app.py

import spaces

import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import hf_hub_download, snapshot_download
import accelerate

accelerator = accelerate.Accelerator()

# Load the model and tokenizer from Hugging Face
model_path = snapshot_download(
    repo_id=os.environ.get("REPO_ID", "SimpleBerry/LLaMA-O1-Supervised-1129")
)

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path,device_map='auto')

DESCRIPTION = '''
# SimpleBerry/LLaMA-O1-Supervised-1129 | Duplicate the space and set it to private for faster & personal inference for free.
SimpleBerry/LLaMA-O1-Supervised-1129: an experimental research model developed by the SimpleBerry.  
Focused on advancing AI reasoning capabilities.  

## This Space was designed by Lyte/LLaMA-O1-Supervised-1129-GGUF, Many Thanks!

**To start a new chat**, click "clear" and start a new dialogue.
'''

LICENSE = """
--- MIT License ---
"""

template = "<start_of_father_id>-1<end_of_father_id><start_of_local_id>0<end_of_local_id><start_of_thought><problem>{content}<end_of_thought><start_of_rating><positive_rating><end_of_rating>\n<start_of_father_id>0<end_of_father_id><start_of_local_id>1<end_of_local_id><start_of_thought><expansion>"

def llama_o1_template(data):
    #query = data['query']
    text = template.format(content=data)
    return text

def format_response(response):
    response = response.replace('<start_of_father_id>','')
    response = response.replace('<end_of_father_id><start_of_local_id>','👉')
    response = response.replace('<end_of_local_id><start_of_thought>',', ')
    response = response.replace('<end_of_thought><start_of_rating>','')
    response = response.replace('<end_of_rating>','')
    response = response.replace('<positive_rating>','👍')
    response = response.replace('<negative_rating>','👎')
    
@spaces.GPU
def generate_text_gpu(message, history, max_tokens=512, temperature=0.9, top_p=0.95):
    input_text = llama_o1_template(message)
    inputs = tokenizer(input_text, return_tensors="pt").to(accelerator.device)

    # Generate the text with the model
    output = model.generate(
        **inputs,
        max_length=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
    )

    response = tokenizer.decode(output[0], skip_special_tokens=False)
    yield response

def generate_text(message, history, max_tokens=512, temperature=0.9, top_p=0.95):
    try:
        yield generate_text_gpu(message, history, max_tokens=512, temperature=0.9, top_p=0.95)
        return
    except Exception as e:
        print(e)
    input_text = llama_o1_template(message)
    inputs = tokenizer(input_text, return_tensors="pt").to(accelerator.device)

    # Generate the text with the model
    output = model.generate(
        **inputs,
        max_length=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
    )

    response = tokenizer.decode(output[0], skip_special_tokens=False)
    yield response

with gr.Blocks() as demo:
    gr.Markdown(DESCRIPTION)

    chatbot = gr.ChatInterface(
        generate_text,
        title="SimpleBerry/LLaMA-O1-Supervised-1129 | GGUF Demo",
        description="Edit Settings below if needed.",
        examples=[
            ["How many r's are in the word strawberry?"],
            ['If Diana needs to bike 10 miles to reach home and she can bike at a speed of 3 mph for two hours before getting tired, and then at a speed of 1 mph until she reaches home, how long will it take her to get home?'],
            ['Find the least odd prime factor of $2019^8+1$.'],
        ],
        cache_examples=True,
        fill_height=True,
    )

    with gr.Accordion("Adjust Parameters", open=False):
        gr.Slider(minimum=1024, maximum=8192, value=2048, step=1, label="Max Tokens")
        gr.Slider(minimum=0.1, maximum=1.5, value=0.7, step=0.1, label="Temperature")
        gr.Slider(minimum=0.05, maximum=1.0, value=0.95, step=0.01, label="Top-p (nucleus sampling)")

    gr.Markdown(LICENSE)

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