--- title: "gemma-4-12B-it-qat-q4_0-gguf" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c59608fc-38e0-4d4d-9afd-9e191858ad3d" updated: "2026-11-12" --- # gemma-4-12B-it-qat-q4\_0-gguf > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. gemma-4-12B-it-qat-q4\_0-gguf is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by google ## 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. ```bash 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 ## README ````markdown # 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. ```table 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 ```table 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. ```bash 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. ```flow 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: ```sh 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [google/gemma-4-12B-it-qat-q4_0-gguf on Hugging Face](https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | gemma-4-12B-it-qat-q4\_0-gguf (this page) | 1,177,710 | 397 | 11.9B | apache-2.0 | 2026-07-17 | | [gemma-4-12B-it-qat-GGUF](https://mindket.com/p/97ef5b61-d969-4212-aa91-1789e4ce409f) | 196,871 | 617 | 11.9B | apache-2.0 | 2026-07-17 | | [gemma-4-E4B-it-GGUF by ggml-org](https://mindket.com/p/7f5a7ac0-ad9d-408e-9626-14d770cf16e0) | 263,980 | 110 | 7.5B | apache-2.0 | 2026-11-12 | | [gemma-4-12B-it GGUF](https://mindket.com/p/27ee2965-03ef-496e-ac4f-fab36ddf168d) | 107,736 | 106 | 11.9B | apache-2.0 | 2026-11-12 | | [gemma-4-E4B-it](https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c) | 4,298,348 | 1,850 | 8B | apache-2.0 | 2026-11-03 | | [gemma-4-E2B-it](https://mindket.com/p/52ee89d2-d2e7-4750-b100-90e089c3ec76) | 3,017,059 | 1,129 | 5.1B | apache-2.0 | 2026-11-03 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · herf · 2026-09-30](https://news.ycombinator.com/item?id=49912533): Launch HN: Magnitude (YC S25) – Self-optimizing inference engine for agents: I have two NVIDIA GPUs (16GB+16GB) here, and it detects them each twice (says I have 4 GPUs). But then, it says most models are too big (anything \>8GB?) and seems to run only on one GPU (5070ti). Unfortunately even with my 5070ti, llama.cpp seems to be about 20-30% faster at decode, running as:… - [Bluesky · SkynetAndChill.com · 2026-06-09](https://bsky.app/profile/druce.ai/post/3mnuaaetfp422): Google releases Gemma 4 12B in QAT GGUF format, enabling near-full-precision quality on consumer GPUs with llama.cpp and Ollama support - [Hacker News · jack\_pp · 2026-06-06](https://news.ycombinator.com/item?id=48419995): Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency: Ran hf.co/google/gemma-4-12B-it-qat-q4\_0-gguf:Q4\_0 with ollama on a AMD Ryzen 9 8940HX, NVIDIA GeForce RTX 5060 (8 GB), 14 GB RAM laptop and it is suprisingly fast - [Hacker News · minimaxir · 2026-06-05](https://news.ycombinator.com/item?id=48415603): Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency: Isn’t this it? https://huggingface.co/google/gemma-4-12B-it-qat-q4\_0-gguf ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-07-17 - First published: 2026-06-05 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/gemma-4-12B-it-qat-q4_0-gguf on Hugging Face](https://huggingface.co/google/gemma-4-12B-it-qat-q4_0-gguf) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c59608fc-38e0-4d4d-9afd-9e191858ad3d) - [Its page on Mindket](https://mindket.com/p/c59608fc-38e0-4d4d-9afd-9e191858ad3d)