Instructions to use keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

Use Docker

docker model run hf.co/keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

LM Studio
Jan

vLLM

How to use keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-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": "keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-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/keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

Ollama
How to use keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF with Ollama:
```
ollama run hf.co/keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16
```

Unsloth Studio

How to use keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF to start chatting

How to use keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

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": "keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

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 "keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16" \
  --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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF with Docker Model Runner:
```
docker model run hf.co/keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16
```

Lemonade

How to use keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

Run and chat with the model

lemonade run user.Gemma-4-26B-A4B-it-MixQ-13G-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

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 keyuan01/Gemma-4-26B-A4B-it-MixQ-13G-GGUF:F16

Run Hermes

hermes

Atomic Chat

Gemma-4-26B-A4B-it-MixQ-13G-GGUF / README.md

keyuan01

Upload README.md with huggingface_hub

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	---
	base_model: google/gemma-4-26B-A4B-it
	base_model_relation: quantized
	library_name: gguf
	pipeline_tag: image-text-to-text
	tags:
	- gguf
	- llama.cpp
	- gemma
	- gemma-4
	- multimodal
	- quantized
	- image-text-to-text
	- imatrix
	- mix-quant
	language:
	- en
	license: apache-2.0
	license_link: https://ai.google.dev/gemma/docs/gemma_4_license
	---

	# Gemma 4 26B A4B it Mix-Quant 13GB GGUF

	## File
	- Model: `google_gemma-4-26b-a4b-it-mix-13GB.gguf`
	- Multimodal projector: `mmproj-gemma-4-26b-a4b-it-f16.gguf`

	## Size
	- Exact file size: `13,638,779,232` bytes
	- Approximate readable size:
	- `12.70 GiB`
	- `13.64 GB`
	- Multimodal projector exact size: `1,193,058,432` bytes

	## What This Is
	This is the smaller mixed-quant target built from the F16 text GGUF for `google/gemma-4-26B-A4B-it`.

	It is not a pure uniform quant.
	It is a mixed recipe built for `llama.cpp`, with multimodal support preserved through the separate projector file.

	## Quantization Type
	This release is a `GGUF` quantized model for `llama.cpp`.

	Quantization family:
	- `GGUF`
	- `llama.cpp`
	- mixed tensor quantization (`Mix-Quant`)
	- `imatrix`-guided quantization

	This is not a single uniform `Q4` or `Q3` file.
	It is a mixed-precision build where different tensor groups keep different quant types according to sensitivity and size budget.

	## Mix Formula
	This published 13GB file follows a mixed recipe in this style:
	- `token_embd -> q5_k`
	- `output -> q5_k`
	- `router -> q8_0`
	- `attn_q -> q6_k`
	- `attn_k -> q6_k`
	- `attn_v -> q6_k`
	- `attn_output -> q6_k`
	- `ffn_gate_up_exps -> mixed q4_k / q3_k`
	- `ffn_down_exps -> q4_0`

	Notes:
	- this file is MoE, so expert tensors are not laid out like the dense 31B recipe
	- `ffn_gate_up_exps` is the main mixed expert block
	- the 13GB release is therefore closer to a `Q6_K + Q4_K/Q3_K` expert mix than to a `Q3-centered` dense recipe

	## Importance Matrix (`imatrix`)
	This release follows the same `imatrix`-guided quantization idea used in the 31B line.

	Core formula:

	`I_j = Σ_t x_{t,j}^2`

	Where:
	- `x_{t,j}` is the activation value of channel `j` for token/sample step `t`
	- `I_j` is the accumulated importance score of that channel across calibration text

	Practical meaning:
	- channels that activate more often and with larger magnitude get larger importance values
	- more important directions are better preserved during quantization
	- less important directions can be compressed more aggressively

	`imatrix` does not use benchmark scores directly.
	It estimates sensitivity from activations collected on calibration data.

	## Multimodal Support
	Yes. Multimodal remains supported when paired with:
	- `mmproj-gemma-4-26b-a4b-it-f16.gguf`

	Notes:
	- the projector was preserved separately
	- the 13GB main file is text-side quantized in the same release style as the 31B line
	- image-text usage depends on loading `mmproj` together with the main model

	## Road
	The working road was:
	1. Keep the original HF Gemma 4 26B A4B it model as the source of truth.
	2. Export the text model to F16 GGUF.
	3. Preserve the multimodal projector as a separate file.
	4. Build a mixed 13GB quantized release for local `llama.cpp` inference.
	5. Publish the main GGUF together with the projector file.

	## Self Tests
	Observed checks for the published 13GB release:
	- the GGUF file is valid and readable
	- the multimodal projector file is present in the repository
	- the release remains a multimodal package when used with `mmproj`

	Note:
	- local experimental variants and local runtime behavior may differ from this published file
	- the README here describes the actual uploaded Hugging Face GGUF file, not a guessed local preset name

	## Environment Build
	Minimal setup:
	1. Install CUDA and a recent NVIDIA driver.
	2. Build `llama.cpp` with CUDA support.
	3. Keep the 13GB GGUF and `mmproj` together if you need vision.
	4. Load both files together for multimodal inference.

	Example server:
	```sh
	llama-server \
	-m 'google_gemma-4-26b-a4b-it-mix-13GB.gguf' \
	--mmproj 'mmproj-gemma-4-26b-a4b-it-f16.gguf' \
	-ngl 999 -fa on --ctx-size 4096 -np 1 --port 18081
	```

	## Datasets And License Notes
	This repository is a GGUF release of the Google base model.

	License:
	- `Apache-2.0`
	- official license link: `https://ai.google.dev/gemma/docs/gemma_4_license`

	## Practical Summary
	Use this version if you want:
	- a published mixed 13GB GGUF for `Gemma 4 26B A4B it`
	- multimodal support preserved through the separate `mmproj`
	- a MoE mixed quant release documented in recipe style instead of a generic quant summary