--- title: "gemma-4-31B-it-qat-q4_0-gguf" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/cacee1c8-7031-4672-8787-facbaea992b5" updated: "2026-11-13" --- # gemma-4-31B-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 31B instruction-tuned model as a 4-bit GGUF file that reads text and images and answers in text. It suits developers and enthusiasts with a consumer GPU or workstation. gemma-4-31B-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 2026-11-13. 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 GGUF version of Google's Gemma 4 31B instruction-tuned model, stored in the Q4\_0 format after quantization-aware training. It reads images and text and writes text. Google says this training keeps quality close to bfloat16 and cuts the memory the model needs. The GGUF format is meant to work with many tools, so you can run a 31B model without the full-size weights. ### Who it suits Developers and enthusiasts who want a large multimodal model on their own machine suit it best. The card places the 31B size on consumer GPUs and workstations. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a local model for coding and reasoning tasks | Ask for a review of a code change | | AI products | Teams that build assistants | Use the model behind an agent that calls functions | Let an agent look up data through a tool | | Research | Enthusiasts and testers | Try a large open model without cloud costs | Compare its answers on a set of pictures | Anyone without a computer that can hold a file of about 15.3 GB can skip it and look at a smaller Gemma 4 size. ### Where to use it You download the file on your own computer with Python, then load it in a program that reads GGUF files. - Windows, macOS or Linux with Python installed - Programs and servers that load GGUF files - Machines with a consumer GPU or a workstation | Use case | Who | What they get back | | --- | --- | --- | | Local chat and reasoning | Developers | Text answers from a model on your own machine | | Picture questions | Developers | A text answer about an image | | Coding help | Developers | Code suggestions and explanations | ### How to set it up You need Python, the Hugging Face hub tools, and a program that can load GGUF files. The 4-bit file is about 15.3 GB, which is an estimate. - Python on Windows, macOS or Linux - The huggingface\_hub package - About 15.3 GB of free disk space - A program that loads GGUF files 1. Install Python. 2. Install the hub tools with pip. 3. Download the model files with the hf command. 4. Open the GGUF file in the program you use. ```sh pip install -U huggingface_hub hf download google/gemma-4-31B-it-qat-q4_0-gguf ``` ### How to use it The model card text available here shows no run command. You load the downloaded file in a GGUF-capable program, then send it text or an image together with your question. - Reasoning: the family offers configurable thinking modes - Coding and agents: native function calling is listed - System role: the model accepts a system message for steadier behavior - Inputs: text and images ### Example You send the model a screenshot of an error dialog together with the question "What does this error mean?". It answers in text. ### For newcomers and experts Think of this file as a large AI model squeezed into a size a good home computer can hold. Your first small try is to run the two commands above and open the file in a GGUF-capable program. Under the hood, quantization-aware training prepares the model for 4-bit use, and the Q4\_0 GGUF file is the ready-to-load result. The trade-off is size against the unquantized checkpoint, which this file is based on. Gemma 4 31B instruction-tuned model → Quantization-aware training → Unquantized QAT checkpoint → Q4\_0 GGUF file for your own tools ### Good to know Several limits come from the Gemma 4 family card, and the sizes below come from the Hugging Face record. - Size: 30.7B parameters, about 15.3 GB at 4-bit (an estimate), in GGUF format - Context: up to 256K tokens for the family, with over 140 languages - Audio: listed only for the E2B, E4B and 12B sizes, so not this one - Helper models: one used for multi-token prediction must be QAT trained too, at matching precision - License: apache-2.0 - Cost to run: none named - Data it reaches: the text and images you give it, and the files downloaded from Hugging Face ## README ````markdown # gemma-4-31B-it-qat-q4_0-gguf Runs Google's Gemma 4 31B instruction-tuned model as a 4-bit GGUF file that reads text and images and answers in text. It suits developers and enthusiasts with a consumer GPU or workstation. 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 GGUF version of Google's Gemma 4 31B instruction-tuned model, stored in the Q4_0 format after quantization-aware training. It reads images and text and writes text. Google says this training keeps quality close to bfloat16 and cuts the memory the model needs. The GGUF format is meant to work with many tools, so you can run a 31B model without the full-size weights. Who it suits: Developers and enthusiasts who want a large multimodal model on their own machine suit it best. The card places the 31B size on consumer GPUs and workstations. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a local model for coding and reasoning tasks | Ask for a review of a code change AI products | Teams that build assistants | Use the model behind an agent that calls functions | Let an agent look up data through a tool Research | Enthusiasts and testers | Try a large open model without cloud costs | Compare its answers on a set of pictures ``` Anyone without a computer that can hold a file of about 15.3 GB can skip it and look at a smaller Gemma 4 size. Where to use it: You download the file on your own computer with Python, then load it in a program that reads GGUF files. - Windows, macOS or Linux with Python installed - Programs and servers that load GGUF files - Machines with a consumer GPU or a workstation ```table Use case | Who | What they get back Local chat and reasoning | Developers | Text answers from a model on your own machine Picture questions | Developers | A text answer about an image Coding help | Developers | Code suggestions and explanations ``` How to set it up: You need Python, the Hugging Face hub tools, and a program that can load GGUF files. The 4-bit file is about 15.3 GB, which is an estimate. - Python on Windows, macOS or Linux - The huggingface_hub package - About 15.3 GB of free disk space - A program that loads GGUF files 1. Install Python. 2. Install the hub tools with pip. 3. Download the model files with the hf command. 4. Open the GGUF file in the program you use. ```sh pip install -U huggingface_hub hf download google/gemma-4-31B-it-qat-q4_0-gguf ``` How to use it: The model card text available here shows no run command. You load the downloaded file in a GGUF-capable program, then send it text or an image together with your question. - Reasoning: the family offers configurable thinking modes - Coding and agents: native function calling is listed - System role: the model accepts a system message for steadier behavior - Inputs: text and images Example: You send the model a screenshot of an error dialog together with the question "What does this error mean?". It answers in text. For newcomers and experts: Think of this file as a large AI model squeezed into a size a good home computer can hold. Your first small try is to run the two commands above and open the file in a GGUF-capable program. Under the hood, quantization-aware training prepares the model for 4-bit use, and the Q4_0 GGUF file is the ready-to-load result. The trade-off is size against the unquantized checkpoint, which this file is based on. ```flow Gemma 4 31B instruction-tuned model Quantization-aware training Unquantized QAT checkpoint Q4_0 GGUF file for your own tools ``` Good to know: Several limits come from the Gemma 4 family card, and the sizes below come from the Hugging Face record. - Size: 30.7B parameters, about 15.3 GB at 4-bit (an estimate), in GGUF format - Context: up to 256K tokens for the family, with over 140 languages - Audio: listed only for the E2B, E4B and 12B sizes, so not this one - Helper models: one used for multi-token prediction must be QAT trained too, at matching precision - License: apache-2.0 - Cost to run: none named - Data it reaches: the text and images you give it, and the files downloaded from Hugging Face ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 30.7B - Weights: about 15.3 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 115,066 - Likes: 152 - License: apache-2.0 - Based on: google/gemma-4-31B-it-qat-q4_0-unquantized - Tags: transformers, gguf, image-text-to-text, conversational ## Links - Model: https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf - Files: https://huggingface.co/google/gemma-4-31B-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-4-31B-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, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [google/gemma-4-31B-it-qat-q4_0-gguf on Hugging Face](https://huggingface.co/google/gemma-4-31B-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-4-31B-it-qat-q4\_0-gguf (this page) | 115,066 | 152 | 30.7B | apache-2.0 | 2026-07-17 | | [Qwen3.6-35B-A3B-MTP-GGUF](https://mindket.com/p/90db7fe0-7044-4238-a508-5d2ecb760999) | 2,315,167 | 1,051 | 35.5B | apache-2.0 | 2026-05-20 | | [Qwen3.8-27B GGUF (ShapeLearn Quantized)](https://mindket.com/p/ca45abfc-d3db-4fb5-aad9-dcf7a7a498c4) | 2,083,628 | 216 | 27.3B | apache-2.0 | 2026-09-18 | | [Qwen3.5-9B-GGUF](https://mindket.com/p/0f08c237-de8d-4e85-a65e-69b9b6061be0) | 1,969,901 | 1,074 | 9B | apache-2.0 | 2026-03-02 | | [Qwen3.6-35B-A3B-GGUF](https://mindket.com/p/9ac02ca9-6ba3-4ea0-b3f6-dcaea6e7789f) | 1,649,871 | 1,789 | 34.7B | apache-2.0 | 2026-04-20 | | [Qwen3.5-4B-GGUF](https://mindket.com/p/6b140028-a3d0-4702-98e2-b6bd302d386e) | 1,537,690 | 557 | 4.2B | apache-2.0 | 2026-03-02 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-05-01 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/gemma-4-31B-it-qat-q4_0-gguf on Hugging Face](https://huggingface.co/google/gemma-4-31B-it-qat-q4_0-gguf) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/cacee1c8-7031-4672-8787-facbaea992b5) - [Its page on Mindket](https://mindket.com/p/cacee1c8-7031-4672-8787-facbaea992b5)