add config.json

README.md

@@ -164,13 +164,12 @@ [email protected]，提交《TeleChat模型社区许可协议》要求的
 如需引用我们的工作，请使用如下 reference:
 
 ```
-@misc{wang2025technicalreporttelechat2telechat25,
-      title={Technical Report of TeleChat2, TeleChat2.5 and T1}, 
-      author={Zihan Wang and Xinzhang Liu and Yitong Yao and Chao Wang and Yu Zhao and Zhihao Yang and Wenmin Deng and Kaipeng Jia and Jiaxin Peng and Yuyao Huang and Sishi Xiong and Zhuo Jiang and Kaidong Yu and Xiaohui Hu and Fubei Yao and Ruiyu Fang and Zhuoru Jiang and Ruiting Song and Qiyi Xie and Rui Xue and Xuewei He and Yanlei Xue and Zhu Yuan and Zhaoxi Zhang and Zilu Huang and Shiquan Wang and Xin Wang and Hanming Wu and Mingyuan Wang and Xufeng Zhan and Yuhan Sun and Zhaohu Xing and Yuhao Jiang and Bingkai Yang and Shuangyong Song and Yongxiang Li and Zhongjiang He and Xuelong Li},
-      year={2025},
-      eprint={2507.18013},
+@misc{wang2024telechat,
+      title={TeleChat Technical Report}, 
+      author={Zihan Wang and Xinzhang Liu and Shixuan Liu and Yitong Yao and Yuyao Huang and Zhongjiang He and Xuelong Li and Yongxiang Li and Zhonghao Che and Zhaoxi Zhang and Yan Wang and Xin Wang and Luwen Pu and Huihan Xu and Ruiyu Fang and Yu Zhao and Jie Zhang and Xiaomeng Huang and Zhilong Lu and Jiaxin Peng and Wenjun Zheng and Shiquan Wang and Bingkai Yang and Xuewei he and Zhuoru Jiang and Qiyi Xie and Yanhan Zhang and Zhongqiu Li and Lingling Shi and Weiwei Fu and Yin Zhang and Zilu Huang and Sishi Xiong and Yuxiang Zhang and Chao Wang and Shuangyong Song},
+      year={2024},
+      eprint={2401.03804},
       archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2507.18013}, 
+      primaryClass={cs.CL}
 }
 ```

config.json

@@ -1,40 +0,0 @@
-{
-  "apply_residual_connection_post_layernorm": false,
-  "architectures": [
-    "TelechatForCausalLM"
-  ],
-  "auto_map": {
-	"AutoConfig": "configuration_telechat.TelechatConfig",
-	"AutoModelForCausalLM": "modeling_telechat.TelechatForCausalLM"
-  },
-  "attention_dropout": 0.0,
-  "attention_softmax_in_fp32": true,
-  "bias_dropout_fusion": true,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "hidden_dropout": 0.0,
-  "hidden_size": 8192,
-  "initializer_range": 0.02,
-  "layer_norm_epsilon": 1e-05,
-  "masked_softmax_fusion": true,
-  "model_type": "telechat",
-  "n_head": 64,
-  "n_inner": null,
-  "num_key_value_heads": 8,
-  "n_layer": 96,
-  "pad_token_id": 3,
-  "pretraining_tp": 2,
-  "skip_bias_add": false,
-  "skip_bias_add_qkv": false,
-  "slow_but_exact": false,
-  "unk_token_id": 0,
-  "use_cache": true,
-  "vocab_size": 131072,
-  "ffn_hidden_size": 40960,
-  "flash_attn":true,
-  "tie_word_embeddings":false,
-  "training_seqlen":8192,
-  "base_seqlen":8192,
-  "seq_length": 8192
-}
-

configuration.json

@@ -1 +0,0 @@
-{"task":"text-generation"}

configuration_telechat.py

@@ -1,94 +0,0 @@
-# coding=utf-8
-# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team.  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.
-
-""" Telechat configuration"""
-
-from packaging import version
-from collections import OrderedDict
-from transformers.utils import is_torch_available, logging
-from transformers.configuration_utils import PretrainedConfig
-from typing import TYPE_CHECKING, Any, List, Mapping, Optional
-
-logger = logging.get_logger(__name__)
-
-class TelechatConfig(PretrainedConfig):
-    """
-    Args:
-        vocab_size (`int`, *optional*, defaults to 160256): Vocabulary size of the Telechat model.
-        hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states.
-        ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimensionality of the feed-forward hidden states.
-        n_layer (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer
-        n_head (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers.
-        initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
-        hidden_dropout (`float`, *optional*, defaults to 0.0): Dropout rate of the dropout function on the bias dropout.
-        attention_dropout (`float`, *optional*, defaults to 0.0): Dropout rate applied to the attention probs
-        use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions.
-        training_seqlen (`int`, *optional*, defaults to 8192): Sequence length during last finetuning.
-        logn (`bool`, *optional*, defaults to `True`): Whether or not to use logN during extrapolation.
-        embed_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to use embedding layernorm.
-
-    """
-
-    model_type = "telechat"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "num_hidden_layers": "n_layer",
-        "num_attention_heads": "n_head",
-    }
-
-    def __init__(
-        self,
-        vocab_size=160256,
-        hidden_size=4096,
-        n_layer=30,
-        n_head=32,
-        layer_norm_epsilon=1e-5,
-        initializer_range=0.02,
-        use_cache=True,
-        bos_token_id=1,
-        eos_token_id=2,
-        apply_residual_connection_post_layernorm=False,
-        hidden_dropout=0.0,
-        attention_dropout=0.0,
-        ffn_hidden_size=12288,
-        training_seqlen = 8192,
-        logn = True,
-        embed_layernorm = False,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        n_embed = kwargs.pop("n_embed", None)
-        self.hidden_size = hidden_size if n_embed is None else n_embed
-        self.n_layer = n_layer
-        self.n_head = n_head
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.use_cache = use_cache
-        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
-        self.hidden_dropout = hidden_dropout
-        self.attention_dropout = attention_dropout
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-        self.logn = logn
-        self.ffn_hidden_size = ffn_hidden_size
-        self.training_seqlen = training_seqlen
-        self.embed_layernorm = embed_layernorm
-        self.num_key_value_heads= kwargs.pop("num_key_value_heads", None)
-
-
-        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
-

generation_config.json

@@ -1,14 +0,0 @@
-{
-  "max_length": 8192,
-  "do_sample": false,
-  "use_cache": true,
-  "temperature": 0.3,
-  "top_k": 5,
-  "top_p": 0.85,
-  "repetition_penalty": 1.03,
-  "pad_token_id": 3,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "user_token_id": 4,
-  "bot_token_id": 5
-}

generation_utils.py

@@ -1,162 +0,0 @@
-from typing import Optional
-from collections import deque
-from queue import Queue
-import copy
-
-
-class History:
-
-    def __init__(self, tokenizer, history):
-        '''
-        init from a list of dict
-        '''
-        # use deque to meet some special situation
-        self.input_history = deque()
-        self.tokenizer = tokenizer
-        if history:
-            self._transfer_from_list(history)
-
-    def _transfer_from_list(self, history):
-        for message in history:
-            content = message.get("content")
-            # the token result may not be equal to the result model gen
-            message.update(self.tokenizer(content))
-            self.input_history.append(message)
-
-    def append(self, message):
-        content = message.get("content")
-        if "input_ids" not in message or "attention_mask" not in message:
-            message.update(self.tokenizer(content))
-        self.input_history.append(message)
-
-    def append_left(self, message):
-        content = message.get("content")
-        if "input_ids" not in message or "attention_mask" not in message:
-            message.update(self.tokenizer(content))
-        self.input_history.appendleft(message)
-
-    def pop(self):
-        x = self.input_history.pop()
-        return x
-
-    def pop_left(self):
-        x = self.input_history.pop_left()
-        return x
-
-    def update(self, message):
-        self.input_history.pop()
-        self.append(message)
-
-    def __len__(self):
-        return self.input_history.__len__()
-
-    def __str__(self):
-        return self.input_history.__str__()
-
-    def __copy__(self):
-        new_instance = type(self)(self.tokenizer, [])
-        new_instance.input_history = copy.copy(self.input_history)
-        return new_instance
-
-    def __deepcopy__(self, memodict={}):
-        new_instance = type(self)(self.tokenizer, [])
-        new_instance.input_history = copy.deepcopy(self.input_history)
-        return new_instance
-
-
-class TelechatIterTextStreamer:
-    """
-    With reference to the TextIterStreamers in transformers, we have rewritten this class
-    """
-
-    def __init__(
-            self, tokenizer, history: History = None, skip_prompt: bool = False, timeout: Optional[float] = None,
-            **decode_kwargs
-    ):
-
-        self.tokenizer = tokenizer
-        self.history = history
-        self.skip_prompt = skip_prompt
-        self.timeout = timeout
-        self.decode_kwargs = decode_kwargs
-
-        self.text_queue = Queue()
-        self.cache_time = 0
-        self.text_until = ""
-        self.token_until = []
-        self.stop_signal = None
-        self.next_tokens_are_prompt = True
-
-        self.history.append({"role": "bot", "content": self.text_until})
-
-    def put(self, value):
-        """
-        put printable text into queue
-        """
-        if len(value.shape) > 1 and value.shape[0] > 1:
-            raise ValueError("TextStreamer only supports batch size 1")
-        elif len(value.shape) > 1:
-            value = value[0]
-
-        if self.skip_prompt and self.next_tokens_are_prompt:
-            self.next_tokens_are_prompt = False
-            return
-
-        if value[-1] == self.tokenizer.eos_token_id:
-            return
-
-        # there may be some smart way to decode.
-        self.token_until.extend(value.tolist())
-        text = self.tokenizer.decode(self.token_until, **self.decode_kwargs)
-
-
-        if self._is_printable(text) or self.cache_time >= 6:
-            output_text = text[len(self.text_until):]
-            self.text_until = text
-
-        else:
-            self.cache_time+=1
-            return
-
-        self.on_finalized_text(output_text)
-
-    def end(self):
-        """Flushes any remaining cache and prints a newline to stdout."""
-        # Flush the cache, if it exists
-        text = self.tokenizer.decode(self.token_until, **self.decode_kwargs)
-        output_text = text[len(self.text_until):]
-        self.text_until = text
-        self.on_finalized_text(output_text, stream_end=True)
-        self.clear_cache()
-
-    def clear_cache(self):
-        self.cache_time = 0
-        self.token_until = []
-        self.text_until = ""
-        self.history = None
-        self.next_tokens_are_prompt = True
-
-    def on_finalized_text(self, text: str, stream_end: bool = False):
-        """Put the text tuple in the queue."""
-        self.history.update({"role": "bot", "content": self.text_until, "input_ids": self.token_until,
-                             "attention_mask": [1] * len(self.token_until)})
-        self.text_queue.put((text, self.history), timeout=self.timeout)
-        if stream_end:
-            self.text_queue.put((self.stop_signal, self.history), timeout=self.timeout)
-
-    @staticmethod
-    def _is_printable(cp):
-        """Checks whether tokens can be decoded or not"""
-        if "�" in cp:
-            return False
-        return True
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        value_now, history_until = self.text_queue.get(timeout=self.timeout)
-        if value_now == self.stop_signal:
-            raise StopIteration()
-        else:
-            return value_now, history_until

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