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| from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM | |
| 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("mistralai/Mistral-7B-v0.1", trust_remote_code=True) | |
| #tokenizer.pad_token = tokenizer.eos_token | |
| #tokenizer.padding_side = 'left' | |
| # Specify the configuration class for the model | |
| #model_config = AutoConfig.from_pretrained(base_model_id) | |
| # Load the PEFT model with the specified configuration | |
| #peft_model = AutoModelForCausalLM.from_pretrained(base_model_id, config=model_config) | |
| # Load the PEFT model | |
| peft_config = PeftConfig.from_pretrained("Tonic/mistralmed", token="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") | |
| peft_model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True) | |
| peft_model = PeftModel.from_pretrained(peft_model, "Tonic/mistralmed", token="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") | |
| 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) | |
| # response = peft_model.generate(chat_history_ids) | |
| response = peft_model.generate(input_ids=chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id) | |
| # Update chat history | |
| self.history = chat_history_ids | |
| # 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() | |