app.py

#!/usr/bin/env python

from collections.abc import Iterator
from threading import Thread

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

MAX_INPUT_TOKEN_LENGTH = 4096

model_id = "Zyphra/Zamba2-7B-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)


@spaces.GPU(duration=90)
def generate(
    message: str,
    chat_history: list[dict],
) -> Iterator[str]:
    conversation = [*chat_history, {"role": "user", "content": message}]

    input_ids = tokenizer.apply_chat_template(conversation, 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=60.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=MAX_INPUT_TOKEN_LENGTH,
        do_sample=False,
        num_beams=1,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)


demo = gr.ChatInterface(
    fn=generate,
    stop_btn=None,
    examples=[
        ["Hello there! How are you doing?"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["How many hours does it take a man to eat a Helicopter?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
    cache_examples=False,
    type="messages",
    description="# Zamba2-7B-instruct",
    css_paths="style.css",
)

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