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| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| from peft import PeftModel, PeftConfig | |
| import gradio as gr | |
| import os | |
| import huggingface | |
| from huggingface_hub import login | |
| # using hf token to login | |
| hf_token = os.environ.get('HUGGINGFACE_TOKEN') | |
| login(hf_token) | |
| # Define the device | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Load tokenizer and model | |
| tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-3b-4e1t',token=hf_token, trust_remote_code=True) | |
| config = PeftConfig.from_pretrained("vaishakgkumar/stablemedv1") | |
| model = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t",token=hf_token, trust_remote_code=True) | |
| model = PeftModel.from_pretrained(model, "vaishakgkumar/stablemedv1",token=hf_token) | |
| model.to(device) | |
| 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 = model.generate( | |
| inputs.to(model.device), | |
| max_new_tokens=250, | |
| 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 = "👋🏻Welcome to StableLM MED chat" | |
| description = """ | |
| """ | |
| examples = [["What is the proper treatment for buccal herpes?", "Please provide information on the most effective antiviral medications and home remedies for treating buccal herpes."]] | |
| iface = gr.Interface( | |
| fn=bot.predict, | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| inputs=["text", "text"], | |
| outputs="text", | |
| theme="ParityError/Anime" | |
| ) | |
| iface.launch() |