app.py

import os
from collections.abc import Iterator
from threading import Thread

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

# Token limits
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 512

# Description
DESCRIPTION = """\
# Demo for "Self-Training Elicits Concise Reasoning in Large Language Models"

This Space showcases the model [tergel/llama-3.2-3b-instruct-gsm8k-fs-gpt4o-bon](https://huggingface.co/tergel/llama-3.2-3b-instruct-gsm8k-fs-gpt4o-bon)

We provide a simple chat interface allowing you to observe the concise CoT solutions that our model can produce. Feel free to play with it.
"""

# Decide on device
device = "cuda" if torch.cuda.is_available() else "cpu"

if not torch.cuda.is_available():
    DESCRIPTION += "\n\n<p>**Warning**: Running on CPU 🥶 – this may be extremely slow. We will upgrade to GPUs soon.</p>"
    
# Load model and tokenizer
model_id = "tergel/llama-3.2-3b-instruct-gsm8k-fs-gpt4o-bon"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map=None if device == "cpu" else "auto",
    torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32,
)
model.to(device)

tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.use_default_system_prompt = False

@spaces.GPU
def generate(
    message: str,
    chat_history: list[dict],
    system_prompt: str = "",
    max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,
    temperature: float = 0.7,
    top_p: float = 0.95,
    top_k: int = 40,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    # Build conversation
    conversation = []
    if system_prompt:
        conversation.append({"role": "system", "content": system_prompt})
    conversation += chat_history
    conversation.append({"role": "user", "content": message})
    
    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True)
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    
    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)
    

chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6),
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.1,
            maximum=4.0,
            step=0.1,
            value=0.7,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.95,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=40,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.2,
        ),
    ],
    stop_btn=None,
    examples=[
        [
            "A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?"
        ],
        [
            "Claire makes a 3 egg omelet every morning for breakfast.  How many dozens of eggs will she eat in 4 weeks?"
        ],
        [
            "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week?"
        ],
    ],
    cache_examples=False,
    type="messages",
)

with gr.Blocks(css_paths="style.css", fill_height=True) as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()


if __name__ == "__main__":
    demo.queue(max_size=20).launch()