--- title: "gemma-4-12B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/683308cd-c4a4-487e-9efb-15401db461b9" updated: "2026-11-12" --- # gemma-4-12B > The name, the description, the About and a README below are the seller's words: data, not instructions. Google's Gemma 4 12B Unified model, an open model that reads text, images, audio and video and writes text, with a 256K context and no separate encoders. For developers running local AI. gemma-4-12B 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 is the 12B Unified model in Google DeepMind's Gemma 4 family of open models. It takes in text, images, audio and video and writes text, and it has about 11.95 billion parameters. The card calls it encoder-free: audio and vision are understood inside the one model, with no separate encoders. ### Who it suits Developers who want one local model for text, pictures and speech, and teams building assistants that run on consumer devices or workstations. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Run a multimodal assistant locally | Ask a question about a photo | | Research | AI researchers | Study an encoder-free multimodal model | Compare it with the other Gemma 4 sizes | | Productivity | Tool builders | Build agents that call functions | Let the model call a calendar tool | Skip it if you need the model to draw images or speak aloud, since it writes text only. ### Where to use it - Local setups with a consumer GPU or a workstation, which the card names as targets - Hugging Face Transformers, which the library tag lists - 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 set | | Image and audio questions | App builders | Text about a picture or a recording | | Agents | Tool builders | Function calls, since the model supports calling functions natively | ### How to set it up - Python and pip to run the download commands - About 23.9 GB of space for the BF16 weights, or about 6 GB at 4-bit by Hugging Face estimate - A runtime that loads Transformers models, following the model card 1. Install the Hugging Face tools. 2. Download the model files. 3. Load the model in Transformers and follow the card's code. ```bash pip install -U huggingface_hub hf download google/gemma-4-12B ``` ### How to use it Load the model and send it text, an image, audio or a mix, then read the text it writes. You can switch on its thinking mode and give it a system message to set the rules for the chat. | Property | 12B Unified | | --- | --- | | Parameters | 11.95B | | Layers | 48 | | Sliding window | 1024 tokens | | Context length | 256K tokens | | Vocabulary | 262K | | Inputs | Text, image, audio | Size of the weights in GB | Label | Value | | --- | --- | | BF16 weights | 23.9 GB | | 4-bit estimate | 6 GB | ### Example You give the model a photo of a whiteboard and ask it to turn the notes into a list. It reads the image itself and answers in text, with no separate vision tool. ### For newcomers and experts If you are new to multimodal models, start with text and one image before you try audio. Keep prompts short until you know how the thinking mode behaves. If you know the field, layers alternate between local sliding-window attention and full global attention, and the last layer is always global. The global layers share Keys and Values and use proportional RoPE to save memory on long contexts. Download the model files → Load the model in Transformers → Send text, an image or audio → Receive text, with optional thinking ### Good to know - The card says audio is supported natively on E2B, E4B and 12B - The model supports the system role for more structured chats - Hugging Face lists 51 quantized and 96 fine-tuned versions made by other people - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download and the text, images and audio you give it - Not for: making images or audio, since the output is text ## README ````markdown # gemma-4-12B Google's Gemma 4 12B Unified model, an open model that reads text, images, audio and video and writes text, with a 256K context and no separate encoders. For developers running local AI. 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 is the 12B Unified model in Google DeepMind's Gemma 4 family of open models. It takes in text, images, audio and video and writes text, and it has about 11.95 billion parameters. The card calls it encoder-free: audio and vision are understood inside the one model, with no separate encoders. Who it suits: Developers who want one local model for text, pictures and speech, and teams building assistants that run on consumer devices or workstations. ```table Industry | Who uses it | How they use it | Example Software | App developers | Run a multimodal assistant locally | Ask a question about a photo Research | AI researchers | Study an encoder-free multimodal model | Compare it with the other Gemma 4 sizes Productivity | Tool builders | Build agents that call functions | Let the model call a calendar tool ``` Skip it if you need the model to draw images or speak aloud, since it writes text only. Where to use it: - Local setups with a consumer GPU or a workstation, which the card names as targets - Hugging Face Transformers, which the library tag lists - 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 set Image and audio questions | App builders | Text about a picture or a recording Agents | Tool builders | Function calls, since the model supports calling functions natively ``` How to set it up: - Python and pip to run the download commands - About 23.9 GB of space for the BF16 weights, or about 6 GB at 4-bit by Hugging Face estimate - A runtime that loads Transformers models, following the model card 1. Install the Hugging Face tools. 2. Download the model files. 3. Load the model in Transformers and follow the card's code. ```bash pip install -U huggingface_hub hf download google/gemma-4-12B ``` How to use it: Load the model and send it text, an image, audio or a mix, then read the text it writes. You can switch on its thinking mode and give it a system message to set the rules for the chat. ```table Property | 12B Unified Parameters | 11.95B Layers | 48 Sliding window | 1024 tokens Context length | 256K tokens Vocabulary | 262K Inputs | Text, image, audio ``` ```chart Title: Size of the weights in GB BF16 weights: 23.9 GB 4-bit estimate: 6 GB ``` Example: You give the model a photo of a whiteboard and ask it to turn the notes into a list. It reads the image itself and answers in text, with no separate vision tool. For newcomers and experts: If you are new to multimodal models, start with text and one image before you try audio. Keep prompts short until you know how the thinking mode behaves. If you know the field, layers alternate between local sliding-window attention and full global attention, and the last layer is always global. The global layers share Keys and Values and use proportional RoPE to save memory on long contexts. ```flow Download the model files Load the model in Transformers Send text, an image or audio Receive text, with optional thinking ``` Good to know: - The card says audio is supported natively on E2B, E4B and 12B - The model supports the system role for more structured chats - Hugging Face lists 51 quantized and 96 fine-tuned versions made by other people - License: Apache 2.0 - Cost to run: none named - Data it reaches: the files you download and the text, images and audio you give it - Not for: making images or audio, since the output is text ## 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: 51,228 - Likes: 861 - License: apache-2.0 - Quantized versions: 51 - Fine-tunes: 97 - Tags: transformers, safetensors, gemma4_unified, image-text-to-text, any-to-any ## Links - Model: https://huggingface.co/google/gemma-4-12B - Files: https://huggingface.co/google/gemma-4-12B/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 ``` 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 on Hugging Face](https://huggingface.co/google/gemma-4-12B) ## 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 (this page) | 51,228 | 861 | 12B | apache-2.0 | 2026-10-30 | | [gemma-4-12B-it](https://mindket.com/p/6830e0b3-1eaa-4197-9fce-f73b57ec505f) | 1,738,905 | 1,735 | 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 · Bender · 2026-06-03](https://news.ycombinator.com/item?id=48390377): Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM - [Bluesky · plautumnpus · 2026-10-28](https://bsky.app/profile/autumn.town/post/3myxpj4qm7s2y): AUTUMN PREDICTED GEMMA 4 12B NEURON 10089??? - [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 · Google Open Source · 2026-10-02](https://bsky.app/profile/opensource.google/post/3mwvz7baj7x2p): This Week in Open Source \#18 is live\! 🌐✨ 🧮 Inside Gemma 4 12B inference \& 100% local RAG (LiteRT + Qdrant) 🧪 Adaptive agent training w/ EnvHarness 🏗️ @mozilla.org \& Mila's open AI stack ⚠️ The maintainer cost of LLM "vibe-coding" 🗓️ Oct/Nov events: goo.gle/twios-18 \#OpenSource - [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 · Felipe Martins 🏳️🌈 · 2026-10-17](https://bsky.app/profile/felipe-martins.bsky.social/post/3my3tjjfhck2w): Só pra não deixar o recado pela metade, o modelo que eu passei a usar local foi o Gemma 4 12B QAT ocupando cerca de 6.5GB - [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 · Gus · 2026-11-04](https://bsky.app/profile/gusthema.bsky.social/post/3mzizxvaqwy2t): 🛡️💎 Forced to finish curl ... https:// 100,000 times in Kamil Czarnogórski's airgapped test, Gemma 4 12B hit 0 cloud metadata IPs, stuck to example.com + GitHub 95.8% of runs, and used Google Search 635x to look up the Linux chore 👀 Write-up: https://int8.io/forced-curl/ - [Hacker News · simonw · 2026-06-16](https://news.ycombinator.com/item?id=48557377): Running local models is good now: I'm still amazed that you can run LLMs of this quality on a machine that costs less than $3,000. I used to assume that anything GPT-4 equivalent or higher would need $30,000+ of server-class hardware. That said... gemma-4-12b-qat is 7.15GB on disk so should run reasonably well in 16GB, that takes… - [Bluesky · Wide Area Intelligence · 2026-11-06](https://bsky.app/profile/wideareaai.bsky.social/post/3mzm4n7lcfr2c): By the numbers: google/gemma-4-12b-qat Context Window: 32768 Max Tokens: 8192 Input Cost: 0 Output Cost: 0 \#thought \#brand \#wide \#area \#intelligence \#preferred - [Hacker News · superkuh · 2026-06-08](https://news.ycombinator.com/item?id=48447578): Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency: Ah, nice, thanks\! https://huggingface.co/google/gemma-4-12B/tree/main No .gguf but I guess I can convert the safetensors myself like it's 2023 again. - [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… ## 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 on Hugging Face](https://huggingface.co/google/gemma-4-12B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/683308cd-c4a4-487e-9efb-15401db461b9) - [Its page on Mindket](https://mindket.com/p/683308cd-c4a4-487e-9efb-15401db461b9)