--- title: "gemma-4-31B-it-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f257bf61-ef21-40bb-9e67-e4d77bf5e0e0" updated: "2026-11-12" --- # gemma-4-31B-it-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 locally from Unsloth's GGUF files, with text and image input and a long context. Suits developers who want a strong local reasoning and coding model. gemma-4-31B-it-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 unsloth ## About ### What it is Unsloth's GGUF packaging of google/gemma-4-31B-it, the 31B instruction-tuned member of Google DeepMind's Gemma 4 family. Gemma 4 takes text and image input and writes text. The card describes configurable thinking modes, native function calling and a system role. ### Who it suits Developers and tinkerers who want a large open model on their own machine for reasoning, coding and image questions, and who are comfortable with GGUF tools. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a local coding and reasoning assistant | Ask it to debug a function | | Agents | Agent builders | Use native function calling in local agents | Let a local agent call tools | | Research | Researchers | Test a family model through a quantized copy | Compare thinking modes on the same prompt | If you only have a small GPU, you can skip it and pick one of the smaller Gemma 4 sizes. ### Where to use it - llama.cpp and other tools that read GGUF - Unsloth Studio, which the card says can run and fine-tune Gemma 4 - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Coding help | Developers | Code, fixes and explanations | | Image questions | Anyone | Text answers about a picture | | Long-context tasks | Analysts | Answers across a context of up to 256K tokens | ### How to set it up - Python with pip, to download the files - A GGUF runner such as llama.cpp or Unsloth Studio - Memory for about 15.3 GB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face hub package. 2. Download the GGUF files. 3. Load them in your runner. The card says to re-download after Google's chat template and llama.cpp fixes. ```sh pip install -U huggingface_hub hf download unsloth/gemma-4-31B-it-GGUF ``` ### How to use it Load the GGUF file in your runner and chat with it. The card points to Unsloth's own guide for running Gemma 4 and notes that multi-token prediction support has been added. Turn thinking on for harder problems. ### Example You load the model and ask it to explain why a function returns the wrong value. It reasons through the code and replies with the cause and a fix. ### For newcomers and experts If you are new, try Unsloth Studio or llama.cpp with one short question. Start without images, then add them. Gemma 4 comes in dense and mixture-of-experts forms in four sizes: E2B, E4B, 26B A4B and 31B. The medium models offer a 256K context window and the small ones 128K. This copy is a quantized build of the 31B model, so quality can differ slightly from the original. Download the GGUF files → Load them in llama.cpp or Unsloth Studio → Send text, or images too → The model replies, with optional thinking ### Good to know - The card says to re-download for the latest chat template and llama.cpp fixes - Gemma 4 supports over 140 languages - License: Apache 2.0, with Google DeepMind as the model's author - Cost to run: none named, you supply the hardware - Data it reaches: your prompts, images and the files downloaded from Hugging Face - Not for: video and audio, which the card lists only for the small E2B and E4B models ## README ````markdown # gemma-4-31B-it-GGUF Runs Google's Gemma 4 31B instruction-tuned model locally from Unsloth's GGUF files, with text and image input and a long context. Suits developers who want a strong local reasoning and coding model. Made by unsloth 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: Unsloth's GGUF packaging of google/gemma-4-31B-it, the 31B instruction-tuned member of Google DeepMind's Gemma 4 family. Gemma 4 takes text and image input and writes text. The card describes configurable thinking modes, native function calling and a system role. Who it suits: Developers and tinkerers who want a large open model on their own machine for reasoning, coding and image questions, and who are comfortable with GGUF tools. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a local coding and reasoning assistant | Ask it to debug a function Agents | Agent builders | Use native function calling in local agents | Let a local agent call tools Research | Researchers | Test a family model through a quantized copy | Compare thinking modes on the same prompt ``` If you only have a small GPU, you can skip it and pick one of the smaller Gemma 4 sizes. Where to use it: - llama.cpp and other tools that read GGUF - Unsloth Studio, which the card says can run and fine-tune Gemma 4 - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Coding help | Developers | Code, fixes and explanations Image questions | Anyone | Text answers about a picture Long-context tasks | Analysts | Answers across a context of up to 256K tokens ``` How to set it up: - Python with pip, to download the files - A GGUF runner such as llama.cpp or Unsloth Studio - Memory for about 15.3 GB at 4-bit by Hugging Face's estimate 1. Install the Hugging Face hub package. 2. Download the GGUF files. 3. Load them in your runner. The card says to re-download after Google's chat template and llama.cpp fixes. ```sh pip install -U huggingface_hub hf download unsloth/gemma-4-31B-it-GGUF ``` How to use it: Load the GGUF file in your runner and chat with it. The card points to Unsloth's own guide for running Gemma 4 and notes that multi-token prediction support has been added. Turn thinking on for harder problems. Example: You load the model and ask it to explain why a function returns the wrong value. It reasons through the code and replies with the cause and a fix. For newcomers and experts: If you are new, try Unsloth Studio or llama.cpp with one short question. Start without images, then add them. Gemma 4 comes in dense and mixture-of-experts forms in four sizes: E2B, E4B, 26B A4B and 31B. The medium models offer a 256K context window and the small ones 128K. This copy is a quantized build of the 31B model, so quality can differ slightly from the original. ```flow Download the GGUF files Load them in llama.cpp or Unsloth Studio Send text, or images too The model replies, with optional thinking ``` Good to know: - The card says to re-download for the latest chat template and llama.cpp fixes - Gemma 4 supports over 140 languages - License: Apache 2.0, with Google DeepMind as the model's author - Cost to run: none named, you supply the hardware - Data it reaches: your prompts, images and the files downloaded from Hugging Face - Not for: video and audio, which the card lists only for the small E2B and E4B models ## On Hugging Face, 13 November 2026 - Task: image text to text - Parameters: 30.7B - Weights: about 15.3 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 428,266 - Likes: 677 - License: apache-2.0 - Based on: google/gemma-4-31B-it - Quantized versions: 2 - Fine-tunes: 1 - Tags: gguf, gemma4, unsloth, gemma, google, image-text-to-text, imatrix, conversational ## Links - Model: https://huggingface.co/unsloth/gemma-4-31B-it-GGUF - Files: https://huggingface.co/unsloth/gemma-4-31B-it-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/gemma-4-31B-it-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 [unsloth/gemma-4-31B-it-GGUF on Hugging Face](https://huggingface.co/unsloth/gemma-4-31B-it-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-GGUF (this page) | 428,266 | 677 | 30.7B | apache-2.0 | 2026-07-17 | | [gemma-4-12b-it-GGUF](https://mindket.com/p/5c808b31-349e-4069-ad4e-f80ffc3a1bee) | 2,534,530 | 963 | 11.9B | apache-2.0 | 2026-07-17 | | [gemma-4-E4B-it-GGUF](https://mindket.com/p/fa9df3ff-5b05-4e15-984f-04ccfc125e3e) | 702,365 | 687 | 7.5B | apache-2.0 | 2026-07-17 | | [gemma-4-E2B-it-GGUF](https://mindket.com/p/be304d82-a02d-4163-94a6-2fab2d7a7210) | 674,565 | 356 | 4.6B | apache-2.0 | 2026-07-17 | | [gemma-4-31B-it-qat-GGUF](https://mindket.com/p/808c5b4b-b27d-43fd-bf03-d1970ccfbce4) | 79,933 | 226 | 30.7B | apache-2.0 | 2026-07-17 | | [gemma-3-27b-it-GGUF](https://mindket.com/p/92fcdbbc-a9e1-4d36-a8b5-90224a919f74) | 50,284 | 211 | 27B | gemma | 2025-08-14 | 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 · c-hendricks · 2026-06-12](https://news.ycombinator.com/item?id=48507679): How to setup a local coding agent on macOS: Not sure you really need huggingface-cli to download anything if you're just using llama.cpp. You can pass \`-hf ...\` and it will download the models for you. Set \`LLAMA\_CACHE\` to change where the downloads go: LLAMA\_CACHE="models" ./llama-server \\ -hf unsloth/gemma-4-31B-it-GGUF:UD-Q4\_K\_XL \\ ... - [Hacker News · lambda · 2026-04-30](https://news.ycombinator.com/item?id=47966764): Granite 4.1: IBM's 8B Model Matching 32B MoE: llama.cpp My setup is a bit of a mess as I experiment with different ways of configuring and hosting local models. So at some point I was experimenting with the router server but stopped doing that, but some of my settings are still in models.ini while some are on the command line. podman run… - [Hacker News · throwaway2027 · 2026-04-12](https://news.ycombinator.com/item?id=47740368): Pro Max 5x quota exhausted in 1.5 hours despite moderate usage: Some claim that some of the recent smaller local models are as good as Sonnet 4.5 of last year and the bigger high-end models can be as almost as good as Claude, Gemini and Codex today, but some say they're benchmaxed and not representative. To try things out you can use llama.cpp with Vulkan or… - [Hacker News · matja · 2026-04-02](https://news.ycombinator.com/item?id=47617135): Google releases Gemma 4 open models: I'm running Gemma 4 with the llama.cpp web UI. https://unsloth.ai/docs/models/gemma-4 \> Gemma 4 GGUFs \> "Use this model" \> llama.cpp \> llama-server -hf unsloth/gemma-4-31B-it-GGUF:Q8\_0 If you already have llama.cpp you might need to update it to support Gemma 4. ## 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: 2026-04-01 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/gemma-4-31B-it-GGUF on Hugging Face](https://huggingface.co/unsloth/gemma-4-31B-it-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f257bf61-ef21-40bb-9e67-e4d77bf5e0e0) - [Its page on Mindket](https://mindket.com/p/f257bf61-ef21-40bb-9e67-e4d77bf5e0e0)