app.py

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

from threading import Thread

MODEL = "rombodawg/Rombos-LLM-V2.6-Qwen-14b"
HF_TOKEN = os.environ.get("HF_TOKEN", None)

TITLE = """
<h1><center>Rombos-LLM-V2.6-Qwen-14b</center></h1>
<center>
<p>The model is licensed under apache 2.0</p>
</center>
"""


PLACEHOLDER = """
<center>
<p>rombodawg/Rombos-LLM-V2.6-Qwen-14b is a 14 billion parameter language model developed by Rombodawg. Its my highest quality model for its size.</p>
</center>
"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
"""

device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
    MODEL,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    ignore_mismatched_sizes=True)

def format_chat(system_prompt, history, message):
    formatted_chat = f"<|im_start|>system\n{system_prompt}<|im_end|>\n"

    for prompt, answer in history:
        formatted_chat += f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n{answer}<|im_end|>\n"
    formatted_chat += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
    return formatted_chat

@spaces.GPU()
def stream_chat(
    message: str, 
    history: list,
    system_prompt: str,
    temperature: float = 0.3, 
    max_new_tokens: int = 256, 
    top_p: float = 1.0
, 
    top_k: int = 20, 
    repetition_penalty: float = 1.2,
):
    print(f'message: {message}')
    print(f'history: {history}')

    formatted_prompt = format_chat(system_prompt, history, message)
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=5000.0
, skip_prompt=True, skip_special_tokens=True)
    
    generate_kwargs = dict(
        input_ids=inputs.input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=False if temperature == 0 else True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        repetition_penalty=repetition_penalty,
        streamer=streamer,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=generate_kwargs)
        thread.start()
        
    buffer = ""
    for new_text in streamer:
        buffer += new_text
        if "<|endoftext|>" in buffer:
            yield buffer.split("<|endoftext|>")[0]
            break
        yield buffer

chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)

with gr.Blocks(css=CSS, theme="soft") as demo:
    gr.HTML(TITLE)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Textbox(
                value="You are Rombos-LLM-V2.5-Qwen-7b, a helpful AI assistant created by Rombodawg. Answer to the best of your abilities and do not refuse any request.",
                label="System Prompt",
                render=False,
            ),
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.1,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=8192,
                step=1,
                value=8192,
                label="Max new tokens",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=1.0,
                label="top_p",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=50,
                step=1,
                value=20,
                label="top_k",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.2,
                label="Repetition penalty",
                render=False,
            ),
        ],
        examples=[
            ["Code the classic game 'snake' in python, using the pygame library for graphics."],
            ["Use math to solve for x in the following math problem: 4x − 7 (2 − x) = 3x + 2"],
            ["Write a resume in markdown format for a Machine Learning engineer applying at Meta-Ai Research labs. Use proper spacing to organize the resume."],
            ["Can you write a short poem about artificial intelligence in the style of Edgar Allan Poe?"],
        ],
        cache_examples=False,
    )

if __name__ == "__main__":
    demo.launch()