Instructions to use Quatfit/Quatfit-Mini-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Quatfit/Quatfit-Mini-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Quatfit/Quatfit-Mini-GGUF",
	filename="Q2_K/Quatfit-Mini-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Quatfit/Quatfit-Mini-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 Quatfit/Quatfit-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Quatfit/Quatfit-Mini-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 Quatfit/Quatfit-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Quatfit/Quatfit-Mini-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 Quatfit/Quatfit-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Quatfit/Quatfit-Mini-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 Quatfit/Quatfit-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Quatfit/Quatfit-Mini-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Quatfit/Quatfit-Mini-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Quatfit/Quatfit-Mini-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Quatfit/Quatfit-Mini-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": "Quatfit/Quatfit-Mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Quatfit/Quatfit-Mini-GGUF:Q4_K_M

Ollama
How to use Quatfit/Quatfit-Mini-GGUF with Ollama:
```
ollama run hf.co/Quatfit/Quatfit-Mini-GGUF:Q4_K_M
```

Unsloth Studio

How to use Quatfit/Quatfit-Mini-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 Quatfit/Quatfit-Mini-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 Quatfit/Quatfit-Mini-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use Quatfit/Quatfit-Mini-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Quatfit/Quatfit-Mini-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": "Quatfit/Quatfit-Mini-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Quatfit/Quatfit-Mini-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 Quatfit/Quatfit-Mini-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 Quatfit/Quatfit-Mini-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Quatfit/Quatfit-Mini-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Quatfit/Quatfit-Mini-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Quatfit/Quatfit-Mini-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Quatfit/Quatfit-Mini-GGUF with Docker Model Runner:
```
docker model run hf.co/Quatfit/Quatfit-Mini-GGUF:Q4_K_M
```

Lemonade

How to use Quatfit/Quatfit-Mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Quatfit/Quatfit-Mini-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Quatfit-Mini-GGUF-Q4_K_M

List all available models

lemonade list

Quatfit Mini — GGUF

Every llama.cpp Quantization of Quatfit Mini 8B · 131K Context · Consumer-Hardware Ready

Overview

This repository provides GGUF-quantized builds of Quatfit Mini, an 8B-parameter multimodal model built on the Gemma 4 architecture. Every legacy and K-quant format offered by llama.cpp — from F16 down to the smallest 2-bit Q2_K — is provided here, converted directly from the published FP32 base weights. Each text quant ships together with its matching mmproj file, so vision and audio input work out of the box with every download.

Use this repository if you want to run Quatfit Mini locally with llama.cpp, Ollama, LM Studio, Jan, Open WebUI, koboldcpp, or any other GGUF-compatible runtime.

For fine-tuning, research, or maximum numerical fidelity, use the base FP32 repository instead: Quatfit/Quatfit-Mini.

Available Quantizations

All sizes below are approximate (computed from the 8B parameter count and each format's bits-per-weight; actual file size varies slightly by build). "Quality" is a rough relative ranking, not a benchmark score — see Choosing a Quant below.

K-Quants and Legacy Quants

File	Bits/Weight	Approx. Size	Quality	Notes
`Quatfit-Mini-F32.gguf`	32	~32.1 GB	Reference	Unquantized, identical to source weights
`Quatfit-Mini-F16.gguf`	16	~16.1 GB	Reference	Half precision, negligible loss vs FP32
`Quatfit-Mini-Q8_0.gguf`	8.5	~8.5 GB	Excellent	Near-lossless, largest practical quant
`Quatfit-Mini-Q6_K.gguf`	6.6	~6.6 GB	Excellent	Very close to F16 quality
`Quatfit-Mini-Q5_K_M.gguf`	5.7	~5.7 GB	Very Good	Recommended high-quality default
`Quatfit-Mini-Q5_K_S.gguf`	5.5	~5.6 GB	Very Good	Slightly smaller than Q5_K_M
`Quatfit-Mini-Q5_1.gguf`	6.0	~6.1 GB	Good	Legacy format
`Quatfit-Mini-Q5_0.gguf`	5.5	~5.6 GB	Good	Legacy format
`Quatfit-Mini-Q4_K_M.gguf`	4.8	~4.9 GB	Good	Most popular general-purpose quant
`Quatfit-Mini-Q4_K_S.gguf`	4.6	~4.7 GB	Good	Smaller, slightly lower quality than Q4_K_M
`Quatfit-Mini-Q4_1.gguf`	5.0	~5.1 GB	Good	Legacy format
`Quatfit-Mini-Q4_0.gguf`	4.5	~4.7 GB	Good	Legacy format, wide compatibility
`Quatfit-Mini-Q3_K_L.gguf`	4.3	~4.3 GB	Moderate	Largest 3-bit K-quant
`Quatfit-Mini-Q3_K_M.gguf`	3.9	~4.0 GB	Moderate	Balanced 3-bit option
`Quatfit-Mini-Q3_K_S.gguf`	3.5	~3.7 GB	Lower	Smallest 3-bit K-quant
`Quatfit-Mini-Q2_K.gguf`	2.6	~3.2 GB	Low	Smallest legacy/K-quant, noticeable degradation

Choosing a Quant

You have ≥ 24 GB VRAM (RTX 3090/4090, A6000): Q6_K or Q8_0 for near-reference quality.
You have 8–12 GB VRAM (RTX 3060/3070/4060 Ti): Q4_K_M is the standard recommendation — the best balance of size, speed, and quality.
You have 6–8 GB VRAM: Q4_K_S or Q4_0.
You have < 6 GB VRAM or are running on CPU only: Q3_K_M or Q2_K, with an expected drop in reasoning/coding accuracy.
You need maximum fidelity for evaluation or further conversion: F16 or F32.

As a rule of thumb, prefer the K-quants (Q_K_M variants) for general use — they offer the best quality-per-byte among the legacy and K-quant families. The plain legacy formats (Q4_0, Q4_1, Q5_0, Q5_1) remain useful mainly for compatibility with older runtimes.

Usage

llama.cpp (CLI)

./llama-cli \
  -hf Quatfit/Quatfit-Mini-GGUF:Q4_K_M \
  -p "Explain the difference between GQA and MHA." \
  -n 512

llama.cpp (server)

./llama-server \
  -hf Quatfit/Quatfit-Mini-GGUF:Q4_K_M \
  --host 0.0.0.0 \
  --port 8080 \
  -c 131072

Manual download

huggingface-cli download Quatfit/Quatfit-Mini-GGUF \
  Quatfit-Mini-Q4_K_M.gguf \
  --local-dir .

./llama-cli -m Quatfit-Mini-Q4_K_M.gguf -p "Hello!" -n 256

Ollama

ollama run hf.co/Quatfit/Quatfit-Mini-GGUF:Q4_K_M

LM Studio / Jan / Open WebUI

Search for Quatfit/Quatfit-Mini-GGUF directly in-app and select your preferred quant from the list above.

Prompt Format

Quatfit Mini uses the Gemma 4-style turn format with support for tool calling and an optional thinking mode:

<|turn>user
Your message here<turn|>
<|turn>model

To enable extended reasoning traces, prefix the conversation with a system turn containing <|think|>. The model will emit its reasoning inside <|channel>thought ... <channel|> before the final response.

Most GGUF-compatible chat UIs (Ollama, LM Studio, Open WebUI, llama-server's built-in UI) apply this template automatically via the embedded chat template in each .gguf file.

Multimodality Note

These GGUF conversions currently expose the text capabilities of Quatfit Mini. Vision and audio inputs require the multimodal projector files (mmproj.gguf), which are provided alongside the text-only quants for runtimes that support llama.cpp multimodal (llama-mtmd-cli, compatible server builds). Consult your runtime's documentation for multimodal GGUF support before relying on image/audio input outside of the base FP32 Transformers checkpoint.

Relationship to the Base Model

	Quatfit Mini (base)	Quatfit Mini GGUF (this repo)
Format	`safetensors`, FP32	`.gguf`, quantized (F32 down to Q2_K)
Best for	Fine-tuning, research, max fidelity	Local inference, consumer hardware
Runtime	🤗 Transformers	llama.cpp and compatible runtimes
Size	~32 GB	~2 GB – ~32 GB depending on quant

Full architecture details, training recipe, and benchmark methodology are documented in the Quatfit Mini Technical Report.

Responsible AI

Quatfit Mini may generate inaccurate, biased, or inappropriate outputs, and quantization can mildly amplify errors at lower bit-widths (particularly Q2_K and Q3_K_S). For production deployments:

Verify critical information
Apply RAG for factual grounding
Use application-level safety filters
Prefer Q4_K_M or higher for anything beyond casual/experimental use
Keep human oversight for high-risk domains

Citation

@article{quatfitmini2026,
    title={Quatfit Mini: A Gemma 4-Based Multimodal Model Optimized for Efficient Inference},
    author={Quatfit AI Research},
    year={2026}
}

License

Apache License 2.0. See LICENSE for details.

🤗 GGUF Repository • 🧠 Base Model (FP32) • 📄 Technical Report

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GGUF

Model size

7B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Quatfit/Quatfit-Mini-GGUF

Base model

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(1)

this model