Upload medllama_use.py

medllama_use.py

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+# -*- coding: utf-8 -*-
+"""Medllama use.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1pZiJn21DK8U77WfKyxw94zNVYnxR40LP
+"""
+
+#!pip install transformers accelerate peft bitsandbytes gradio
+
+from huggingface_hub import notebook_login
+import torch
+notebook_login()
+
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+config = PeftConfig.from_pretrained("tmberooney/medllama")
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf",load_in_4bit=True, torch_dtype=torch.float16, device_map="auto")
+model = PeftModel.from_pretrained(model, "tmberooney/medllama")
+tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model = model.to('cuda:0')
+
+
+
+
+
+"""### Using Gradio App"""
+
+from transformers import pipeline
+
+llama_pipeline = pipeline(
+    "text-generation",  # LLM task
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    tokenizer=tokenizer
+)
+
+SYSTEM_PROMPT = """<s>[INST] <<SYS>>
+You are a helpful medical bot. Your answers are clear and concise with medical information.
+<</SYS>>
+
+"""
+
+# Formatting function for message and history
+def format_message(message: str, history: list, memory_limit: int = 3) -> str:
+    """
+    Formats the message and history for the Llama model.
+
+    Parameters:
+        message (str): Current message to send.
+        history (list): Past conversation history.
+        memory_limit (int): Limit on how many past interactions to consider.
+
+    Returns:
+        str: Formatted message string
+    """
+    # always keep len(history) <= memory_limit
+    if len(history) > memory_limit:
+        history = history[-memory_limit:]
+
+    if len(history) == 0:
+        return SYSTEM_PROMPT + f"{message} [/INST]"
+
+    formatted_message = SYSTEM_PROMPT + f"{history[0][0]} [/INST] {history[0][1]} </s>"
+
+    # Handle conversation history
+    for user_msg, model_answer in history[1:]:
+        formatted_message += f"<s>[INST] {user_msg} [/INST] {model_answer} </s>"
+
+    # Handle the current message
+    formatted_message += f"<s>[INST] {message} [/INST]"
+
+    return formatted_message
+
+from transformers import TextIteratorStreamer
+streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+
+# Generate a response from the Llama model
+def get_model_response(message: str, history: list) -> str:
+    """
+    Generates a conversational response from the Llama model.
+
+    Parameters:
+        message (str): User's input message.
+        history (list): Past conversation history.
+
+    Returns:
+        str: Generated response from the Llama model.
+    """
+    query = format_message(message, history)
+    response = ""
+
+    sequences = llama_pipeline(
+        query,
+        generation_config = model.generation_config,
+        do_sample=True,
+        top_k=10,
+        streamer=streamer,
+        top_p=0.7,
+        temperature=0.7,
+        num_return_sequences=1,
+        eos_token_id=tokenizer.eos_token_id,
+        max_length=1024,
+    )
+
+    generated_text = sequences[0]['generated_text']
+    response = generated_text[len(query):]  # Remove the prompt from the output
+
+    partial_message  = ""
+    for new_token in streamer:
+        if new_token != '<':
+            partial_message += new_token
+            yield partial_message
+
+import gradio as gr
+
+gr.ChatInterface(fn=get_model_response,
+    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+                 title="Medllama : The Medically Fine-tuned LLaMA-2").queue().launch()
+
+!gradio deploy
+