Instructions to use unsloth/gemma-4-31B-it-qat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/gemma-4-31B-it-qat-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="unsloth/gemma-4-31B-it-qat-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("unsloth/gemma-4-31B-it-qat-GGUF")
model = AutoModelForMultimodalLM.from_pretrained("unsloth/gemma-4-31B-it-qat-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unsloth/gemma-4-31B-it-qat-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 unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

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 unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

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 unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/gemma-4-31B-it-qat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/gemma-4-31B-it-qat-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": "unsloth/gemma-4-31B-it-qat-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/unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

SGLang

How to use unsloth/gemma-4-31B-it-qat-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "unsloth/gemma-4-31B-it-qat-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/gemma-4-31B-it-qat-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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "unsloth/gemma-4-31B-it-qat-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/gemma-4-31B-it-qat-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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use unsloth/gemma-4-31B-it-qat-GGUF with Ollama:
```
ollama run hf.co/unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL
```
Unsloth Desktop

How to use unsloth/gemma-4-31B-it-qat-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/gemma-4-31B-it-qat-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/gemma-4-31B-it-qat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.gemma-4-31B-it-qat-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use unsloth/gemma-4-31B-it-qat-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 unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

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 unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/gemma-4-31B-it-qat-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL

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 "unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL" \
  --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"

gemma-4-31B-it-qat-GGUF / MTP /README.md

danielhanchen

Rename MTP GGUFs

1f1e542 verified 3 months ago

preview code

Raw History Blame Contribute Delete

2.51 kB

	# Gemma 4 31B QAT MTP drafter

	Multi-Token Prediction (MTP) drafter for `unsloth/gemma-4-31B-it-qat-GGUF`. It runs as a speculative draft model that shares the target's KV cache and speeds up text generation. The drafter is the Gemma 4 31B QAT assistant and pairs with the QAT model unchanged.

	Verified on a single B200 against the `gemma-4-31B-it-qat-UD-Q4_K_XL.gguf` target with `-hf` auto-discovery: draft acceptance 0.79.

	MTP was merged into llama.cpp on 2026-06-07 (PR ggml-org/llama.cpp#23398). You need a llama.cpp build from after that date. Older builds cannot load these (arch `gemma4-assistant`).

	## Files

	The recommended drafter is a smart Q4_0: the native 4-bit QAT drafter (about 97% of its weights are byte-exact on the int4 grid), near-lossless versus higher precision while roughly half the size. It sits at the repo root as `mtp-gemma-4-31B-it.gguf` so `-hf` finds it automatically, and the same file plus higher-precision drafters are in `MTP/`:

	- `mtp-gemma-4-31B-it.gguf` (repo root, smart Q4_0, recommended; used by `-hf`)
	- `MTP/mtp-gemma-4-31B-it-Q4_0.gguf` (same smart Q4_0)
	- `MTP/mtp-gemma-4-31B-it-Q8_0.gguf`
	- `MTP/mtp-gemma-4-31B-it-BF16.gguf`
	- `MTP/mtp-gemma-4-31B-it-F16.gguf`

	## Build llama.cpp

	```bash
	git clone https://github.com/ggml-org/llama.cpp
	cd llama.cpp

	# CUDA build. Set the arch for your GPU: 89 (RTX 4090), 90 (H100), 100 (B200).
	cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=90
	cmake --build build --config Release -j --target llama-server
	```

	## Run, the easy way

	A recent llama.cpp finds the drafter automatically from the root `mtp-` file, so `-hf` is all you need. No `--model-draft`.

	```bash
	./build/bin/llama-server \
	-hf unsloth/gemma-4-31B-it-qat-GGUF:UD-Q4_K_XL \
	--spec-type draft-mtp --spec-draft-n-max 4 \
	-ngl 999 -fa on
	```

	If your build is too old to auto-discover the sibling, use the explicit form below.

	## Run with an explicit drafter

	Use this to choose a precision or point at a local file.

	```bash
	hf download unsloth/gemma-4-31B-it-qat-GGUF gemma-4-31B-it-qat-UD-Q4_K_XL.gguf --local-dir .
	hf download unsloth/gemma-4-31B-it-qat-GGUF MTP/mtp-gemma-4-31B-it-Q8_0.gguf --local-dir .

	./build/bin/llama-server \
	-m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf \
	--model-draft MTP/mtp-gemma-4-31B-it-Q8_0.gguf \
	--spec-type draft-mtp --spec-draft-n-max 4 \
	-ngl 999 -fa on
	```

	Multi GPU: add `--spec-draft-device CUDA0 -sm layer`. The drafter pairs with any quant of the 31B QAT model. Quantized KV cache works.