Instructions to use WeiboAI/VibeThinker-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("WeiboAI/VibeThinker-3B") model = AutoModelForCausalLM.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
- 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
Has anyone tried this
I downloaded this model using hf CLI, because unsloth is notoriously bad at downloading models, but now for some reason Unsloth doesn't accept my model and wants to download it again, which it fails to do of course, so I am stuck with not being able to test this for now
wdym? unsloth is annoyinglyy good at managing models. maybe because this is not a GGUF file :(
wdym? unsloth is annoyinglyy good at managing models. maybe because this is not a GGUF file :(
well I can't load this model in unsloth, it just doesn't do anything, keeps being stuck at loading weights, in the mean time I loaded JohnRoger/VibeThinker-3B-Q8_0-GGUF but I think there is an issue with that model, because it starts reasoning about its own chat template, so I think the chat template is wrong in it
Ahh, sometimes it happens when the community duct type things. letm guess, the model start doing something like:
<think>
the user asked [question] hmm... what does /think mean? maybe the user was trying to indicate thinking traces?
</think>
bcs that is the EXACT thing whch happened to me with an older depsek disill model