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

GGUF quantizations of WeiboAI/VibeThinker-3B, a Qwen2-based 3B parameter thinking model with 131K context.

Converted with 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

./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

Downloads last month: 389

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for heath0xFF/VibeThinker-3B-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

WeiboAI/VibeThinker-3B

Quantized

(48)

this model