Instructions to use unsloth/gemma-4-12b-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use unsloth/gemma-4-12b-it-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-4-12b-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/gemma-4-12b-it-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-4-12b-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/gemma-4-12b-it-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-4-12b-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/gemma-4-12b-it-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-4-12b-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/gemma-4-12b-it-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/gemma-4-12b-it-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-4-12b-it-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-4-12b-it-GGUF:UD-Q4_K_XL

Ollama
How to use unsloth/gemma-4-12b-it-GGUF with Ollama:
```
ollama run hf.co/unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/gemma-4-12b-it-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 unsloth/gemma-4-12b-it-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 unsloth/gemma-4-12b-it-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/gemma-4-12b-it-GGUF to start chatting

How to use unsloth/gemma-4-12b-it-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-4-12b-it-GGUF:UD-Q4_K_XL

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": "unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/gemma-4-12b-it-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/gemma-4-12b-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/gemma-4-12b-it-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.gemma-4-12b-it-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use unsloth/gemma-4-12b-it-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-4-12b-it-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-4-12b-it-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/gemma-4-12b-it-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-4-12b-it-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-4-12b-it-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-4-12b-it-GGUF / MTP /README.md

danielhanchen

Update MTP README for renamed drafters

d997c80 verified about 1 month ago

preview code

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Gemma 4 12B MTP drafter

Multi-Token Prediction (MTP) drafter for unsloth/gemma-4-12b-it-GGUF. It runs as a speculative draft model that shares the target's KV cache and speeds up text generation.

Verified on a single B200 with the gemma-4-12b-it-Q4_K_M.gguf target: 52 tok/s without MTP, 162 tok/s with MTP, 0.70 draft acceptance.

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

For -hf auto-discovery a Q8_0 drafter sits at the repo root as mtp-gemma-4-12b-it.gguf. The same three precisions live in MTP/:

mtp-gemma-4-12b-it-Q8_0.gguf (smallest, recommended; mirrored at the repo root as mtp-gemma-4-12b-it.gguf)
mtp-gemma-4-12b-it-BF16.gguf
mtp-gemma-4-12b-it-F16.gguf

Build llama.cpp

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.

./build/bin/llama-server \
  -hf unsloth/gemma-4-12b-it-GGUF:Q4_K_M \
  --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.

hf download unsloth/gemma-4-12b-it-GGUF gemma-4-12b-it-Q4_K_M.gguf --local-dir .
hf download unsloth/gemma-4-12b-it-GGUF MTP/mtp-gemma-4-12b-it-Q8_0.gguf --local-dir .

./build/bin/llama-server \
  -m gemma-4-12b-it-Q4_K_M.gguf \
  --model-draft MTP/mtp-gemma-4-12b-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 12B. Quantized KV cache works.