Instructions to use WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

LM Studio
Jan
Ollama
How to use WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF with Ollama:
```
ollama run hf.co/WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4
```

Unsloth Studio

How to use WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF to start chatting

How to use WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

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": "WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

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 "WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4" \
  --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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4
```

Lemonade

How to use WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Gemma-4-26B-A4B-it-qat-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

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 WhiskyAKM/Gemma-4-26B-A4B-it-qat-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

Gemma 4 26B A4B IT QAT — NVFP4 GGUF

NVFP4 GGUF conversions derived from the Quantization-Aware Training (QAT) checkpoint of Gemma 4 26B A4B IT (google/gemma-4-26B-A4B-it-qat-q4_0-unquantized).

This repository provides multiple GGUF quantization levels, making the model usable with llama.cpp and other GGUF-compatible inference engines.

Model Overview

Gemma 4 26B A4B IT is a multimodal model built by Google DeepMind that handles text, image, and audio inputs and generates text output. It is designed for efficient on-device and server deployment.

Model Architecture

Property	Value
Architecture	Gemma4ForConditionalGeneration
Parameters	26B
Supported Modalities	Text, Image, Audio
Context Length	256K tokens (262144)
Vocabulary Size	262K (262144)

GGUF Files

File	Description
`gemma-4-26b-a4b-qat-nvfp4.gguf`	NVFP4 Quantization
`mmproj.gguf`	Multimodal projector (vision + audio)

A chat_template.jinja file is also provided for use with chat-based inference.

Usage

llama.cpp (CLI)

# Run text-only inference
./llama-cli \
  -m gemma-4-26b-a4b-qat-nvfp4.gguf \
  -p "Explain quantum computing in simple terms." \
  --temp 1.0 --top-k 64 --top-p 0.95

llama-server (OpenAI-compatible API)

# Text-only
./llama-server \
  -m gemma-4-26b-a4b-qat-nvfp4.gguf \
  --host 0.0.0.0 --port 8080

# Multimodal (image + audio)
./llama-server \
  -m gemma-4-26b-a4b-qat-nvfp4.gguf \
  --mmproj mmproj.gguf \
  --host 0.0.0.0 --port 8080

Multimodal (Image / Audio)

For image and audio inputs, use llama-server or llama-cli with the --mmproj flag pointing to mmproj.gguf. Refer to your inference engine's documentation for passing image/audio data alongside text prompts.

Modality order tip: For best results, place image content before text and audio content after text in your prompt.

Generation Parameters

Recommended parameters:

Parameter	Value
Temperature	1.0
Top-K	64
Top-P	0.95
BOS Token ID	2
EOS Token ID	1
Pad Token ID	0
Mask Token ID	4

Thinking Mode

Gemma 4 supports configurable thinking (reasoning) mode:

Enable: Include the <|think|> token at the start of the system prompt.
Output format: When thinking is enabled, the model outputs internal reasoning followed by the final answer:
```
<|channel>thought
[Internal reasoning]
<channel|>
[Final answer]
```
Disable: Omit the <|think|> token.

Key Features

Multimodal: Text, image, and audio understanding
Long Context: 256K token context window
Function Calling: Native support for structured tool use (agentic workflows)
Multilingual: Support for 140+ languages
Native System Prompt: Supports the system role for structured conversations

Acknowledgements

Original model: google/gemma-4-26B-A4B-it
QAT checkpoint: google/gemma-4-26B-A4B-it-qat-q4_0-unquantized
Technical report: Gemma 4 Technical Report (arXiv:2607.02770)

Citation

@misc{gemmateam2026gemma4,
      title={Gemma 4 Technical Report},
      author={Gemma Team},
      year={2026},
      eprint={2607.02770},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.02770},
}