Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,630 Bytes
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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import spaces
# Model configuration
MODEL_ID = "WeiboAI/VibeThinker-1.5B"
SYSTEM_PROMPT = "You are a concise solver. Respond briefly."
# Load model and tokenizer
def load_model():
"""Load the model and tokenizer"""
try:
print(f"Loading model: {MODEL_ID}")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16,
device_map="auto",
)
print("Model loaded successfully!")
return model, tokenizer
except Exception as e:
print(f"Error loading model: {e}")
raise
# Initialize model and tokenizer
try:
model, tokenizer = load_model()
except Exception as e:
print(f"Failed to load model: {e}")
model = None
tokenizer = None
@spaces.GPU
def chat_response(message, history):
"""
Generate response for the chat interface.
Args:
message (str): Current user message
history (list): Chat history as list of tuples [(user_msg, assistant_msg), ...]
Returns:
str: Generated response
"""
if model is None or tokenizer is None:
return "Model not loaded. Please check the model configuration."
try:
# Build conversation format
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
# Add chat history
for user_msg, assistant_msg in history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": assistant_msg})
# Add current message
messages.append({"role": "user", "content": message})
# Apply chat template
formatted_input = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Tokenize input
model_inputs = tokenizer([formatted_input], return_tensors="pt").to(model.device)
# Generate response
with torch.no_grad():
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id
)
# Decode response
generated_ids = [
output_ids[len(input_ids):]
for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response.strip()
except Exception as e:
print(f"Error generating response: {e}")
return f"Sorry, I encountered an error: {str(e)}"
def create_demo():
"""Create the Gradio chat interface"""
# Create chat interface
demo = gr.ChatInterface(
fn=chat_response,
title="VibeThinker-1.5B Chat",
description=f"Chat with {MODEL_ID}. {SYSTEM_PROMPT}",
examples=[
"What is 2+2?",
"Explain quantum physics briefly",
"Write a short poem",
"How do I make good decisions?"
],
theme=gr.themes.Soft(),
show_progress="minimal",
retry_btn="🔄 Retry",
undo_btn="↩️ Undo",
clear_btn="🗑️ Clear",
)
return demo
if __name__ == "__main__":
demo = create_demo()
demo.launch(share=False) |