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 = """\
# OpenELM-3B-Instruct

This Space demonstrates [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) by Apple. Please, check the original model card for details.
You can see the other models of the OpenELM family [here](https://huggingface.co/apple/OpenELM)
The following Colab notebooks are available:
* [OpenELM-3B-Instruct (GPU)](https://gist.github.com/Norod/4f11bb36bea5c548d18f10f9d7ec09b0)
* [OpenELM-270M (CPU)](https://gist.github.com/Norod/5a311a8e0a774b5c35919913545b7af4)

You might also be interested in checking out Apple's [CoreNet Github page](https://github.com/apple/corenet?tab=readme-ov-file).

If you duplicate this space, make sure you have access to [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
because this model uses it as a tokenizer.

# Note: Use this model for only for completing sentences and instruction following.
## While the user interface is a chatbot for convenience, this is an instruction tuned model not fine-tuned for chatbot tasks. As such, the model is not provided a chat history and will complete your text based on the last given prompt only.
"""

LICENSE = """
<p/>

---
As a derivative work of [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) by Apple,
this demo is governed by the original [license](https://huggingface.co/apple/OpenELM-3B-Instruct/blob/main/LICENSE).
"""

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

# Global variables
model = None
tokenizer = None

def initialize_model_and_tokenizer():
    global model, tokenizer
    if torch.cuda.is_available():
        model_id = "apple/OpenELM-3B-Instruct"    
        model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, low_cpu_mem_usage=True)
        tokenizer_id = "meta-llama/Llama-2-7b-hf"
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token
            tokenizer.pad_token_id = tokenizer.eos_token_id
    else:
        print("CUDA is not available. Model and tokenizer will not be initialized.")

initialize_model_and_tokenizer()

@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]:
    global model, tokenizer

    if tokenizer is None or model is None:
        yield "Error: Model or tokenizer not initialized. Make sure you have GPU support and the necessary model access."
        return

    try:
        input_ids = tokenizer([message], 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=True,
        )
        t = Thread(target=model.generate, kwargs=generate_kwargs)
        t.start()

        outputs = []
        for text in streamer:
            outputs.append(text)
            yield "".join(outputs)
    except Exception as e:
        yield f"An error occurred: {str(e)}"

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()