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| from transformers import AutoTokenizer, MistralForCausalLM | |
| import torch | |
| import gradio as gr | |
| import random | |
| from textwrap import wrap | |
| from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM | |
| from peft import PeftModel, PeftConfig | |
| import torch | |
| import gradio as gr | |
| import os | |
| import huggingface | |
| from huggingface_hub import login | |
| hf_token = os.environ.get('HUGGINGFACE_TOKEN') | |
| login(hf_token) | |
| # Functions to Wrap the Prompt Correctly | |
| def wrap_text(text, width=90): | |
| lines = text.split('\n') | |
| wrapped_lines = [textwrap.fill(line, width=width) for line in lines] | |
| wrapped_text = '\n'.join(wrapped_lines) | |
| return wrapped_text | |
| def multimodal_prompt(user_input, system_prompt="You are an expert medical analyst:"): | |
| # Combine user input and system prompt | |
| formatted_input = f"{user_input}{system_prompt}" | |
| # Encode the input text | |
| encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False) | |
| model_inputs = encodeds.to(device) | |
| # Generate a response using the model | |
| output = model.generate( | |
| **model_inputs, | |
| max_length=max_length, | |
| use_cache=True, | |
| early_stopping=True, | |
| bos_token_id=model.config.bos_token_id, | |
| eos_token_id=model.config.eos_token_id, | |
| pad_token_id=model.config.eos_token_id, | |
| temperature=0.1, | |
| do_sample=True | |
| ) | |
| # Decode the response | |
| response_text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| return response_text | |
| # Define the device | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Use the base model's ID | |
| base_model_id = "stabilityai/stablelm-3b-4e1t" | |
| model_directory = "vaishakgkumar/stablemedv1" | |
| # Instantiate the Tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t", token=hf_token, trust_remote_code=True, padding_side="left") | |
| # tokenizer = AutoTokenizer.from_pretrained("vaishakgkumar/stablemedv3", trust_remote_code=True, padding_side="left") | |
| tokenizer.pad_token = tokenizer.eos_token | |
| tokenizer.padding_side = 'left' | |
| # Load the PEFT model | |
| peft_config = PeftConfig.from_pretrained("vaishakgkumar/stablemedv1", token=hf_token) | |
| peft_model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t", token=hf_token, trust_remote_code=True) | |
| peft_model = PeftModel.from_pretrained(peft_model, "vaishakgkumar/stablemedv1", token=hf_token) | |
| class ChatBot: | |
| def __init__(self): | |
| self.history = [] | |
| def predict(self, user_input, system_prompt="You are an expert analyst and provide assessment:"): | |
| prompt = [{'role': 'user', 'content': user_input + "\n" + system_prompt + ":"}] | |
| inputs = tokenizer.apply_chat_template( | |
| prompt, | |
| add_generation_prompt=True, | |
| return_tensors='pt' | |
| ) | |
| # Generate a response using the model | |
| tokens = peft_model.generate( | |
| inputs.to(model.device), | |
| max_new_tokens=512, | |
| temperature=0.8, | |
| do_sample=False | |
| ) | |
| # Decode the response | |
| response_text = tokenizer.decode(tokens[0], skip_special_tokens=False) | |
| # Free up memory | |
| del tokens | |
| torch.cuda.empty_cache() | |
| return response_text | |
| bot = ChatBot() | |
| title = "StableDoc Chat" | |
| description = """ | |
| You can use this Space to test out the current model vaishakgkumar/stablemedv3. | |
| """ | |
| iface = gr.Interface( | |
| fn=bot.predict, | |
| title=title, | |
| description=description, | |
| inputs=["text"], # Take user input and system prompt separately | |
| outputs="text", | |
| theme="ParityError/Anime" | |
| ) | |
| iface.launch() |