app.py

import spaces
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, TorchAoConfig
from threading import Thread
import torch
from torchao.quantization import Int8DynamicActivationInt8WeightConfig, Int8WeightOnlyConfig

quant_config = Int8DynamicActivationInt8WeightConfig()
#quant_config = Int8WeightOnlyConfig()
quantization_config = TorchAoConfig(quant_type=quant_config)
#checkpoint = "HuggingFaceTB/SmolLM2-135M-Instruct"
checkpoint = "unsloth/gemma-3-4b-it"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
#model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32).to(device)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
                                             device_map=device, quantization_config=quantization_config)

@spaces.GPU(duration=30)
def respond_stream(message, history, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}] + history + [{"role": "user", "content": message}]

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

    streamer = TextIteratorStreamer(
        tokenizer, skip_prompt=True, skip_special_tokens=True
    )
    gen_kwargs = dict(
        inputs=input_ids,
        streamer=streamer,
        max_new_tokens=max_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        eos_token_id=tokenizer.eos_token_id,
        cache_implementation="static",
    )
    thread = Thread(target=model.generate, kwargs=gen_kwargs)
    thread.start()

    partial = ""
    for piece in streamer:
        partial += piece
        yield partial

@spaces.GPU(duration=30)
def respond(message, history, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}] + history + [{"role": "user", "content": message}]

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

    outputs = model.generate(
        input_ids=input_ids,
        max_new_tokens=max_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        eos_token_id=tokenizer.eos_token_id,
        cache_implementation="static",
    )

    gen_ids = outputs[0][input_ids.shape[-1]:]
    return tokenizer.decode(gen_ids, skip_special_tokens=True)

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", 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)"),
    ],
)

with gr.Blocks() as demo:
    chatbot.render()

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