app.py

import os

os.system("pip install git+https://github.com/shumingma/transformers.git")

import threading
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TextIteratorStreamer,
)
import gradio as gr
import spaces

# Load model and tokenizer
model_id = "microsoft/bitnet-b1.58-2B-4T"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
print(model.device)

@spaces.GPU
def respond(
    message: str,
    history: list[tuple[str, str]],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    messages = [{"role": "system", "content": system_message}]
    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})
    messages.append({"role": "user", "content": message})

    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    outputs = model.generate(
        **inputs,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    yield response

# Initialize Gradio chat interface

demo = gr.ChatInterface(
    fn=respond,
    title="Bitnet-b1.58-2B-4T Chatbot",
    description="This chat application is powered by Microsoft BitNet-b1.58-2B-4T and designed for natural and fast conversations.",
    examples=[
        # Each example: [message, system_message, max_new_tokens, temperature, top_p]
        [
            "Hello! How are you?",
            "You are a helpful AI assistant.",
            512,
            0.7,
            0.95,
        ],
        [
            "Can you code a snake game in Python?",
            "You are a helpful AI assistant.",
            512,
            0.7,
            0.95,
        ],
    ],
    additional_inputs=[
        gr.Textbox(
            value="You are a helpful AI assistant.",
            label="System message"
        ),
        gr.Slider(
            minimum=1,
            maximum=2048,
            value=512,
            step=1,
            label="Max new tokens"
        ),
        gr.Slider(
            minimum=0.1,
            maximum=4.0,
            value=0.7,
            step=0.1,
            label="Temperature"
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)"
        ),
    ],
)

if __name__ == "__main__":
    demo.launch()