Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,918 Bytes
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import gradio as gr
import torch, threading, time, spaces
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
# ---------------------
# Model Config
# ---------------------
MODEL_ID = "WeiboAI/VibeThinker-1.5B"
SYSTEM_PROMPT = "You are a concise solver. Respond briefly with the correct answer."
print(f"⏳ Loading {MODEL_ID} …")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
trust_remote_code=True,
low_cpu_mem_usage=True,
dtype=torch.bfloat16,
device_map="auto"
)
print("✅ Model ready.")
# ---------------------
# Chat Function
# ---------------------
@spaces.GPU(duration=60)
def chat_fn(message, history):
history = history or []
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
for user_msg, bot_msg in history:
if user_msg: messages.append({"role": "user", "content": user_msg})
if bot_msg: messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
gen_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=200,
temperature=0.3,
top_p=0.9,
do_sample=False,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
repetition_penalty=1.15
)
thread = threading.Thread(target=model.generate, kwargs=gen_kwargs)
thread.start()
partial_text = ""
for new_text in streamer:
partial_text += new_text
yield partial_text
# ---------------------
# UI
# ---------------------
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("## 💡 VibeThinker-1.5B · Edge/ZeroGPU (Streaming Stable)")
chatbot = gr.Chatbot(label="Chatbot", height=500)
msg_box = gr.Textbox(label="Textbox", placeholder="Type here…")
send_btn = gr.Button("Send", variant="primary")
def user_message(message, history):
history = history or []
return "", history + [[message, None]]
def bot_response(history):
user_message = history[-1][0]
response = ""
for partial in chat_fn(user_message, history[:-1]):
response = partial
history[-1][1] = response
yield history
msg_box.submit(user_message, [msg_box, chatbot], [msg_box, chatbot], queue=False).then(
bot_response, chatbot, chatbot
)
send_btn.click(user_message, [msg_box, chatbot], [msg_box, chatbot], queue=False).then(
bot_response, chatbot, chatbot
)
if __name__ == "__main__":
demo.queue(max_size=16).launch() |