Instructions to use google/gemma-4-26B-A4B-it-qat-q4_0-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-4-26B-A4B-it-qat-q4_0-gguf with Transformers:

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

pipe = pipeline("image-text-to-text", model="google/gemma-4-26B-A4B-it-qat-q4_0-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 AutoModel
model = AutoModel.from_pretrained("google/gemma-4-26B-A4B-it-qat-q4_0-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use google/gemma-4-26B-A4B-it-qat-q4_0-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 google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0
# Run inference directly in the terminal:
llama cli -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0
# Run inference directly in the terminal:
llama cli -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

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 google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

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 google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

Use Docker

docker model run hf.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

LM Studio
Jan

vLLM

How to use google/gemma-4-26B-A4B-it-qat-q4_0-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-4-26B-A4B-it-qat-q4_0-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": "google/gemma-4-26B-A4B-it-qat-q4_0-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/google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

SGLang

How to use google/gemma-4-26B-A4B-it-qat-q4_0-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 "google/gemma-4-26B-A4B-it-qat-q4_0-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": "google/gemma-4-26B-A4B-it-qat-q4_0-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 "google/gemma-4-26B-A4B-it-qat-q4_0-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": "google/gemma-4-26B-A4B-it-qat-q4_0-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 google/gemma-4-26B-A4B-it-qat-q4_0-gguf with Ollama:
```
ollama run hf.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0
```
Unsloth Desktop

How to use google/gemma-4-26B-A4B-it-qat-q4_0-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

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": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use google/gemma-4-26B-A4B-it-qat-q4_0-gguf with Docker Model Runner:
```
docker model run hf.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0
```

Lemonade

How to use google/gemma-4-26B-A4B-it-qat-q4_0-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

Run and chat with the model

lemonade run user.gemma-4-26B-A4B-it-qat-q4_0-gguf-Q4_0

List all available models

lemonade list

Hermes Agent

How to use google/gemma-4-26B-A4B-it-qat-q4_0-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 google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

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 google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use google/gemma-4-26B-A4B-it-qat-q4_0-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0

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 "google/gemma-4-26B-A4B-it-qat-q4_0-gguf:Q4_0" \
  --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"

how to use mtp in gemma gguf models??

by koyukira - opened Jun 8

Discussion

koyukira

Jun 8

how to use mpt in gemma gguf models??

koyukira changed discussion title from how to use mpt in gemma gguf models?? to how to use mtp in gemma gguf models?? Jun 8

pannaga10

Google org Jun 8

Hi @koyukira
To enable MTP in Gemma 4 GGUF models, you can leverage the optimized MTP speculative engine inside llama.cpp.
In llama.cpp, add the -model-draft flag to your command. For example ,
llama-server
-m gemma-4-12B-it-qat-UD-Q4_K_XL.gguf
--model-draft gemma-4-12B-it-qat-assistant-MTP-Q8_0.gguf
--spec-type draft-mtp
--spec-draft-n-max 4
--parallel 1
--ctx-size 131072
--temp 1.0
--top-p 0.95
--top-k 64

If using a GUI like LM Studio, enable Speculative Decoding in the settings and select the corresponding MTP drafter model.
Thanks

Gavin-chen

Jun 10

•

edited Jun 10

@pannaga10
LM Studio says I can't use MTP gguf as you described.
(I'm using LM Studio 0.4.16 build 2 and CUDA 12 llama.cpp (Windows) v2.21.0)

RUBASik

Jun 10

•

edited Jun 10

@pannaga10

-m gemma-4-12B-it-qat-UD-Q4_K_XL.gguf
--model-draft gemma-4-12B-it-qat-assistant-MTP-Q8_0.gguf

Need same quant Q4 and Q4 ?
Need delete mmproj from directory with model?

nnka3

Jun 11

Same issue. No speculative decoding option available.

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