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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| # Load your fine-tuned model | |
| model_id = "ragunath-ravi/distilgpt2-lmsys-chat" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| # Set padding token to be the same as EOS token if not set | |
| if tokenizer.pad_token is None: | |
| tokenizer.pad_token = tokenizer.eos_token | |
| # Move model to GPU if available | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = model.to(device) | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| # Format the conversation history as expected by the model | |
| prompt = system_message + "\n\n" | |
| for user_msg, assistant_msg in history: | |
| if user_msg: | |
| prompt += f"User: {user_msg}\n" | |
| if assistant_msg: | |
| prompt += f"Assistant: {assistant_msg}\n" | |
| # Add the latest user message | |
| prompt += f"User: {message}\nAssistant:" | |
| # Tokenize the prompt | |
| inputs = tokenizer(prompt, return_tensors="pt").to(device) | |
| # Generate response | |
| with torch.no_grad(): | |
| output = model.generate( | |
| inputs["input_ids"], | |
| max_new_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| do_sample=True, | |
| pad_token_id=tokenizer.eos_token_id, | |
| attention_mask=inputs["attention_mask"], | |
| ) | |
| # Decode the generated response | |
| full_response = tokenizer.decode(output[0], skip_special_tokens=True) | |
| # Extract only the assistant's part from the full response | |
| assistant_response = full_response[len(prompt):].strip() | |
| # Sometimes the model might continue with "User:" - we need to cut that off | |
| if "User:" in assistant_response: | |
| assistant_response = assistant_response.split("User:")[0].strip() | |
| # Stream the response token by token (simulated for this model) | |
| response_so_far = "" | |
| tokens = assistant_response.split() | |
| for token in tokens: | |
| response_so_far += token + " " | |
| yield response_so_far.strip() | |
| # Create the Gradio chat interface | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a helpful assistant.", label="System message"), | |
| gr.Slider(minimum=1, maximum=512, value=128, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.9, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| title="DistilGPT-2 Chat Assistant", | |
| description="A simple chatbot powered by a fine-tuned DistilGPT-2 model on the LMSYS Chat 1M dataset.", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |