from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from peft import PeftModel, PeftConfig import torch import gradio as gr # Use the base model's ID base_model_id = "mistralai/Mistral-7B-v0.1" model_directory = "Tonic/mistralmed" # Instantiate the Models tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = 'left' # Load the PEFT model peft_config = PeftConfig.from_pretrained("Tonic/mistralmed") base_model = AutoModelForSeq2SeqLM.from_pretrained(model_directory) peft_model = PeftModel.from_pretrained(base_model, "Tonic/mistralmed") class ChatBot: def __init__(self): self.history = [] def predict(self, input): # Encode user input user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt") # Concatenate the user input with chat history if self.history: chat_history_ids = torch.cat([self.history, user_input_ids], dim=-1) else: chat_history_ids = user_input_ids # Generate a response using the PEFT model response = peft_model.generate(chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id) # Update chat history self.history = response # Decode and return the response response_text = tokenizer.decode(response[0], skip_special_tokens=True) return response_text bot = ChatBot() title = "👋🏻Welcome to Tonic's MistralMed Chat🚀" description = "You can use this Space to test out the current model (MistralMed) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on Discord to build together." examples = [["What is the boiling point of nitrogen"]] iface = gr.Interface( fn=bot.predict, title=title, description=description, examples=examples, inputs="text", outputs="text", theme="ParityError/Anime" ) iface.launch()