app.py

import gradio as gr
import spaces
import torch

import transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meta-llama/Meta-Llama-3-8B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_name,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)

@spaces.GPU
def chat_function(message, history):
    messages = [
        {"role": "system", "content": "You are a helpful assistant!"},
        {"role": "user", "content": message},
    ]
    prompt = pipeline.tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    terminators = [
        pipeline.tokenizer.eos_token_id,
        pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
    ]
    outputs = pipeline(
        prompt,
        max_new_tokens=256,
        eos_token_id=terminators,
        do_sample=True,
        temperature=0.6,
        top_p=0.9,
    )
    return outputs[0]["generated_text"][len(prompt):]

gr.ChatInterface(
    chat_function,
    chatbot=gr.Chatbot(height=300),
    textbox=gr.Textbox(placeholder="Enter message here", container=False, scale=7),
    title="LLAMA 3 8B Chat",
    description="Chat with Meta LLama 3 8 Billion",
    theme="soft",
).launch()