Instructions to use r0b0tlab/VibeThinker-3B-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use r0b0tlab/VibeThinker-3B-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="r0b0tlab/VibeThinker-3B-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("r0b0tlab/VibeThinker-3B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("r0b0tlab/VibeThinker-3B-NVFP4", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use r0b0tlab/VibeThinker-3B-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "r0b0tlab/VibeThinker-3B-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "r0b0tlab/VibeThinker-3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/r0b0tlab/VibeThinker-3B-NVFP4

SGLang

How to use r0b0tlab/VibeThinker-3B-NVFP4 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 "r0b0tlab/VibeThinker-3B-NVFP4" \
    --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": "r0b0tlab/VibeThinker-3B-NVFP4",
		"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 "r0b0tlab/VibeThinker-3B-NVFP4" \
        --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": "r0b0tlab/VibeThinker-3B-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use r0b0tlab/VibeThinker-3B-NVFP4 with Docker Model Runner:
```
docker model run hf.co/r0b0tlab/VibeThinker-3B-NVFP4
```

VibeThinker-3B-NVFP4

File size: 2,567 Bytes

2fc0013

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    "eos_token_id": 151643,
    "hidden_act": "silu",
    "hidden_size": 2048,
    "initializer_range": 0.02,
    "intermediate_size": 11008,
    "layer_types": [
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    "num_hidden_layers": 36,
    "num_key_value_heads": 2,
    "pad_token_id": null,
    "rms_norm_eps": 1e-06,
    "rope_parameters": {
        "rope_theta": 1000000.0,
        "rope_type": "default"
    },
    "sliding_window": null,
    "tie_word_embeddings": true,
    "transformers_version": "5.5.4",
    "use_cache": false,
    "use_sliding_window": false,
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    "quantization_config": {
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                "weights": {
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                "targets": [
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        },
        "ignore": [
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        "quant_algo": "NVFP4",
        "producer": {
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    }
}