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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
class VibeThinkerChat:
def __init__(self, model_path="WeiboAI/VibeThinker-1.5B"):
print("Loading model and tokenizer...")
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto"
)
self.tokenizer = AutoTokenizer.from_pretrained(
model_path,
trust_remote_code=True
)
print("Model loaded successfully!")
def generate_response(self, prompt, temperature=0.6, max_tokens=40960, top_p=0.95):
messages = [
{"role": "user", "content": prompt}
]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
generation_config = dict(
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
top_p=top_p,
top_k=1
)
generated_ids = self.model.generate(
model_inputs.input_ids,
**generation_config
)
generated_ids = [
output_ids[len(input_ids):]
for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
# Initialize model
chat_model = VibeThinkerChat()
def chat_interface(message, history, temperature, max_tokens):
try:
response = chat_model.generate_response(
message,
temperature=temperature,
max_tokens=max_tokens
)
return response
except Exception as e:
return f"Error: {str(e)}"
# Create Gradio interface
with gr.Blocks(title="VibeThinker-1.5B Chat") as demo:
gr.Markdown("# 🧠 VibeThinker-1.5B Chat Interface")
gr.Markdown("A 1.5B parameter reasoning model optimized for math and coding problems.")
with gr.Row():
with gr.Column(scale=3):
chatbot = gr.Chatbot(height=500)
msg = gr.Textbox(
label="Your Message",
placeholder="Ask a math or coding question...",
lines=3
)
with gr.Row():
submit = gr.Button("Submit", variant="primary")
clear = gr.Button("Clear")
with gr.Column(scale=1):
temperature = gr.Slider(
minimum=0.1,
maximum=2.0,
value=0.6,
step=0.1,
label="Temperature",
info="Recommended: 0.6 or 1.0"
)
max_tokens = gr.Slider(
minimum=512,
maximum=40960,
value=4096,
step=512,
label="Max Tokens",
info="Maximum response length"
)
def user_message(user_msg, history):
return "", history + [[user_msg, None]]
def bot_response(history, temp, max_tok):
user_msg = history[-1][0]
bot_msg = chat_interface(user_msg, history, temp, max_tok)
history[-1][1] = bot_msg
return history
msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
bot_response, [chatbot, temperature, max_tokens], chatbot
)
submit.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
bot_response, [chatbot, temperature, max_tokens], chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
if __name__ == "__main__":
demo.queue()
demo.launch()
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