app.py

import os
from threading import Thread
from typing import Iterator

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

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

DESCRIPTION = """\
# Playground with Ghost 8B Beta (p)

**Ghost 8B Beta** is a large language model developed with goals that include excellent multilingual support, superior knowledge capabilities, and cost-effectiveness. The model comes in two context length versions, 8k and 128k, along with multilingual function tools support by default. 

The languages supported are 🇺🇸 English, 🇫🇷 French, 🇮🇹 Italian, 🇪🇸 Spanish, 🇵🇹 Portuguese, 🇩🇪 German, 🇻🇳 Vietnamese, 🇰🇷 Korean and 🇨🇳 Chinese.

📋 Note: current model version is "disl-0x5-8k" (10 Jul 2024), context length 8k and current status is "moderating / previewing". For detailed information about the model, see [here](https://ghost-x.org/docs/models/ghost-8b-beta/). Try to experience it the way you want!
"""


PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <h1 style="font-size: 26px; margin-bottom: 2px; opacity: 0.20;">👻 Ghost 8B Beta</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.10;">Ask and share whatever you want ~</p>
</div>
"""

LICENSE = """
<p/>

---
Ghost 8B Beta may give inaccurate information, including information about people, so please verify Ghost 8B Beta's answers. [Ghost 8B Beta](https://ghost-x.org/docs/models/ghost-8b-beta/) by [Ghost X](https://ghost-x.org).
"""

EXAMPLES = [
    [
        "Explain the concept of quantum entanglement and its implications for quantum computing."
    ],
    ["Comment le mouvement des Lumières a-t-il influencé la Révolution française ?"],
    ["Quale fu l'impatto del Rinascimento italiano sull'arte e la cultura europea?"],
    [
        "Spiega il funzionamento e le applicazioni della spettroscopia Raman in chimica analitica."
    ],
    [
        "Explique el teorema de incompletitud de Gödel y sus implicaciones en la lógica matemática."
    ],
    [
        "Descreva o processo de meiose celular e sua importância na variabilidade genética."
    ],
    [
        "Giải thích nguyên lý hoạt động của máy học sâu (deep learning) trong trí tuệ nhân tạo và ứng dụng của nó trong xử lý ngôn ngữ tự nhiên."
    ],
    ["조선 시대의 신분제도가 한국 사회에 미친 영향을 분석하시오."],
    ["分析丝绸之路对中国古代文化交流和经济发展的影响。"],
]

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"


if torch.cuda.is_available():
    model_id = "lamhieu/ghost-8b-beta-disl-0x5-8k"
    model_tk = os.getenv("HF_TOKEN", None)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map="auto",
        trust_remote_code=True,
        token=model_tk,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_id,
        trust_remote_code=True,
        token=model_tk,
    )


@spaces.GPU(duration=60)
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str,
    max_new_tokens: int = 1024,
    temperature: float = 0.4,
    top_p: float = 0.95,
    top_k: int = 50,
    repetition_penalty: float = 1.0,
) -> Iterator[str]:
    conversation = []
    if system_prompt:
        conversation.append({"role": "system", "content": system_prompt})
    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"
    )
    input_ids = input_ids.to(model.device)
    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."
        )

    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,
        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)


chatbot = gr.Chatbot(height=400, placeholder=PLACEHOLDER, label="Ghost 8B Beta")

chat_interface = gr.ChatInterface(
    fn=generate,
    chatbot=chatbot,
    fill_height=True,
    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=2.0,
            step=0.1,
            value=0.4,
        ),
        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=100,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.0,
        ),
    ],
    stop_btn=None,
    cache_examples=False,
    examples=EXAMPLES,
)

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

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