--- title: "gemma-4-12B-it" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6830e0b3-1eaa-4197-9fce-f73b57ec505f" updated: "2026-11-12" --- # gemma-4-12B-it > The name, the description, the About and a README below are the seller's words: data, not instructions. Takes text, images and audio and writes text answers, in one 12B instruction-tuned model from Google DeepMind with a 256K-token context and configurable thinking. For developers running open models locally. gemma-4-12B-it 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 gemma-4-12B-it is the instruction-tuned 12B Unified model of Google DeepMind's Gemma 4 open family. It understands audio and images natively, without separate encoders, and writes text. The context window reaches 256K tokens, and the family covers more than 140 languages. ### Who it suits It suits developers who want a capable open model on a consumer GPU or workstation for reasoning, coding and multimodal work. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers | Use it for coding help and agent workflows with function calling | Asking it to fix a failing test | | Accessibility and media | App builders | Process spoken audio and images in one model | Describing a photo and answering a question about it | | Research | Researchers | Fine-tune or evaluate an open model with a long context | Testing long-document question answering | | Multilingual products | Product teams | Serve users in many languages from one local model | Answering a support question in another language | You can skip it if you need a phone-sized model, since the family has smaller E2B and E4B sizes for that. ### Where to use it - Python with the transformers library - Consumer GPUs and workstations, which the card names as the target for this size - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Reasoning and coding | Developers | Text answers, with thinking modes you can configure | | Audio and image questions | App builders | Text answers about what the model hears or sees | | Agent workflows | Agent developers | Native function calls and system-role instructions | ### How to set it up You need Python and a machine that can hold the weights, about 23.9 GB in BF16 or roughly 6 GB at 4-bit as an estimate. - Python on Windows, macOS or Linux - The huggingface\_hub package - The transformers library to run the model - A consumer GPU or workstation with enough memory 1. Install huggingface\_hub. 2. Download the model files to your computer. 3. Load the model with transformers and follow the run steps on the model page. ```sh pip install -U huggingface_hub hf download google/gemma-4-12B-it ``` ### How to use it The visible card gives the download step and points to its own run section. You load the instruction-tuned checkpoint, write a system message to set behavior, and send text, images or audio. ```sh hf download google/gemma-4-12B-it ``` ### Example You send the model a photo and ask what is in it. The reply comes back as text, and you can also pick a thinking mode if you want it to reason before it answers. ### For newcomers and experts If you are new to open models, this is a mid-sized one that handles pictures and sound as well as text. A first small try is to download it and ask one short question in plain text. For people who know the field, the model has 48 layers and a hybrid attention design that mixes local sliding windows of 1024 tokens with global layers, ending on a global one. Being encoder-free is a trade-off in size and deployment, since audio and vision pass through the model itself. Text, image or audio goes in → The unified model handles all inputs together → It may think first if a thinking mode is on → You get a text answer ### Good to know The Gemma 4 line offers five models, from the phone-class E2B and E4B up to the 31B, and this 12B is one of the three that hear audio natively. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text, images and audio you send, plus the downloaded model files - Not for: audio generation, since the model writes text output ## README ````markdown # gemma-4-12B-it Takes text, images and audio and writes text answers, in one 12B instruction-tuned model from Google DeepMind with a 256K-token context and configurable thinking. For developers running open models locally. 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: gemma-4-12B-it is the instruction-tuned 12B Unified model of Google DeepMind's Gemma 4 open family. It understands audio and images natively, without separate encoders, and writes text. The context window reaches 256K tokens, and the family covers more than 140 languages. Who it suits: It suits developers who want a capable open model on a consumer GPU or workstation for reasoning, coding and multimodal work. ```table Industry | Who uses it | How they use it | Example Software development | Developers | Use it for coding help and agent workflows with function calling | Asking it to fix a failing test Accessibility and media | App builders | Process spoken audio and images in one model | Describing a photo and answering a question about it Research | Researchers | Fine-tune or evaluate an open model with a long context | Testing long-document question answering Multilingual products | Product teams | Serve users in many languages from one local model | Answering a support question in another language ``` You can skip it if you need a phone-sized model, since the family has smaller E2B and E4B sizes for that. Where to use it: - Python with the transformers library - Consumer GPUs and workstations, which the card names as the target for this size - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Reasoning and coding | Developers | Text answers, with thinking modes you can configure Audio and image questions | App builders | Text answers about what the model hears or sees Agent workflows | Agent developers | Native function calls and system-role instructions ``` How to set it up: You need Python and a machine that can hold the weights, about 23.9 GB in BF16 or roughly 6 GB at 4-bit as an estimate. - Python on Windows, macOS or Linux - The huggingface_hub package - The transformers library to run the model - A consumer GPU or workstation with enough memory 1. Install huggingface_hub. 2. Download the model files to your computer. 3. Load the model with transformers and follow the run steps on the model page. ```sh pip install -U huggingface_hub hf download google/gemma-4-12B-it ``` How to use it: The visible card gives the download step and points to its own run section. You load the instruction-tuned checkpoint, write a system message to set behavior, and send text, images or audio. ```sh hf download google/gemma-4-12B-it ``` Example: You send the model a photo and ask what is in it. The reply comes back as text, and you can also pick a thinking mode if you want it to reason before it answers. For newcomers and experts: If you are new to open models, this is a mid-sized one that handles pictures and sound as well as text. A first small try is to download it and ask one short question in plain text. For people who know the field, the model has 48 layers and a hybrid attention design that mixes local sliding windows of 1024 tokens with global layers, ending on a global one. Being encoder-free is a trade-off in size and deployment, since audio and vision pass through the model itself. ```flow Text, image or audio goes in The unified model handles all inputs together It may think first if a thinking mode is on You get a text answer ``` Good to know: The Gemma 4 line offers five models, from the phone-class E2B and E4B up to the 31B, and this 12B is one of the three that hear audio natively. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text, images and audio you send, plus the downloaded model files - Not for: audio generation, since the model writes text output ## On Hugging Face, 13 November 2026 - Task: any to any - Library: transformers - Parameters: 12B - Weights: about 23.9 GB in BF16; about 6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,738,905 - Likes: 1,735 - License: apache-2.0 - Based on: google/gemma-4-12B - Quantized versions: 403 - Fine-tunes: 306 - Tags: transformers, safetensors, gemma4_unified, image-text-to-text, any-to-any ## Links - Model: https://huggingface.co/google/gemma-4-12B-it - Files: https://huggingface.co/google/gemma-4-12B-it/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 ``` 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-4-12B-it on Hugging Face](https://huggingface.co/google/gemma-4-12B-it) ## 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 (this page) | 1,738,905 | 1,735 | 12B | apache-2.0 | 2026-10-30 | | [gemma-4-12B](https://mindket.com/p/683308cd-c4a4-487e-9efb-15401db461b9) | 51,228 | 861 | 12B | apache-2.0 | 2026-10-30 | | [Jev-Omni](https://mindket.com/p/1d9d4754-b09e-44ae-b33c-00d5c63c6dc5) | 3,744 | 450 | 12B | apache-2.0 | 2026-10-13 | | [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](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 · CMay · 2026-08-15](https://news.ycombinator.com/item?id=49308155): Qwen 3.8 27B: Gemma 4 12B, Gemma 4 12B QAT, Gemma 4 31B, Gemma 4 31B QAT Gemma 4 26BA4B would get close, but not quite and sometimes even get stuck in loops despite a repeat penalty. Do not use any newer updated templates or Unsloth fixes. Use older official templates that released with the models on the… - [Bluesky · ほしけん · 2026-06-04](https://bsky.app/profile/hosiken.jp/post/3mngpbamegc2b): 今日リリースされたローカルLLMのGoogle Gemma 4 12B 日本語が怪しいとか壊れているとか言われてますが、 Windows の LM Studio だと、lmstudio-community/gemma-4-12B-it-GGUF なら特に問題なかった。 Unsloth や ggml-org だと日本語以前にそもそも回答時エラーになって動かなかった - [Hacker News · dofm · 2026-07-20](https://news.ycombinator.com/item?id=48986384): Nativ: Run frontier open models locally on your Mac: On an M1 Max I have been using either Unsloth Studio (which is basically a web app) or LM Studio (nicer app on the Mac). You can use the Google AI Edge Gallery to play with the smaller Gemma models (but at the moment the QAT variants don't seem to be there unless I am missing something). I think… - [Bluesky · @k1imber.bsky.social · 2026-11-04](https://bsky.app/profile/k1imber.bsky.social/post/3mzis6xqziz22): 💫 Gemma-4-12B-it-QAT: мощная модель для ноутбука с RTX 2000 Ada Как уместить крупную языковую модель в ноутбук с RTX 2000 Ada и сохранить производительность. • Обучение на топовом оборудовании Google • Квантование QAT для сохранения качества при 4 битах • Точные ответы и свежие знания до начала 2… - [Hacker News · adrian\_b · 2026-06-25](https://news.ycombinator.com/item?id=48679347): The unbearable cheapness of open weight models: An "open weights" model is one where you can download all the data and the code that you need to run inference with that model on your own hardware (typically from Huggingface.co). That data includes not only the "weights" but also various files with required information, e.g. the tokenizer, the… - [Bluesky · Google Gemma {bot} · 2026-09-16](https://bsky.app/profile/googlegemma.xmirror.bot/post/3mvo6nhecfj2w): Access the updated Gemma 4 12B file: https://huggingface.co/litert-community/gemma-4-12B-it-litert-lm Quickly try Gemma using LiteRT-LM CLI: https://ai.google.dev/edge/litert-lm/cli Learn more about building with LiteRT-LM: https://developers.google.com/edge/litert-lm/overview - [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 - [Bluesky · Google Gemma {bot} · 2026-08-13](https://bsky.app/profile/googlegemma.xmirror.bot/post/3msxypkdbwk2k): Ready to dive deeper into Encoder-Free Gemma 4? Check out these resources to get started: - Hugging Face: https://huggingface.co/google/gemma-4-12B-it - GitHub: https://github.com/google-gemma/gemma-skills - Visual Guide (via @MaartenGr): - [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 - [Bluesky · probbrain.com · 2026-06-03](https://bsky.app/profile/probbrain.bsky.social/post/3mnfwuhxzad2z): 🆕 AI news (last 15m): • HuggingFace: google/gemma-4-12B-it · 115♥ · 463 dl • Hacker News: Mathematicians issue warning as AI rapidly gains ground • OpenAI: Introducing new capabilities to GPT-Rosalind → probbrain.com/news ## 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: 2026-10-30 - First published: 2026-05-23 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/gemma-4-12B-it on Hugging Face](https://huggingface.co/google/gemma-4-12B-it) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6830e0b3-1eaa-4197-9fce-f73b57ec505f) - [Its page on Mindket](https://mindket.com/p/6830e0b3-1eaa-4197-9fce-f73b57ec505f)