import gradio as gr from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer import torch import spaces from threading import Thread from typing import Iterator model_id = "mistralai/Mistral-Nemo-Instruct-2407" MAX_INPUT_TOKEN_LENGTH = 4096 # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", load_in_8bit=True ) @spaces.GPU def generate( message: str, chat_history: list[tuple[str, str]], max_new_tokens: int = 1024, temperature: float = 0.6, top_p: float = 0.9 ) -> Iterator[str]: conversation = [] for user, assistant in chat_history: conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) conversation.append({"role": "user", "content": message}) input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt") if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") input_ids = input_ids.to(model.device) streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( {"input_ids": input_ids}, streamer=streamer, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, temperature=temperature, num_beams=1 ) t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] for text in streamer: outputs.append(text) yield "".join(outputs) # Set up Gradio interface iface = gr.ChatInterface( generate, chatbot=gr.Chatbot(height=600), textbox=gr.Textbox(placeholder="Enter your message here...", container=False, scale=7), title="Chat with Mistral Next v1.1", description="This is a chat interface for the Mistral Next v1.1 Chat 4B model. Ask questions and get answers!", theme="soft", retry_btn="Retry", undo_btn="Undo Last", clear_btn="Clear", additional_inputs=[ gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Maximum number of 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=0.95, step=0.05, label="Top-p (nucleus sampling)"), ], ) # Launch the interface iface.launch()