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# 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(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
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()
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