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# import gradio as gr
# from huggingface_hub import InferenceClient
# import os
# client = InferenceClient(
# model="mistralai/Mistral-Small-24B-Instruct-2501",
# token=os.getenv('HF_TOKEN')
# )
# def chat_fn(message, system_message, history_str, max_tokens, temperature, top_p):
# # Convert history string (optional) to message list
# messages = [{"role": "system", "content": system_message}]
# if history_str:
# # Format: user1||assistant1\nuser2||assistant2
# for pair in history_str.split("\n"):
# if "||" in pair:
# user_msg, assistant_msg = pair.split("||", 1)
# messages.append({"role": "user", "content": user_msg})
# messages.append({"role": "assistant", "content": assistant_msg})
# messages.append({"role": "user", "content": message})
# # Get response from HF
# response = ""
# for chunk in client.chat_completion(
# messages=messages,
# stream=True,
# max_tokens=max_tokens,
# temperature=temperature,
# top_p=top_p,
# ):
# response += chunk.choices[0].delta.content or ""
# return response
# demo = gr.Interface(
# fn=chat_fn,
# inputs=[
# gr.Textbox(lines=2, label="User Message"),
# gr.Textbox(value="You are a friendly Chatbot.", label="System Prompt"),
# gr.Textbox(lines=4, placeholder="user||bot\nuser2||bot2", label="Conversation History (optional)"),
# gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max 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"),
# ],
# outputs="text",
# allow_flagging="never",
# title="LLM Budaya",
# description="Chatbot menggunakan model HuggingFace Zephyr-7B"
# )
# if __name__ == "__main__":
# demo.launch()
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load model & tokenizer
model_id = "mistralai/Mistral-Small-24B-Instruct-2501"
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load model di CPU
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float32,
device_map={"": "cpu"}
)
# Inference function
def chat_fn(message, system_prompt, max_tokens, temperature, top_p):
prompt = f"<s>[INST] {system_prompt.strip()}\n{message.strip()} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
decoded = tokenizer.decode(output[0], skip_special_tokens=True)
return decoded.split("[/INST]")[-1].strip()
# Gradio UI
demo = gr.Interface(
fn=chat_fn,
inputs=[
gr.Textbox(lines=2, label="User Message"),
gr.Textbox(value="You are a helpful and concise assistant.", label="System Prompt"),
gr.Slider(minimum=1, maximum=1024, value=256, step=1, label="Max 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"),
],
outputs="text",
title="Mistral-Small-24B CPU Chat",
description="Chatbot menggunakan model Mistral-Small-24B-Instruct-2501 dijalankan lokal via CPU. Ini akan berjalan lambat.",
flagging_mode="never",
)
if __name__ == "__main__":
demo.launch()
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