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Model: KakTakOne/VibeThinker-3B-GGUF Source: Original Platform
2026-09-06 22:02:16 +08:00
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+---
+license: mit
+language:
+- en
+- ru
+base_model: WeiboAI/VibeThinker-3B
+tags:
+- math
+- code
+- reasoning
+- gpqa
+- instruction-following
+- gguf
+- llama.cpp
+pipeline_tag: text-generation
+---
+
+# KakTakOne/VibeThinker-3B-GGUF
+
+This repository contains GGUF format model files for [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B).
+
+VibeThinker-3B is a 3-billion-parameter dense reasoning model designed for verifiable reasoning tasks like mathematics, competitive programming, and STEM.
+
+<details>
+<summary><b>Читать описание на русском языке (Russian Description)</b></summary>
+
+# KakTakOne/VibeThinker-3B-GGUF
+
+В этом репозитории содержатся файлы моделей в формате GGUF для [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B).
+
+VibeThinker-3B — это модель рассуждений (reasoning model) с 3 миллиардами параметров, сфокусированная на сложных задачах рассуждения с проверяемыми результатами, таких как математика, программирование и STEM.
+
+## Доступные кванты
+
+| Имя файла | Тип кванта | Размер файла | Ссылка |
+| --- | --- | --- | --- |
+| [VibeThinker-3B-f16.gguf](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/blob/main/VibeThinker-3B-f16.gguf) | FP16 | 6.18 ГБ | [Скачать](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/resolve/main/VibeThinker-3B-f16.gguf?download=true) |
+| [VibeThinker-3B-Q8_0.gguf](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/blob/main/VibeThinker-3B-Q8_0.gguf) | Q8_0 | 3.29 ГБ | [Скачать](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/resolve/main/VibeThinker-3B-Q8_0.gguf?download=true) |
+| [VibeThinker-3B-Q5_K_M.gguf](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/blob/main/VibeThinker-3B-Q5_K_M.gguf) | Q5_K_M | 2.22 ГБ | [Скачать](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/resolve/main/VibeThinker-3B-Q5_K_M.gguf?download=true) |
+| [VibeThinker-3B-Q4_K_M.gguf](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/blob/main/VibeThinker-3B-Q4_K_M.gguf) | Q4_K_M | 1.93 ГБ | [Скачать](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/resolve/main/VibeThinker-3B-Q4_K_M.gguf?download=true) |
+
+---
+
+## Введение
+
+VibeThinker-3B продолжает развитие серии моделей рассуждения VibeThinker на масштабе 3 миллиардов параметров. Благодаря оптимизации пайплайна обучения Spectrum-to-Signal Principle (SSP), модель демонстрирует выдающиеся результаты на бенчмарках AIME, HMMT, IMO-AnswerBench, LiveCodeBench и недавних контестах LeetCode, приближаясь по качеству к флагманским коммерческим моделям рассуждения вроде Qwen3.6 Plus, Gemini 3 Pro, GLM-5 и Kimi K2.5.
+
+## Ключевые показатели производительности
+
+* 📏 Модель набирает **76.4** на сложном бенчмарке IMO-AnswerBench (400 олимпиадных задач уровня IMO) с использованием всего 3 млрд параметров, и улучшает результат до **80.6** с применением CLR (Claim-Level Reliability Assessment) на этапе инференса. Это сопоставимо с показателями гораздо более крупных моделей, таких как DeepSeek V3.2 (78.3, 671B), GLM-5 (82.5, 744B) и Kimi K2.5 (81.8, 1T).
+* 🏆 На еженедельных и двухнедельных соревнованиях LeetCode (Python) за период с 25 апреля по 31 мая 2026 года модель успешно прошла **123 из 128** тестов с первой попытки (доля успешных решений составляет **96.1%**).
+
+## Пайплайн обучения
+
+Обучение VibeThinker-3B основано на методологии **Spectrum-to-Signal Principle (SSP)**:
+1. **Curriculum SFT в два этапа**: сначала общая кодовая и математическая база, затем сложные рассуждения с длинным контекстом.
+2. **Multi-domain RL** с алгоритмом MaxEnt-Guided Policy Optimization (MGPO) в окне контекста 64K.
+3. **Офлайн дистилляция на себя (Self-Distillation)** для отбора лучших траекторий рассуждений.
+4. **Instruct RL** для улучшения управляемости и форматирования ответов под пользователя.
+
+---
+
+## Как использовать
+
+Эти файлы GGUF можно запускать в **LM Studio**, **Ollama**, **llama.cpp** и других совместимых клиентах.
+
+### LM Studio
+Просто вбей в строку поиска `KakTakOne/VibeThinker-3B-GGUF` и скачай нужный квант.
+
+### Запуск через консоль (llama.cpp)
+```bash
+llama-cli -m VibeThinker-3B-Q4_K_M.gguf -p "2+2=" -n 128
+```
+
+</details>
+
+---
+
+## Available Quantizations
+
+| File Name | Quant Type | File Size | File Link |
+| --- | --- | --- | --- |
+| [VibeThinker-3B-f16.gguf](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/blob/main/VibeThinker-3B-f16.gguf) | FP16 | 6.18 GB | [Download](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/resolve/main/VibeThinker-3B-f16.gguf?download=true) |
+| [VibeThinker-3B-Q8_0.gguf](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/blob/main/VibeThinker-3B-Q8_0.gguf) | Q8_0 | 3.29 GB | [Download](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/resolve/main/VibeThinker-3B-Q8_0.gguf?download=true) |
+| [VibeThinker-3B-Q5_K_M.gguf](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/blob/main/VibeThinker-3B-Q5_K_M.gguf) | Q5_K_M | 2.22 GB | [Download](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/resolve/main/VibeThinker-3B-Q5_K_M.gguf?download=true) |
+| [VibeThinker-3B-Q4_K_M.gguf](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/blob/main/VibeThinker-3B-Q4_K_M.gguf) | Q4_K_M | 1.93 GB | [Download](https://huggingface.co/KakTakOne/VibeThinker-3B-GGUF/resolve/main/VibeThinker-3B-Q4_K_M.gguf?download=true) |
+
+---
+
+## 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)
+```bash
+llama-cli -m VibeThinker-3B-Q4_K_M.gguf -p "2+2=" -n 128
+```
+
+## Citations & References
+
+```bibtex
+@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](https://huggingface.co/KakTakOne) using `llama-quantize`.*
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