app.py

#!/usr/bin/env python
import os
from typing import Iterator

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

from chat_interface_preference import ChatInterface

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "8192"))

if torch.cuda.is_available():
    # model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
    # model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
    # tokenizer = AutoTokenizer.from_pretrained(model_id)
    pass

style = None

AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer


@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.06,
    top_p: float = 0.95,
    top_k: int = 40,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    # conversation = []
    # for user, assistant in chat_history:
    #     conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
    # conversation.append({"role": "user", "content": message})

    # input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
    # 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)

    for char in "help":
        yield "help"


chat_interface = ChatInterface(
    fn=generate,
    prefence_techniques="dpo",
    min_turns=1,
    max_turns=10,
    repo_id="llm-human-feedback-collector-chat-interface-dpo",
    chatbot=gr.Chatbot(height=450, label="Meta-Llama-3.1-8B-Instruct", show_share_button=True),
    cache_examples=False,
    additional_inputs=[
        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=0.05,
            maximum=1.2,
            step=0.05,
            value=0.2,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.9,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.2,
        ),
    ],
    examples=[
        ["""What word doesn't make sense in this row: "car, airplane, lama, bus"?"""],
        ["Write a news article about the usage of Lama's by the CSI"],
        ["What are great things cook when getting started with Asian cooking?"],
        ["Who was Anthony Bourdain?"],
    ],
    title="💪🏽🦾 LLM human-feedback collector ChatInterface (DPO) 🦾💪🏽",
    description="""This is an adaptation of the gr.ChatInferface which allows for human feedback collection for SFT, DPO and KTO.""",
)

with gr.Blocks(css="style.css") as demo:
    chat_interface.render()

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