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

Libraries

How to use RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf",
	filename="gemma-4-26B-A4B-it-assistant-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-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 RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-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 RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16

Use Docker

docker model run hf.co/RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16

LM Studio
Jan
Ollama
How to use RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf with Ollama:
```
ollama run hf.co/RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16
```

Unsloth Studio

How to use RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-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 RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-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 RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf to start chatting

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-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": "RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RachidAR/gemma-4-26B-A4B-it-qat-assistant-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-server -hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-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 RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf with Docker Model Runner:
```
docker model run hf.co/RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf:F16

Run and chat with the model

lemonade run user.gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf-F16

List all available models

lemonade list

Gemma 4 26B A4B Assistant GGUF

GGUF quantizations converted from google/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistant.

Tested with llama.cpp b9549 (Gemma 4 MTP support).

Update

Added experimental IQ quantizations with Q4 embeddings (token_embd.weight = Q4_0).

Recommendations

Q4_0-q4emb — recommended for most users
Q8_0 — for users with spare VRAM

Files

gemma-4-26B-A4B-it-assistant-f16.gguf
gemma-4-26b-A4B-it-assistant-Q4_0.gguf
gemma-4-26b-A4B-it-assistant-Q4_0-q4emb.gguf (closest to pure Q4 QAT layout)
gemma-4-26b-A4B-it-assistant-IQ4_NL-q4emb.gguf
gemma-4-26b-A4B-it-assistant-IQ3_M-q4emb.gguf (smallest that still works)
gemma-4-26b-A4B-it-assistant-Q8_0.gguf

Q4 Embedding Variant

Q4_0-q4emb is an experimental quantization where token_embd.weight is kept in Q4_0 instead of Q6_K precision quantization typically used by llama.cpp.

This follows a similar approach to recent QAT experiments for Gemma models, where preserving the original Q4-trained embedding format may better match the intended QAT behavior.

Initial testing showed similar draft acceptance rates to the default Q4_0 quant, with a small speed advantage, though more benchmarking is needed.

Example

llama-server \
  -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf \
  -md gemma-4-26b-A4B-it-assistant-Q4_0.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 2

Recommended values:

--spec-draft-n-max 2 for general use
--spec-draft-n-max 3 for coding workloads

Downloads last month: 2,186

GGUF

Model size

0.4B params

Architecture

gemma4-assistant

Hardware compatibility

3-bit

4-bit

8-bit

16-bit

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Model tree for RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf

Base model

google/gemma-4-26B-A4B-it-assistant

Finetuned

google/gemma-4-26B-A4B-it-qat-q4_0-unquantized-assistant

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Collection including RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4_0-gguf

Gemma 4 QAT MTP

Collection

3 items • Updated 1 day ago • 1