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import gradio as gr
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
repo_id="AstroMLab/AstroSage-8B-GGUF",
filename="AstroSage-8B-Q8_0.gguf"
)
llm = Llama(
model_path=model_path,
n_ctx=2048,
n_threads=4,
seed=42,
f16_kv=True,
logits_all=False,
use_mmap=True,
use_gpu=True
)
def respond(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
response = llm.generate_chat(
messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p
)
return response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="Assume the role of AstroSage, a helpful chatbot designed to answer user queries about astronomy, astrophysics, and cosmology.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
]
)
if __name__ == "__main__":
demo.launch() |