gemma-4-12B-it-qat-q4_0-gguf

gemma-4-12B-it-qat-q4_0-gguf

Runs Google's Gemma 4 12B instruction-tuned model in a 4-bit GGUF file made with quantization-aware training. Reads text and images, and suits laptops and consumer GPUs.

Made by google on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: This is the 12B instruction-tuned model from Google DeepMind's Gemma 4 family, as a Q4_0 GGUF file. It was trained with quantization-aware training, which the card says keeps quality close to bfloat16 while cutting the memory needed to load it. It has about 11.9B parameters and comes to roughly 6 GB at 4-bit.

Who it suits: Developers and enthusiasts who want a capable open model with reasoning and image input on a laptop, a consumer GPU or a small server.

Industry | Who uses it | How they use it | Example
Software | Developers | Run a local assistant with coding and function calling | A local coding helper
Research | ML researchers | Study quantization-aware training results | A comparison with a full-precision copy
Education | Students | Try a reasoning model on a personal computer | A study helper that works offline
IT operations | Small teams | Keep text and images on their own hardware | An internal document question tool

If you want a phone-sized model or the largest sizes, you can skip it, since the family has smaller and larger versions.

Where to use it:

• Programs that read GGUF files, on Windows, macOS or Linux
• Python, for downloading the files from Hugging Face
• Consumer GPUs and laptops, which the card names as targets for the 12B size

Use case | Who | What they get back
Chat and reasoning | Individuals | Answers with an adjustable thinking mode
Image questions | Developers | Text answers about an image you supply
Function calling | Agent builders | Tool calls from the model
Multilingual text | Anyone | Support for more than 140 languages, per the card

How to set it up:

• Python, to run the Hugging Face download tool
• Room for about 6 GB of 4-bit weights
• A program that loads GGUF files

1. Install the Hugging Face hub tools with pip.
2. Download the GGUF files with the hf command.
3. Open the file in your GGUF-capable program.

pip install -U huggingface_hub
hf download google/gemma-4-12B-it-qat-q4_0-gguf

How to use it: Load the GGUF in your program and chat as you would with any instruction-tuned model. The card says Gemma 4 supports a system role, so you can set the rules for a conversation in a system message.

Example: You attach a photo of a street sign and ask "What does this sign say, and in which language". The model reads the image and replies in text with the wording and the language.

For newcomers and experts: Start with a short text question, then add an image. The first small step is to confirm the file loads in your program before you try long prompts.

The card lists four QAT variants in the family: unquantized Q4_0 checkpoints, GGUF, mobile-optimized files and compressed tensors for vLLM. If you pair a QAT model with an assistant model for speculative decoding, the assistant must also be a QAT checkpoint at the same precision.

Download the GGUF file
Load it in a GGUF program
Send text or an image
The model reasons and answers

Good to know:

• License: Apache 2.0
• Cost to run: none named
• Data it reaches: downloads files from Hugging Face, then runs on your own machine
• Not for: phones and small edge devices, where the card points to the smaller E2B and E4B sizes
• Limits: the family goes up to a 256K token context, but your hardware sets the practical limit

On Hugging Face, 13 November 2026

• Task: any to any
• Library: transformers
• Parameters: 11.9B
• Weights: about 6 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 1,177,710
• Likes: 397
• License: apache-2.0
• Based on: google/gemma-4-12B-it-qat-q4_0-unquantized
• Quantized versions: 3
• Fine-tunes: 2
• Tags: transformers, gguf, any-to-any, conversational

Links

• Model: https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf
• Files: https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download google/gemma-4-12B-it-qat-q4_0-gguf

Then follow the usage in its model card. Nothing in this kit runs by itself.