Instructions to use M1n1A1/MiniAI-Quata1-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use M1n1A1/MiniAI-Quata1-4b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="M1n1A1/MiniAI-Quata1-4b-GGUF",
	filename="MiniAI-Quata1-4b.BF16.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 M1n1A1/MiniAI-Quata1-4b-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 M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf M1n1A1/MiniAI-Quata1-4b-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 M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf M1n1A1/MiniAI-Quata1-4b-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 M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf M1n1A1/MiniAI-Quata1-4b-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 M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use M1n1A1/MiniAI-Quata1-4b-GGUF with Ollama:
```
ollama run hf.co/M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M
```

Unsloth Studio

How to use M1n1A1/MiniAI-Quata1-4b-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 M1n1A1/MiniAI-Quata1-4b-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 M1n1A1/MiniAI-Quata1-4b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for M1n1A1/MiniAI-Quata1-4b-GGUF to start chatting

How to use M1n1A1/MiniAI-Quata1-4b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf M1n1A1/MiniAI-Quata1-4b-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": "M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use M1n1A1/MiniAI-Quata1-4b-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 M1n1A1/MiniAI-Quata1-4b-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 M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use M1n1A1/MiniAI-Quata1-4b-GGUF with Docker Model Runner:
```
docker model run hf.co/M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M
```

Lemonade

How to use M1n1A1/MiniAI-Quata1-4b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull M1n1A1/MiniAI-Quata1-4b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniAI-Quata1-4b-GGUF-Q4_K_M

List all available models

lemonade list

MiniAI-Quata1-4b-GGUF : GGUF

This is a GGUF of M1n1A1/MiniAI-Quata1-4b.

NOTE: These weights are corrupted due to safetensors being corrupted, PLEASE, PLEASE wait for new safetensors to come out in like ~24 hours.

Thank you <3

Official GGUF Quantization Files

File Name	Size	Type / Recommended Use
📦 MiniAI-Quata1-4b.BF16.gguf	8.05 GB	Original precision, maximum quality (requires high VRAM).
📦 MiniAI-Quata1-4b.F16.gguf	8.05 GB	Full precision alternative for specific hardware loaders.
📦 MiniAI-Quata1-4b.Q8_0.gguf	4.28 GB	Near-lossless quality. Highly recommended if you have 6GB+ VRAM.
📦 MiniAI-Quata1-4b.Q6_K.gguf	3.31 GB	Excellent balance of high quality and resource optimization.
📦 MiniAI-Quata1-4b.Q5_K_M.gguf	2.89 GB	Optimal choice for standard consumer GPUs / fast inference.
📦 MiniAI-Quata1-4b.Q4_K_M.gguf	2.50 GB	Highly lightweight, fast, and very light on RAM/VRAM usage.
📦 MiniAI-Quata1-4b.Q3_K_M.gguf	2.08 GB	Verified original quant — low resource/mobile setups.
📦 MiniAI-Quata1-4b.Q2_K_L.gguf	1.70 GB	Minimum footprint variant.

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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