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

Libraries

How to use KakTakOne/VibeThinker-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KakTakOne/VibeThinker-3B-GGUF",
	filename="VibeThinker-3B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KakTakOne/VibeThinker-3B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use KakTakOne/VibeThinker-3B-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Ollama
How to use KakTakOne/VibeThinker-3B-GGUF with Ollama:
```
ollama run hf.co/KakTakOne/VibeThinker-3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use KakTakOne/VibeThinker-3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for KakTakOne/VibeThinker-3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for KakTakOne/VibeThinker-3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KakTakOne/VibeThinker-3B-GGUF to start chatting

How to use KakTakOne/VibeThinker-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KakTakOne/VibeThinker-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use KakTakOne/VibeThinker-3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use KakTakOne/VibeThinker-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/KakTakOne/VibeThinker-3B-GGUF:Q4_K_M
```

Lemonade

How to use KakTakOne/VibeThinker-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KakTakOne/VibeThinker-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.VibeThinker-3B-GGUF-Q4_K_M

List all available models

lemonade list

VibeThinker-3B-GGUF / README.md

KakTakOne

Upload folder using huggingface_hub

b211bf8 verified 11 days ago

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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`.