Instructions to use WeiboAI/VibeThinker-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WeiboAI/VibeThinker-3B with Transformers:

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

pipe = pipeline("text-generation", model="WeiboAI/VibeThinker-3B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WeiboAI/VibeThinker-3B")
model = AutoModelForCausalLM.from_pretrained("WeiboAI/VibeThinker-3B", device_map="auto")
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 Settings

vLLM

How to use WeiboAI/VibeThinker-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WeiboAI/VibeThinker-3B"
# 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-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WeiboAI/VibeThinker-3B

SGLang

How to use WeiboAI/VibeThinker-3B 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-3B" \
    --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-3B",
		"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-3B" \
        --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-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WeiboAI/VibeThinker-3B with Docker Model Runner:
```
docker model run hf.co/WeiboAI/VibeThinker-3B
```

junlinzhang commited on Jun 19

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@@ -19,8 +19,10 @@ library_name: transformers
 <blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;">
     <span style="font-weight: bold;">🚨 </span> 1.This model was not trained on tool-calling or agent-based programming data. We therefore do not recommend using it for tasks that involve function calling, API orchestration, or autonomous coding agents.
 For programming tasks, we recommend using this model on competitive programming problems (e.g., LeetCode-style).
-  <br>2.If you are interested in testing advanced mathematical problems, you may use [AMOBench]（https://huggingface.co/datasets/meituan-longcat/AMO-Bench）. It is a math problem set more challenging than the International Mathematical Olympiad (IMO), and standard answers are included. You can evaluate VibeThinker’s mathematical reasoning capability by comparing its outputs with those of other state-of-the-art mainstream models.
-    Note: Due to the extreme difficulty of the problems, we recommend raising the maximum token length of the model’s context window to 60K–100K.
 </blockquote>
 <p align="center"><a href="https://github.com/WeiboAI/VibeThinker">GitHub</a>&nbsp;&nbsp;|&nbsp;&nbsp;<a href="https://modelscope.cn/models/WeiboAI/VibeThinker-3B">ModelScope</a>&nbsp;&nbsp;|&nbsp;&nbsp;<a href="https://huggingface.co/papers/2606.16140">Technical Report</a></p>

 <blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;">
     <span style="font-weight: bold;">🚨 </span> 1.This model was not trained on tool-calling or agent-based programming data. We therefore do not recommend using it for tasks that involve function calling, API orchestration, or autonomous coding agents.
 For programming tasks, we recommend using this model on competitive programming problems (e.g., LeetCode-style).
+  <br>
+  <br>
+  2.For harder math reasoning, try [AMOBench](https://huggingface.co/datasets/meituan-longcat/AMO-Bench), a problem set harder than the International Mathematical Olympiad (IMO), with included standard answers. Use it to evaluate VibeThinker against other SOTA models. Note: due to extreme difficulty, set max tokens to 60K–100K.
 </blockquote>
 <p align="center"><a href="https://github.com/WeiboAI/VibeThinker">GitHub</a>&nbsp;&nbsp;|&nbsp;&nbsp;<a href="https://modelscope.cn/models/WeiboAI/VibeThinker-3B">ModelScope</a>&nbsp;&nbsp;|&nbsp;&nbsp;<a href="https://huggingface.co/papers/2606.16140">Technical Report</a></p>