.pt b/rec_model/Caser_steam.pt
new file mode 100644
index 0000000000000000000000000000000000000000..7b652ab55ec253a0429e09a7a330d50b3eedc24a
--- /dev/null
+++ b/rec_model/Caser_steam.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b219811b7ff1e2b0521282aa6457c4277fc090ec364f0e40657b283f62285292
+size 2582158
diff --git a/rec_model/GRU_movielens.pt b/rec_model/GRU_movielens.pt
new file mode 100644
index 0000000000000000000000000000000000000000..fb182d830e0e137b67a8f9468583fdc9151a8848
--- /dev/null
+++ b/rec_model/GRU_movielens.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad5151035bc466d55efedc5620bb67b9ffcafb0fed22ff86ae3341ced490b54e
+size 972921
diff --git a/rec_model/GRU_steam.pt b/rec_model/GRU_steam.pt
new file mode 100644
index 0000000000000000000000000000000000000000..adfa32d9323fab375aa61bf97824ae5396b085c5
--- /dev/null
+++ b/rec_model/GRU_steam.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd14c401a1529cb678f0f66fa586b26c9875653b91c95680b511f06b2125b3db
+size 1952825
diff --git a/rec_model/SASRec_goodreads.pt b/rec_model/SASRec_goodreads.pt
new file mode 100644
index 0000000000000000000000000000000000000000..e3baa55e0e31789f4f77c57b2a67e1af30493e73
--- /dev/null
+++ b/rec_model/SASRec_goodreads.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f75c0b397e8d7146b10ebee3be909f03ebabb1179afc1faf6363aca49d8e1140
+size 2448251
diff --git a/rec_model/SASRec_movielens.pt b/rec_model/SASRec_movielens.pt
new file mode 100644
index 0000000000000000000000000000000000000000..d53a5e2e77cefd412ff88baa2882c74295b56978
--- /dev/null
+++ b/rec_model/SASRec_movielens.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6928a06778d0e5b64ead0641501f73f13beb2c61ca2d881ee15582630d101df9
+size 973163
diff --git a/rec_model/SASRec_steam.pt b/rec_model/SASRec_steam.pt
new file mode 100644
index 0000000000000000000000000000000000000000..767187016d83deb3c988fd1782bb9a92e64731dd
--- /dev/null
+++ b/rec_model/SASRec_steam.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25c27ec31a611c0d02c2ed1e36f633d7a34c3ce5917b812215879b0d2bc237bc
+size 1953067
diff --git a/rec_model/lastfm.pt b/rec_model/lastfm.pt
new file mode 100644
index 0000000000000000000000000000000000000000..0c9e8015ae7a35eb98d8eeef029a819464166f50
--- /dev/null
+++ b/rec_model/lastfm.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f94253543e0ca3b0bbd42b4f6b94f2c97c9fdfdd36c3b5ad3f6b576a806dd86
+size 2481963
diff --git a/rec_model/movielens.pt b/rec_model/movielens.pt
new file mode 100644
index 0000000000000000000000000000000000000000..d53a5e2e77cefd412ff88baa2882c74295b56978
--- /dev/null
+++ b/rec_model/movielens.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6928a06778d0e5b64ead0641501f73f13beb2c61ca2d881ee15582630d101df9
+size 973163
diff --git a/rec_model/steam.pt b/rec_model/steam.pt
new file mode 100644
index 0000000000000000000000000000000000000000..ed507ca58de5cb39c3c4aa805512a8dff80e8291
--- /dev/null
+++ b/rec_model/steam.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:752c0cd11a8646adca10025b5746dfcb8968967a6104546f6f869a3ebf26609b
+size 1953067
diff --git a/recommender/A_SASRec_final_bce_llm.py b/recommender/A_SASRec_final_bce_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc7c94999a4db5f1b97971a2913baae44687c124
--- /dev/null
+++ b/recommender/A_SASRec_final_bce_llm.py
@@ -0,0 +1,222 @@
+import numpy as np
+import pandas as pd
+import argparse
+import torch
+from torch import nn
+import torch.nn.functional as F
+import os
+import logging
+import time as Time
+from collections import Counter
+from SASRecModules_ori import *
+
+def extract_axis_1(data, indices):
+    res = []
+    for i in range(data.shape[0]):
+        res.append(data[i, indices[i], :])
+    res = torch.stack(res, dim=0).unsqueeze(1)
+    return res
+
+class GRU(nn.Module):
+    def __init__(self, hidden_size, item_num, state_size, gru_layers=1):
+        super(GRU, self).__init__()
+        self.hidden_size = hidden_size
+        self.item_num = item_num
+        self.state_size = state_size
+        self.item_embeddings = nn.Embedding(
+            num_embeddings=item_num + 1,
+            embedding_dim=self.hidden_size,
+        )
+        nn.init.normal_(self.item_embeddings.weight, 0, 0.01)
+        self.gru = nn.GRU(
+            input_size=self.hidden_size,
+            hidden_size=self.hidden_size,
+            num_layers=gru_layers,
+            batch_first=True
+        )
+        self.s_fc = nn.Linear(self.hidden_size, self.item_num)
+
+    def forward(self, states, len_states):
+        # Supervised Head
+        emb = self.item_embeddings(states)
+        emb_packed = torch.nn.utils.rnn.pack_padded_sequence(emb, len_states, batch_first=True, enforce_sorted=False)
+        emb_packed, hidden = self.gru(emb_packed)
+        hidden = hidden.view(-1, hidden.shape[2])
+        supervised_output = self.s_fc(hidden)
+        return supervised_output
+
+    def forward_eval(self, states, len_states):
+        # Supervised Head
+        emb = self.item_embeddings(states)
+        emb_packed = torch.nn.utils.rnn.pack_padded_sequence(emb, len_states, batch_first=True, enforce_sorted=False)
+        emb_packed, hidden = self.gru(emb_packed)
+        hidden = hidden.view(-1, hidden.shape[2])
+        supervised_output = self.s_fc(hidden)
+
+        return supervised_output
+
+
+class Caser(nn.Module):
+    def __init__(self, hidden_size, item_num, state_size, num_filters, filter_sizes,
+                 dropout_rate):
+        super(Caser, self).__init__()
+        self.hidden_size = hidden_size
+        self.item_num = int(item_num)
+        self.state_size = state_size
+        self.filter_sizes = eval(filter_sizes)
+        self.num_filters = num_filters
+        self.dropout_rate = dropout_rate
+        self.item_embeddings = nn.Embedding(
+            num_embeddings=item_num + 1,
+            embedding_dim=self.hidden_size,
+        )
+
+        # init embedding
+        nn.init.normal_(self.item_embeddings.weight, 0, 0.01)
+
+        # Horizontal Convolutional Layers
+        self.horizontal_cnn = nn.ModuleList(
+            [nn.Conv2d(1, self.num_filters, (i, self.hidden_size)) for i in self.filter_sizes])
+        # Initialize weights and biases
+        for cnn in self.horizontal_cnn:
+            nn.init.xavier_normal_(cnn.weight)
+            nn.init.constant_(cnn.bias, 0.1)
+
+        # Vertical Convolutional Layer
+        self.vertical_cnn = nn.Conv2d(1, 1, (self.state_size, 1))
+        nn.init.xavier_normal_(self.vertical_cnn.weight)
+        nn.init.constant_(self.vertical_cnn.bias, 0.1)
+
+        # Fully Connected Layer
+        self.num_filters_total = self.num_filters * len(self.filter_sizes)
+        final_dim = self.hidden_size + self.num_filters_total
+        self.s_fc = nn.Linear(final_dim, item_num)
+
+        # dropout
+        self.dropout = nn.Dropout(self.dropout_rate)
+
+    def forward(self, states, len_states):
+        input_emb = self.item_embeddings(states)
+        mask = torch.ne(states, self.item_num).float().unsqueeze(-1)
+        input_emb *= mask
+        input_emb = input_emb.unsqueeze(1)
+        pooled_outputs = []
+        for cnn in self.horizontal_cnn:
+            h_out = nn.functional.relu(cnn(input_emb))
+            h_out = h_out.squeeze()
+            p_out = nn.functional.max_pool1d(h_out, h_out.shape[2])
+            pooled_outputs.append(p_out)
+
+        h_pool = torch.cat(pooled_outputs, 1)
+        h_pool_flat = h_pool.view(-1, self.num_filters_total)
+
+        v_out = nn.functional.relu(self.vertical_cnn(input_emb))
+        v_flat = v_out.view(-1, self.hidden_size)
+
+        out = torch.cat([h_pool_flat, v_flat], 1)
+        out = self.dropout(out)
+        supervised_output = self.s_fc(out)
+
+        return supervised_output
+
+    def forward_eval(self, states, len_states):
+        input_emb = self.item_embeddings(states)
+        mask = torch.ne(states, self.item_num).float().unsqueeze(-1)
+        input_emb *= mask
+        input_emb = input_emb.unsqueeze(1)
+        pooled_outputs = []
+        for cnn in self.horizontal_cnn:
+            h_out = nn.functional.relu(cnn(input_emb))
+            h_out = h_out.squeeze()
+            p_out = nn.functional.max_pool1d(h_out, h_out.shape[2])
+            pooled_outputs.append(p_out)
+
+        h_pool = torch.cat(pooled_outputs, 1)
+        h_pool_flat = h_pool.view(-1, self.num_filters_total)
+
+        v_out = nn.functional.relu(self.vertical_cnn(input_emb))
+        v_flat = v_out.view(-1, self.hidden_size)
+
+        out = torch.cat([h_pool_flat, v_flat], 1)
+        out = self.dropout(out)
+        supervised_output = self.s_fc(out)
+        
+        return supervised_output
+
+
+class SASRec(nn.Module):
+    def __init__(self, hidden_size, item_num, state_size, dropout, device, num_heads=1):
+        super().__init__()
+        self.state_size = state_size
+        self.hidden_size = hidden_size
+        self.item_num = int(item_num)
+        self.dropout = nn.Dropout(dropout)
+        self.device = device
+        self.item_embeddings = nn.Embedding(
+            num_embeddings=item_num + 1,
+            embedding_dim=hidden_size,
+        )
+        nn.init.normal_(self.item_embeddings.weight, 0, 1)
+        self.positional_embeddings = nn.Embedding(
+            num_embeddings=state_size,
+            embedding_dim=hidden_size
+        )
+        self.emb_dropout = nn.Dropout(dropout)
+        self.ln_1 = nn.LayerNorm(hidden_size)
+        self.ln_2 = nn.LayerNorm(hidden_size)
+        self.ln_3 = nn.LayerNorm(hidden_size)
+        self.mh_attn = MultiHeadAttention(hidden_size, hidden_size, num_heads, dropout)
+        self.feed_forward = PositionwiseFeedForward(hidden_size, hidden_size, dropout)
+        self.s_fc = nn.Linear(hidden_size, item_num)
+
+    def forward(self, states, len_states):
+        inputs_emb = self.item_embeddings(states)
+        inputs_emb += self.positional_embeddings(torch.arange(self.state_size).to(self.device))
+        seq = self.emb_dropout(inputs_emb)
+        mask = torch.ne(states, self.item_num).float().unsqueeze(-1).to(self.device)
+        seq *= mask
+        seq_normalized = self.ln_1(seq)
+        mh_attn_out = self.mh_attn(seq_normalized, seq)
+        ff_out = self.feed_forward(self.ln_2(mh_attn_out))
+        ff_out *= mask
+        ff_out = self.ln_3(ff_out)
+        state_hidden = extract_axis_1(ff_out, len_states - 1)
+        supervised_output = self.s_fc(state_hidden).squeeze()
+        return supervised_output
+
+    def forward_eval(self, states, len_states):
+        inputs_emb = self.item_embeddings(states)
+        inputs_emb += self.positional_embeddings(torch.arange(self.state_size).to(self.device))
+        seq = self.emb_dropout(inputs_emb)
+        mask = torch.ne(states, self.item_num).float().unsqueeze(-1).to(self.device)
+        seq *= mask
+        seq_normalized = self.ln_1(seq)
+        mh_attn_out = self.mh_attn(seq_normalized, seq)
+        ff_out = self.feed_forward(self.ln_2(mh_attn_out))
+        ff_out *= mask
+        ff_out = self.ln_3(ff_out)
+        state_hidden = extract_axis_1(ff_out, len_states - 1)
+        supervised_output = self.s_fc(state_hidden).squeeze()
+        return supervised_output
+    
+    def cacul_h(self, states, len_states):
+        device = self.device  
+        states = states.to(device)
+        inputs_emb = self.item_embeddings(states)
+        inputs_emb += self.positional_embeddings(torch.arange(self.state_size).to(self.device))
+        seq = self.emb_dropout(inputs_emb)
+        mask = torch.ne(states, self.item_num).float().unsqueeze(-1).to(self.device)
+        seq *= mask
+        seq_normalized = self.ln_1(seq)
+        mh_attn_out = self.mh_attn(seq_normalized, seq)
+        ff_out = self.feed_forward(self.ln_2(mh_attn_out))
+        ff_out *= mask
+        ff_out = self.ln_3(ff_out)
+        state_hidden = extract_axis_1(ff_out, len_states - 1)
+        # print("state_hidden.size", state_hidden.size())
+        return state_hidden
+    
+    def cacu_x(self, x):
+        x = self.item_embeddings(x)
+
+        return x
diff --git a/recommender/__init__.py b/recommender/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f1b15f87b265590ade9141801d5c1c0b1d9a731c
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,150 @@
+accelerate==0.18.0
+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+altair==4.2.2
+anyio==3.6.2
+appdirs==1.4.4
+asttokens==2.2.1
+async-timeout==4.0.2
+attrs==22.2.0
+backcall==0.2.0
+backports.functools-lru-cache==1.6.5
+bitsandbytes==0.37.2
+black==23.3.0
+certifi==2023.7.22
+charset-normalizer==3.1.0
+click==8.1.3
+cmake==3.26.1
+comm==0.1.3
+contourpy==1.0.7
+cycler==0.11.0
+datasets==2.10.1
+debugpy==1.6.6
+decorator==5.1.1
+dill==0.3.6
+docker-pycreds==0.4.0
+entrypoints==0.4
+executing==1.2.0
+fastapi==0.95.0
+ffmpy==0.3.0
+filelock==3.10.7
+fire==0.5.0
+fonttools==4.39.3
+frozenlist==1.3.3
+fsspec==2023.3.0
+gitdb==4.0.10
+GitPython==3.1.31
+gradio==3.23.0
+h11==0.14.0
+httpcore==0.16.3
+httpx==0.23.3
+huggingface-hub==0.13.3
+idna==3.4
+importlib-metadata==6.8.0
+ipykernel==6.22.0
+ipython==8.11.0
+jedi==0.18.2
+Jinja2==3.1.2
+joblib==1.2.0
+jsonschema==4.17.3
+jupyter_client==8.1.0
+jupyter_core==5.3.0
+kiwisolver==1.4.4
+linkify-it-py==2.0.0
+lit==16.0.0
+markdown-it-py==2.2.0
+MarkupSafe==2.1.2
+matplotlib==3.7.1
+matplotlib-inline==0.1.6
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.4
+multiprocess==0.70.14
+mypy-extensions==1.0.0
+nest-asyncio==1.5.6
+networkx==3.0
+numpy==1.24.2
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+orjson==3.8.9
+packaging==23.0
+pandas==1.5.3
+parso==0.8.3
+pathspec==0.11.1
+pathtools==0.1.2
+peft==0.3.0
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.4.0
+pip==23.2.1
+platformdirs==3.10.0
+prompt-toolkit==3.0.38
+protobuf==4.22.1
+psutil==5.9.4
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==11.0.0
+pydantic==1.10.7
+pydub==0.25.1
+Pygments==2.14.0
+pyparsing==3.0.9
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2023.3
+PyYAML==6.0
+pyzmq==25.0.2
+regex==2023.3.23
+requests==2.28.2
+responses==0.18.0
+rfc3986==1.5.0
+scikit-learn==1.2.2
+scipy==1.10.1
+seaborn==0.12.2
+semantic-version==2.10.0
+sentencepiece==0.1.97
+sentry-sdk==1.19.1
+setproctitle==1.3.2
+setuptools==68.1.2
+six==1.16.0
+smmap==5.0.0
+sniffio==1.3.0
+stack-data==0.6.2
+starlette==0.26.1
+sympy==1.11.1
+termcolor==2.2.0
+threadpoolctl==3.1.0
+tokenize-rt==5.0.0
+tokenizers==0.13.2
+tomli==2.0.1
+toolz==0.12.0
+torch==2.0.0
+tornado==6.2
+tqdm==4.65.0
+traitlets==5.9.0
+transformers==4.28.0
+triton==2.0.0
+typing_extensions==4.5.0
+uc-micro-py==1.0.1
+urllib3==1.26.15
+uvicorn==0.21.1
+wandb==0.14.1
+wcwidth==0.2.6
+websockets==10.4
+wheel==0.41.2
+xxhash==3.2.0
+yarl==1.8.2
+zipp==3.16.2
+pytorch_lightning==1.8.6
+bitsandbytes==0.37.2
\ No newline at end of file
diff --git a/setup.sh b/setup.sh
new file mode 100755
index 0000000000000000000000000000000000000000..15467ecba6f2cfe22b5994659498cbca8b13de7e
--- /dev/null
+++ b/setup.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+uv sync --offline
+cp debug/utils.py .venv/lib/python3.10/site-packages/transformers/generation/utils.py
+cp debug/modeling_llama.py .venv/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py
+
+export LLM_PATH=meta-llama/Llama-2-7b-hf
+
+# 获取/usr/local下所有cuda-目录（末尾可能带/，需要去掉）
+cuda_dirs=(/usr/local/cuda-*/)
+if [ ${#cuda_dirs[@]} -eq 0 ]; then
+    echo "未在 /usr/local 找到任何 cuda 文件夹"
+    exit 1
+fi
+
+# 去除末尾的斜杠，并利用sort -V按版本排序，选取最后一个作为最新版本
+latest_cuda=$(for dir in "${cuda_dirs[@]}"; do echo "${dir%/}"; done | sort -V | tail -n 1)
+
+echo "最新的 CUDA 文件夹为: $latest_cuda"
+
+# 构造新的LD_LIBRARY_PATH
+# 依次加入以下路径：
+#   1. ${latest_cuda}/lib64/stubs
+#   2. ${latest_cuda}/lib64
+#   3. ${latest_cuda}/cudnn/lib
+export CUDA_HOME="${latest_cuda}"
+export PATH="${latest_cuda}/bin:${PATH}"
+export LD_LIBRARY_PATH="${latest_cuda}/lib64/stubs:${latest_cuda}/lib64:${latest_cuda}/cudnn/lib:${LD_LIBRARY_PATH}"
+
+sh train_movielens.sh
diff --git a/test_lastfm.sh b/test_lastfm.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c0d31dd71e4aa66f263a3aa8a1ae1befca018d02
--- /dev/null
+++ b/test_lastfm.sh
@@ -0,0 +1,21 @@
+python main.py \
+--mode test \
+--router share \
+--gating Dense \
+--batch_size 8 \
+--accumulate_grad_batches 16 \
+--dataset lastfm_data \
+--data_dir data/ref/lastfm \
+--cans_num 20 \
+--prompt_path ./prompt/artist.txt \
+--rec_embed SASRec \
+--llm_tuning moelora \
+--llm_path $LLM_PATH \
+--rec_model_path ./rec_model/lastfm.pt \
+--ckpt_path ./checkpoints/lastfm.ckpt \
+--output_dir ./output/lastfm/ \
+--log_dir lastfm_logs \
+--lr_warmup_start_lr 7e-6 \
+--lr 7e-4 \
+--lr_decay_min_lr 7e-6 \
+--max_epochs 5
diff --git a/test_movielens.sh b/test_movielens.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e058b23fe77f98fb5dc7f2d636b5e8d1556c3de7
--- /dev/null
+++ b/test_movielens.sh
@@ -0,0 +1,21 @@
+python main.py \
+--mode test \
+--router share \
+--gating Dense \
+--batch_size 8 \
+--accumulate_grad_batches 16 \
+--dataset movielens_data \
+--data_dir data/ref/movielens \
+--cans_num 20 \
+--prompt_path ./prompt/movie.txt \
+--rec_embed SASRec \
+--llm_tuning moelora \
+--llm_path $LLM_PATH \
+--rec_model_path ./rec_model/movielens.pt \
+--ckpt_path ./checkpoints/movielens.ckpt \
+--output_dir ./output/movielens/ \
+--log_dir movielens_logs \
+--lr_warmup_start_lr 8e-6 \
+--lr 8e-4 \
+--lr_decay_min_lr 8e-6 \
+--max_epochs 5
diff --git a/test_steam.sh b/test_steam.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6b45bb0e5d088da0383dbf7363732ee89bccc8f5
--- /dev/null
+++ b/test_steam.sh
@@ -0,0 +1,21 @@
+python main.py \
+--mode test \
+--router share \
+--gating Dense \
+--batch_size 4 \
+--accumulate_grad_batches 32 \
+--dataset steam_data \
+--data_dir data/ref/steam \
+--cans_num 20 \
+--prompt_path ./prompt/game.txt \
+--rec_embed SASRec \
+--llm_tuning moelora \
+--llm_path $LLM_PATH \
+--rec_model_path ./rec_model/steam.pt \
+--ckpt_path ./checkpoints/steam.ckpt \
+--output_dir ./output/steam/ \
+--log_dir steam_logs \
+--lr_warmup_start_lr 5e-6 \
+--lr 5e-4 \
+--lr_decay_min_lr 5e-6 \
+--max_epochs 5
diff --git a/train_lastfm.sh b/train_lastfm.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f0e105a332b0e9f6356c894718fb1db9ba1c89e9
--- /dev/null
+++ b/train_lastfm.sh
@@ -0,0 +1,21 @@
+python main.py \
+--mode train \
+--router share \
+--gating Dense \
+--batch_size 8 \
+--accumulate_grad_batches 16 \
+--dataset lastfm_data \
+--data_dir data/ref/lastfm \
+--cans_num 20 \
+--prompt_path ./prompt/artist.txt \
+--rec_embed SASRec \
+--llm_tuning moelora \
+--llm_path $LLM_PATH \
+--rec_model_path ./rec_model/lastfm.pt \
+--ckpt_dir ./checkpoints/lastfm/ \
+--output_dir ./output/lastfm/ \
+--log_dir lastfm_logs \
+--lr_warmup_start_lr 7e-6 \
+--lr 7e-4 \
+--lr_decay_min_lr 7e-6 \
+--max_epochs 5
diff --git a/train_movielens.sh b/train_movielens.sh
new file mode 100644
index 0000000000000000000000000000000000000000..bf116422de045dee0ae66095ce86223e4f4c741d
--- /dev/null
+++ b/train_movielens.sh
@@ -0,0 +1,21 @@
+python main.py \
+--mode train \
+--router share \
+--gating Dense \
+--batch_size 1 \
+--accumulate_grad_batches 4 \
+--dataset movielens_data \
+--data_dir data/ref/movielens \
+--cans_num 20 \
+--prompt_path ./prompt/movie.txt \
+--rec_embed SASRec \
+--llm_tuning moelora \
+--llm_path $LLM_PATH \
+--rec_model_path ./rec_model/movielens.pt \
+--ckpt_dir ./checkpoints/movielens/ \
+--output_dir ./output/movielens/ \
+--log_dir movielens_logs \
+--lr_warmup_start_lr 8e-6 \
+--lr 8e-4 \
+--lr_decay_min_lr 8e-6 \
+--max_epochs 5
diff --git a/train_steam.sh b/train_steam.sh
new file mode 100644
index 0000000000000000000000000000000000000000..2a72dd39d39576620080ec36c5ef7f6b2b56c694
--- /dev/null
+++ b/train_steam.sh
@@ -0,0 +1,21 @@
+python main.py \
+--mode train \
+--router share \
+--gating Dense \
+--batch_size 4 \
+--accumulate_grad_batches 32 \
+--dataset steam_data \
+--data_dir data/ref/steam \
+--cans_num 20 \
+--prompt_path ./prompt/game.txt \
+--rec_embed SASRec \
+--llm_tuning moelora \
+--llm_path $LLM_PATH \
+--rec_model_path ./rec_model/steam.pt \
+--ckpt_dir ./checkpoints/steam/ \
+--output_dir ./output/steam/ \
+--log_dir steam_logs \
+--lr_warmup_start_lr 5e-6 \
+--lr 5e-4 \
+--lr_decay_min_lr 5e-6 \
+--max_epochs 5
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000000000000000000000000000000000000..f42566f6ca565a73ddb446b5097f7dd10b4476e9
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2698 @@
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