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import os
import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
model = Llama(
model_path=hf_hub_download(
repo_id=os.environ.get("REPO_ID", "Lyte/LLaMA-O1-Supervised-1129-Q4_K_M-GGUF"),
filename=os.environ.get("MODEL_FILE", "llama-o1-supervised-1129-q4_k_m.gguf"),
)
)
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.
**To start a new chat**, click "clear" and start a new dialog.
'''
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 generate_text(message, history, max_tokens=512, temperature=0.9, top_p=0.95):
temp = ""
input_texts = [llama_o1_template(message)]
input_texts = [input_text.replace('<|end_of_text|>','') for input_text in input_texts]
#print(f"input_texts[0]: {input_texts[0]}")
inputs = model.tokenize(input_texts[0].encode('utf-8'))
for token in model.generate(inputs, top_p=top_p, temp=temperature):
#print(f"token: {token}")
text = model.detokenize([token])
#print(f"text detok: {text}")
temp += text.decode('utf-8')
yield temp
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?"],
['What is the most optimal way to do Test-Time Scaling?'],
['Explain to me how gravity works like I am 5!'],
],
cache_examples=False,
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() |