KakTakOne/VibeThinker-3B-GGUF

This repository contains GGUF format model files for WeiboAI/VibeThinker-3B.

VibeThinker-3B is a 3-billion-parameter dense reasoning model designed for verifiable reasoning tasks like mathematics, competitive programming, and STEM.

llama-cli -m VibeThinker-3B-Q4_K_M.gguf -p "2+2=" -n 128

Available Quantizations

Introduction

VibeThinker-3B is a further exploration of the VibeThinker series at the 3B-parameter scale, focusing on challenging reasoning tasks with clear verification signals, such as mathematics, coding, and STEM. By systematically optimizing the Spectrum-to-Signal Principle (SSP) post-training pipeline introduced in VibeThinker-1.5B, VibeThinker-3B achieves strong performance on AIME, HMMT, IMO-AnswerBench, LiveCodeBench, and recent LeetCode contests, reaching the performance range of top-tier frontier reasoning models, including Qwen3.6 Plus, Gemini 3 Pro, GLM-5, and Kimi K2.5, on verifiable reasoning benchmarks.

Key Performance Data

• 📏 In terms of reasoning accuracy relative to model scale, VibeThinker-3B reaches 76.4 on IMO-AnswerBench, a highly challenging benchmark with 400 IMO-level problems, with only 3B parameters, and improves to 80.6 with Claim-Level Reliability Assessment (CLR), a test-time scaling strategy. This demonstrates that a model within a strictly small-model regime can reach the performance range of substantially larger models, such as DeepSeek V3.2 (78.3, 671B), GLM-5 (82.5, 744B), and Kimi K2.5 (81.8, 1T).
• 🏆 To further test the model's out-of-distribution performance, it was evaluated on recent unseen LeetCode weekly and biweekly contests (Python) from Apr. 25 to May 31, 2026. VibeThinker-3B passes 123/128 first-attempt submissions, corresponding to a 96.1% acceptance rate.

Training Pipeline

VibeThinker-3B follows the Spectrum-to-Signal Principle (SSP). The SFT stage constructs a broad spectrum of valid reasoning trajectories, while the RL stage amplifies correct reasoning signals using verifiable rewards:

1. Curriculum-based two-stage SFT (Stage 1: broad capability coverage, Stage 2: harder/longer samples).
2. Multi-domain Reasoning RL using MaxEnt-Guided Policy Optimization (MGPO) with a 64K context window.
3. Offline Self-Distillation using a learning-potential score to distill high-quality trajectories back into a student model.
4. Instruct RL to improve format controllability on user-facing prompts.

How to use

You can load these GGUF files in LM Studio, Ollama, llama.cpp, or any other GGUF-compatible inference engine.

LM Studio

Search for KakTakOne/VibeThinker-3B-GGUF directly in LM Studio search bar and download the desired quantization.

CLI (llama.cpp)

llama-cli -m VibeThinker-3B-Q4_K_M.gguf -p "2+2=" -n 128

Citations & References

@misc{xu2026vibethinker3bexploringfrontierverifiable,
      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 Zhibin Yin and Yirong Chen and Xin Zhou and Junlin Zhang},
      year={2026},
      eprint={2606.16140},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2606.16140}, 
}

Quantized by KakTakOne using llama-quantize.