This release provides a compact and efficient MLX conversion of VibeThinker-3B for local inference while preserving the strong reasoning, mathematics, and coding capabilities of the original model.

About VibeThinker-3B

VibeThinker-3B is a compact 3B parameter reasoning model developed by WeiboAI to explore the limits of verifiable reasoning in small language models.

Building upon the Spectrum-to-Signal (SSP) training methodology, VibeThinker-3B combines curriculum-based supervised fine-tuning, reinforcement learning, and self-distillation to achieve strong performance across:

Mathematical reasoning
Competitive programming
Code generation
STEM problem solving
Instruction following
Long-form reasoning

Despite its small size, VibeThinker-3B achieves performance competitive with significantly larger reasoning models on several challenging benchmarks. Its development focuses on maximizing reasoning efficiency rather than scaling parameter count alone.

Quantization

This release uses standard oQ quantization.

Format: MLX
Quantization: oQ6
Group Size: 64
Target Platform: Apple Silicon

This is not an oQ mixed-precision quantization. The model uses standard oQ6 quantization for efficient local deployment.

Recommended Settings

For best reasoning performance:

yaml

temp: 0.9
top_p: 0.95
rep_penalty: 1.05
presence_penalty: 1.5
enable_thinking: true

These settings encourage deeper reasoning chains while maintaining output diversity and stable long-form responses.

Example Usage

python

from mlx_lm import load, generate

model, tokenizer = load("VibeThinker-3B-oQ6")

prompt = """
Solve this problem step by step:

If a train travels 240 km in 3 hours,
what is its average speed?
"""

response = generate(
    model,
    tokenizer,
    prompt=prompt,
    temp=0.9,
    top_p=0.95,
)

print(response)

Intended Use

This model is well suited for:

Mathematical reasoning
Programming assistance
Algorithm design
Competitive programming practice
STEM education
Research experiments
Local reasoning agents

Hardware Recommendations

Recommended:

Apple M1 Pro / Max
Apple M2 Pro / Max / Ultra
Apple M3 Series
Apple M4 Series

Compatible with:

MLX
oMLX
Open WebUI
Custom agent frameworks
Local Apple Silicon deployments

Credits

Original Model

All credit for the original model, training methodology, datasets, and research belongs to the WeiboAI team.

Original model:

WeiboAI/VibeThinker-3B

Quantized Release

This repository contains only an oQ6 quantized conversion of the original model for efficient Apple Silicon deployment.

Acknowledgements

WeiboAI
VibeThinker Research Team
Apple MLX
Hugging Face

References

VibeThinker-3B extends the Spectrum-to-Signal training paradigm and demonstrates that frontier-level reasoning capabilities can emerge in compact language models through optimized post-training rather than parameter scaling alone.

Citation

Please cite the original VibeThinker-3B paper when using this model in research.

bibtex

@article{xu2026vibethinker3b,
  title={VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models},
  author={Sen Xu and Shixi Liu and Wei Wang and Jixin Min and Yingwei Dai and others},
  year={2026},
  eprint={2606.16140},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

yugeshkarunamurthy/VibeThinker-3B-oQ6

VibeThinker-3B-oQ6