app.py

from threading import Thread
import gradio as gr
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    AutoConfig,
    TextIteratorStreamer
)

MODEL_ID = "universeTBD/astrollama"
WINDOW_SIZE = 4096
DEVICE = "cuda"

config = AutoConfig.from_pretrained(pretrained_model_name_or_path=MODEL_ID)

tokenizer = AutoTokenizer.from_pretrained(
    pretrained_model_name_or_path=MODEL_ID
)

model = AutoModelForCausalLM.from_pretrained(
    pretrained_model_name_or_path=MODEL_ID,
    config=config,
    device_map="auto",
    use_safetensors=True,
    trust_remote_code=True,
    load_in_4bit=True,
    torch_dtype=torch.bfloat16
)


def generate_text(prompt: str,
                  max_new_tokens: int = 512,
                  temperature: float = 0.5,
                  top_p: float = 0.95,
                  top_k: int = 50) -> str:
    # Encode the prompt
    inputs = tokenizer([prompt],
                       return_tensors='pt',
                       add_special_tokens=False).to(DEVICE)
    # Prepare arguments for generation
    input_length = inputs["input_ids"].shape[-1]
    max_new_tokens = min(max_new_tokens, WINDOW_SIZE - input_length)
    if temperature >= 1.0:
        temperature = 0.99
    elif temperature <= 0.0:
        temperature = 0.01
    if top_p > 1.0 or top_p <= 0.0:
        top_p = 1.0
    if top_k <= 0:
        top_k = 100
    streamer = TextIteratorStreamer(tokenizer,
                                    timeout=10.,
                                    skip_prompt=True,
                                    skip_special_tokens=True)
    generation_kwargs = dict(
        **inputs,
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
    )
    # Generate text
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    generated_text = prompt
    for new_text in streamer:
        generated_text += new_text
    return generated_text


demo = gr.Interface(
    fn=generate_text,
    inputs=[
        # Prompt
        gr.Textbox(
            label="Prompt",
            container=False,
            show_label=False,
            placeholder="Enter some text...",
            lines=10,
            scale=10,
        ),
        gr.Slider(
            label="Maximum new tokens",
            minimum=1,
            maximum=4096,
            step=1,
            value=1024,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.01,
            maximum=0.99,
            step=0.01,
            value=0.5,
        ),
        gr.Slider(
            label="Top-p (for sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.95,
        ),
        gr.Slider(
            label='Top-k (for sampling)',
            minimum=1,
            maximum=1000,
            step=1,
            value=100,
        )
    ],
    outputs=[
        gr.Textbox(
            container=False,
            show_label=False,
            placeholder="Generated output...",
            scale=10,
            lines=10,
        )
    ],
)

demo.queue(max_size=20).launch()