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 = 1024
DEFAULT_MAX_NEW_TOKENS = 256
MAX_INPUT_TOKEN_LENGTH = 512

DESCRIPTION = """\
# Buzz-3B-Small
This Space demonstrates Buzz-3b-small-v0.6.3.
"""

LICENSE = """
<p/>
---
This demo uses Buzz-3b-small-v0.6.3. Please check the model card for details.
"""

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

model_id = "H-D-T/Buzz-3b-small-v0.6.3"

if torch.cuda.is_available():
    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True)
else:
    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cpu", trust_remote_code=True, low_cpu_mem_usage=True)
    
tokenizer = AutoTokenizer.from_pretrained(model_id)
if tokenizer.pad_token == None:
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.pad_token_id = tokenizer.eos_token_id
    model.config.pad_token_id = tokenizer.eos_token_id

# Define the special tokens
bos_token = "<|begin_of_text|>"
eos_token = "<|eot_id|>"
start_header_id = "<|start_header_id|>"
end_header_id = "<|end_header_id|>"

def format_chat_history(chat_history: list[tuple[str, str]], add_generation_prompt=False) -> str:
    """
    Formats the chat history according to the model's chat template.
    """
    chat_template = f"""
    {{% if not add_generation_prompt is defined %}}{{% set add_generation_prompt = false %}}{{% endif %}}
    {{% set loop_messages = messages %}}
    {{% for message in loop_messages %}}
        {{% set content = '{start_header_id}' + message['role'] + '{end_header_id}\\n\\n' + message['content'].strip() + '{eos_token}' %}}
        {{% if loop.index0 == 0 %}}{{% set content = bos_token + content %}}{{% endif %}}
        {{ content }}
    {{% endfor %}}
    {{% if add_generation_prompt %}}{{ '{start_header_id}assistant{end_header_id}\\n\\n' }}{{% else %}}{{ eos_token }}{{% endif %}}
    """
    chat_context = ""
    for i, (user, assistant) in enumerate(chat_history):
        user_msg = start_header_id + "user" + end_header_id + "\n\n" + user.strip() + eos_token
        assistant_msg = start_header_id + "assistant" + end_header_id + "\n\n" + assistant.strip() + eos_token
        if i == 0:
            user_msg = bos_token + user_msg
        chat_context += user_msg + assistant_msg

    if add_generation_prompt:
        chat_context += start_header_id + "assistant" + end_header_id + "\n\n"
    else:
        chat_context += eos_token

    return chat_context

@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.4,
) -> Iterator[str]:
    
    chat_history.append(("user", message))
    chat_context = format_chat_history(chat_history, add_generation_prompt=True)
    input_ids = tokenizer([chat_context], return_tensors="pt").input_ids
    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,
        pad_token_id = tokenizer.eos_token_id,
        repetition_penalty=repetition_penalty,
        no_repeat_ngram_size=5,
        early_stopping=False,
    )
    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.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=0.6,
        ),
        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=1.4,
        ),
    ],
    stop_btn=None,
    examples=[
        ["A recipe for a chocolate cake:"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["Question: What is the capital of France?\nAnswer:"],
        ["Question: I am very tired, what should I do?\nAnswer:"],
    ],
)

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    chat_interface.render()
    gr.Markdown(LICENSE)

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