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import os
import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download #, login
#login(os.getenv("HF_TOKEN"))# my bad now its public
model = Llama(
model_path=hf_hub_download(
repo_id=os.environ.get("REPO_ID", "bartowski/HuatuoGPT-o1-7B-GGUF"),#"bartowski/HuatuoGPT-o1-7B-v0.1-GGUF"),
filename=os.environ.get("MODEL_FILE", "HuatuoGPT-o1-7B-Q4_K_M.gguf"),#"HuatuoGPT-o1-7B-v0.1-Q4_0.gguf"),
)
)
DESCRIPTION = '''
# FreedomIntelligence/HuatuoGPT-o1-7B | Duplicate the space and set it to private for faster & personal inference for free.
HuatuoGPT-o1 is a medical LLM designed for advanced medical reasoning.
It generates a complex thought process, reflecting and refining its reasoning, before providing a final response.
**To start a new chat**, click "clear" and start a new dialog.
'''
LICENSE = """
--- Apache 2.0 License ---
"""
def user(message, history):
return "", history + [{"role": "user", "content": message}]
def generate_text(history, max_tokens=512, temperature=0.9, top_p=0.95):
"""Generate a response using the Llama model."""
messages = [{"role": item["role"], "content": item["content"]} for item in history[:-1]]
message = history[-1]['content']
response = model.create_chat_completion(
messages=messages + [{"role": "user", "content": message}],
temperature=temperature,
max_tokens=max_tokens,
top_p=top_p,
stream=True,
)
history.append({"role": "assistant", "content": ""})
for streamed in response:
delta = streamed["choices"][0].get("delta", {})
text_chunk = delta.get("content", "")
history[-1]['content'] += text_chunk
yield history
with gr.Blocks() as demo:
gr.Markdown(DESCRIPTION)
chatbot = gr.Chatbot(type="messages")
msg = gr.Textbox()
clear = gr.Button("Clear")
with gr.Accordion("Adjust Parameters", open=False):
max_tokens = gr.Slider(minimum=512, maximum=4096, value=1024, step=1, label="Max Tokens")
temperature = gr.Slider(minimum=0.1, maximum=1.5, value=0.9, step=0.1, label="Temperature")
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
generate_text, [chatbot, max_tokens, temperature, top_p], chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
gr.Examples(
examples=[
["How many r's are in the word strawberry?"],
['How to stop a cough?'],
['How do I relieve feet pain?'],
],
inputs=msg,
label="Examples",
)
gr.Markdown(LICENSE)
if __name__ == "__main__":
demo.launch() |