Instructions to use heath0xFF/VibeThinker-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use heath0xFF/VibeThinker-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="heath0xFF/VibeThinker-3B-GGUF",
	filename="VibeThinker-3B-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use heath0xFF/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 heath0xFF/VibeThinker-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf heath0xFF/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 heath0xFF/VibeThinker-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf heath0xFF/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 heath0xFF/VibeThinker-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf heath0xFF/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 heath0xFF/VibeThinker-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf heath0xFF/VibeThinker-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/heath0xFF/VibeThinker-3B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use heath0xFF/VibeThinker-3B-GGUF with Ollama:
```
ollama run hf.co/heath0xFF/VibeThinker-3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use heath0xFF/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 heath0xFF/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 heath0xFF/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 heath0xFF/VibeThinker-3B-GGUF to start chatting

How to use heath0xFF/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 heath0xFF/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": "heath0xFF/VibeThinker-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use heath0xFF/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 heath0xFF/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 heath0xFF/VibeThinker-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use heath0xFF/VibeThinker-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/heath0xFF/VibeThinker-3B-GGUF:Q4_K_M
```

Lemonade

How to use heath0xFF/VibeThinker-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull heath0xFF/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

File size: 1,355 Bytes

9feb3d5

---
license: apache-2.0
base_model: WeiboAI/VibeThinker-3B
language:
  - en
  - zh
tags:
  - qwen
  - gguf
  - llama.cpp
  - thinking
---

# VibeThinker-3B GGUF

GGUF quantizations of [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B), a Qwen2-based 3B parameter thinking model with 131K context.

Converted with [llama.cpp](https://github.com/ggerganov/llama.cpp) `convert_hf_to_gguf.py`.

## Available Quantizations

| File | Size | BPW | Description |
|------|------|-----|-------------|
| `VibeThinker-3B-F16.gguf` | 5.8 GB | 16.00 | Full FP16 (reference) |
| `VibeThinker-3B-Q8_0.gguf` | 3.1 GB | 8.50 | Near-lossless 8-bit |
| `VibeThinker-3B-Q5_K_M.gguf` | 2.1 GB | 5.75 | High quality 5-bit |
| `VibeThinker-3B-Q4_K_M.gguf` | 1.8 GB | 4.99 | Great size/quality tradeoff |

## Usage

### llama.cpp

```bash
./llama-cli -m VibeThinker-3B-Q4_K_M.gguf -p "Hello!" -n 128
```

### Chat Format

This model uses the Qwen2 chat format with thinking tags:

```
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant
<think>...reasoning...</think>
...response...
<|im_end|>
```

## Model Details

- **Architecture:** Qwen2ForCausalLM
- **Parameters:** ~3B
- **Layers:** 36
- **Hidden size:** 2048
- **Heads:** 16 (2 KV heads)
- **Context:** 131,072 tokens
- **Vocab:** 151,936