Update app.py

app.py

@@ -5,17 +5,19 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 import gradio as gr
 import sentencepiece
+from tokenization_xgen import XgenTokenizer
 
-title = "Welcome to Tonic's 🐋🐳Orca-2-13B (in 8bit)!"
-description = "You can use [🐋🐳microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) via API using Gradio by scrolling down and clicking Use 'Via API' or privately by [cloning this space on huggingface](https://huggingface.co/spaces/Tonic1/TonicsOrca2?duplicate=true) . [Join my active builders' server on discord](https://discord.gg/VqTxc76K3u). Let's build together! Big thanks to the HuggingFace Organisation for the Community Grant."
+title = "Welcome to 🙋🏻‍♂️Tonic's🌷Tulu Chat!"
+description = "[allenai/tulu-2-dpo-7b](https://huggingface.co/allenai/tulu-2-dpo-7b) and larger Tulu-2 models are Instruct Llama Finetunes using the [mistralai/Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) recipe. You can use [allenai/tulu-2-13b](https://huggingface.co/allenai/tulu-2-13b) here via API using Gradio by scrolling down and clicking Use 'Via API' or privately by [cloning this space on huggingface](https://huggingface.co/spaces/Tonic1/TuluDemo?duplicate=true) See also the large model here : [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b) . [Join my active builders' server on discord](https://discord.gg/VqTxc76K3u). Let's build together!."
 
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:50'
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model_name = "microsoft/Orca-2-13b"
-tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
-model = transformers.AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
+model_name = "allenai/tulu-2-dpo-13b"
+tokenizer = AutoTokenizer.from_pretrained("allenai/tulu-2-dpo-13b")
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
 
-class OrcaChatBot:
-    def __init__(self, model, tokenizer, system_message="You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."):
+class TuluChatBot:
+    def __init__(self, model, tokenizer, system_message="You are 🌷Tulu, an AI language model created by Tonic-AI. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."):
         self.model = model
         self.tokenizer = tokenizer
         self.system_message = system_message
@@ -24,16 +26,19 @@ class OrcaChatBot:
         self.system_message = new_system_message
 
     def format_prompt(self, user_message):
-        prompt = f"<|im_start|>assistant\n{self.system_message}<|im_end|>\n<|im_start|>\nuser\n{user_message}<|im_end|>\nassistant\n"
+        prompt = f"<|assistant|>\n {self.system_message}\n\n <|user|>{user_message}\n\n<|assistant|>\n"
         return prompt
 
     def predict(self, user_message, temperature=0.4, max_new_tokens=70, top_p=0.99, repetition_penalty=1.9):
         prompt = self.format_prompt(user_message)
         inputs = self.tokenizer(prompt, return_tensors='pt', add_special_tokens=False)
         input_ids = inputs["input_ids"].to(self.model.device)
+        attention_mask = inputs["attention_mask"].to(self.model.device)
+
 
         output_ids = self.model.generate(
             input_ids,
+            attention_mask=attention_mask,
             max_length=input_ids.shape[1] + max_new_tokens,
             temperature=temperature,
             top_p=top_p,
@@ -45,11 +50,11 @@ class OrcaChatBot:
         return response
 
 def gradio_predict(user_message, system_message, max_new_tokens, temperature, top_p, repetition_penalty):
-    Orca_bot.set_system_message(system_message)
-    response = Orca_bot.predict(user_message, temperature, max_new_tokens, top_p, repetition_penalty)
+    Tulu_bot.set_system_message(system_message)
+    response = Tulu_bot.predict(user_message, temperature, max_new_tokens, top_p, repetition_penalty)
     return response
     
-Orca_bot = OrcaChatBot(model, tokenizer)
+Tulu_bot = TuluChatBot(model, tokenizer)
 
 iface = gr.Interface(
     fn=gradio_predict,
@@ -58,8 +63,8 @@ iface = gr.Interface(
     inputs=[
         gr.Textbox(label="Your Message", type="text", lines=3),
         gr.Textbox(label="Introduce a Character Here or Set a Scene (system prompt)", type="text", lines=2),
-        gr.Slider(label="Max new tokens", value=550, minimum=360, maximum=600, step=1),
-        gr.Slider(label="Temperature", value=0.1, minimum=0.05, maximum=1.0, step=0.05),
+        gr.Slider(label="Max new tokens", value=1269, minimum=550, maximum=3200, step=1),
+        gr.Slider(label="Temperature", value=1.2, minimum=0.05, maximum=4.0, step=0.05),
         gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.01, maximum=0.99, step=0.05),
         gr.Slider(label="Repetition penalty", value=1.9, minimum=1.0, maximum=2.0, step=0.05)
     ],