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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("WeiboAI/VibeThinker-3B")
model = AutoModelForMultimodalLM.from_pretrained("WeiboAI/VibeThinker-3B")
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
```

It's a very strong model for what it is trained! Bravo!

by codingquark-personal - opened 1 day ago

Discussion

codingquark-personal

1 day ago

I gave it quite a few hard problems, within the domain, focused on programming. It performs exceptionally well IMO. Here are a few noteworthy prompts:

https://gist.github.com/codingquark/8df7f68eaafdabbae3498f7e083a0861
https://gist.github.com/codingquark/beaf63593c96ff52bf289d44167433b2
https://gist.github.com/codingquark/091e0b04bee6ccc9f53e80d9e0587c03

Just to give an idea quickly in case you don't want to go through the gists, these are the prompts:

Place 4 rooks on a 6×6 chessboard so that no two share a row or column, and no rook sits on a cell (i,i) of the main diagonal (rows and columns numbered 1–6). How many such placements are there? Give the final answer as \boxed{N}
Write a self-contained Python function count_dominant(a: list[int]) -> int returning the number of contiguous subarrays that are dominant: a subarray is dominant if its maximum element is strictly greater than the sum of all its other elements. For a length-1 subarray there are no other elements, so the "sum of others" is 0 (so it's dominant iff its single element is > 0). The array may contain negatives and duplicates. Aim for better than the brute-force O(n^2) and state the complexity you reach. Explain your approach, then hand-trace on a=[3,-1,2,-5,4], a=[5,5,1], and a=[-2,-3,-1], giving the return value for each.
Let N be the number of functions f:{1,…,8}→{1,…,8} such that f(f(x))=f(x) for every x, and such that there are exactly 3 values x with f(x)=xf. Find N and give it as \boxed{N}.

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