Instructions to use WeiboAI/VibeThinker-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WeiboAI/VibeThinker-1.5B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="WeiboAI/VibeThinker-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WeiboAI/VibeThinker-1.5B")
model = AutoModelForCausalLM.from_pretrained("WeiboAI/VibeThinker-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use WeiboAI/VibeThinker-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WeiboAI/VibeThinker-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WeiboAI/VibeThinker-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WeiboAI/VibeThinker-1.5B

SGLang

How to use WeiboAI/VibeThinker-1.5B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "WeiboAI/VibeThinker-1.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WeiboAI/VibeThinker-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "WeiboAI/VibeThinker-1.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WeiboAI/VibeThinker-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WeiboAI/VibeThinker-1.5B with Docker Model Runner:
```
docker model run hf.co/WeiboAI/VibeThinker-1.5B
```

add huggingface app

by akhaliq HF Staff - opened Nov 13, 2025

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

-0

akhaliq

Nov 13, 2025

add gradio app for this model

add huggingface appb3e150f0

YiZhouDenseHub

WeiboAI org Nov 13, 2025

•

edited Nov 13, 2025

Thanks so much for adding the Gradio app, really appreciate it.
Just to clarify, this model isn’t really intended to be used as a general chatbot yet.
If users want to test it on competitive-style math or Python algorithm problems, please make sure the model supports up to 32k–40k tokens in a single response, as shorter generation limits may cut off the reasoning process.
Thanks again for your support, it really means a lot to us!

YiZhouDenseHub

WeiboAI org Nov 13, 2025

We’ll optimize and shorten the reasoning process in future versions. This release is mainly to verify whether a small model can outperform larger ones in competitive math and coding tasks.

Jason9718

Nov 16, 2025

多搞点小模型大模型我电脑4070ti带不动

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