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import spaces
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
# Remove GPU decorator since we are CPU-only
def predict(message, history):
# Load model and tokenizer on CPU
model_id = "kurakurai/Luth-0.6B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cpu", # CPU only
torch_dtype=torch.float16,
trust_remote_code=True,
load_in_4bit=False # 4-bit quantization not supported on CPU
)
# Format conversation history for chat template
messages = [{"role": "user" if i % 2 == 0 else "assistant", "content": msg}
for conv in history for i, msg in enumerate(conv) if msg]
messages.append({"role": "user", "content": message})
# Apply chat template
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
tokenize=True
).to('cpu') # CPU device
# Setup streamer for real-time output
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
# Generation parameters
generate_kwargs = dict(
input_ids=input_ids,
streamer=streamer,
max_new_tokens=256,
do_sample=True,
temperature=0.3,
min_p=0.15,
repetition_penalty=1.05,
pad_token_id=tokenizer.eos_token_id
)
# Start generation in separate thread
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
# Stream tokens
partial_message = ""
for new_token in streamer:
partial_message += new_token
yield partial_message
# Setup Gradio interface
gr.ChatInterface(
predict,
description="""
<center><h2>Kurakura AI Luth-0.6B-Instruct Chat</h2></center>
Chat with [Luth-0.6B-Instruct](https://huggingface.co/kurakurai/Luth-0.6B-Instruct), a French-tuned version of Qwen3-0.6B.
""",
examples=[
"Peux-tu résoudre l'équation 3x - 7 = 11 pour x ?",
"Explique la photosynthèse en termes simples.",
"Écris un petit poème sur l'intelligence artificielle."
],
theme=gr.themes.Soft(primary_hue="purple"),
).launch()
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