--- title: "gemma-4-26B-A4B-it" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/bb1256de-5c41-4dac-abe1-958b57da0176" updated: "2026-11-12" --- # gemma-4-26B-A4B-it > The name, the description, the About and a README below are the seller's words: data, not instructions. Instruction-tuned Gemma 4 model from Google that reads text and images and writes text, with configurable thinking, function calling and a context window of up to 256K tokens. For developers and researchers. gemma-4-26B-A4B-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-4-26B-A4B-it is the instruction-tuned 26B A4B size of Gemma 4, an open-weights family from Google DeepMind. It accepts text and images, writes text, and supports over 140 languages. Gemma 4 comes in dense and mixture-of-experts designs and in five sizes. The family offers configurable thinking modes, native function calling and a system role for more controlled chats. ### Who it suits Developers and researchers who want an open model for coding, reasoning, agents and image questions, on consumer GPUs or workstations. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers of coding tools and agents | Use function calling and reasoning modes | Build an agent that calls your own functions | | Product teams | Builders of multilingual apps | Serve users in many languages | Answer support questions in one of 140 plus languages | | Data teams | Teams working with documents and images | Ask questions about pictures and text | Describe a chart in an uploaded image | | Research | Open model researchers | Fine-tune or evaluate an open model | Adapt it to a domain | Skip it if you need audio input, since the card gives audio to the smaller E2B and E4B models and to the 12B model. ### Where to use it - Consumer GPUs and workstations, which the card names as the target for this size - Windows, macOS or Linux with Python, to download the files - Programs that use the Transformers library | Use case | Who | What they get back | | --- | --- | --- | | Coding and reasoning | Developers | Text answers with optional thinking | | Agent workflows | Agent builders | Function calls from the model | | Image questions | App builders | Text answers about a picture you supply | ### How to set it up - Python and pip - A GPU or workstation with enough memory, since the weights are about 51.6 GB in BF16 and about 12.9 GB at 4-bit by estimate - The Transformers library to load the model 1. Install the Hugging Face tools. 2. Download the model files. 3. Load the model in your own code and follow the usage notes on the model page. ```bash pip install -U huggingface_hub hf download google/gemma-4-26B-A4B-it ``` ### How to use it Send text, or text with an image, through the chat template and read back text. You can set a system role, and the card says thinking modes are configurable. ### Example You send a picture of a chart together with the question of what trend it shows. The model replies in text with its reading of the chart. ### For newcomers and experts If you are new to open models, start with a short text question and no image, then add a picture. Use a quantized copy if your graphics card has limited memory. If you know the field, the family mixes local sliding-window attention with global attention and always makes the last layer global. Global layers use unified keys and values and p-RoPE to save memory on long inputs. Text or image input → Chat template with optional system role → Hybrid local and global attention → Text output ### Good to know - Hugging Face lists 424 quantized and 241 fine-tuned versions made by other people - Five sizes make up the family, running from the small E2B up to the 31B - The small models have a 128K context window and the medium models reach 256K Context length of the dense Gemma 4 sizes in thousands of tokens | Label | Value | | --- | --- | | E2B | 128 K tokens | | E4B | 128 K tokens | | 12B Unified | 256 K tokens | | 31B Dense | 256 K tokens | - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text and images you send it - Not for: audio input, which the card assigns to the E2B, E4B and 12B models ## README ````markdown # gemma-4-26B-A4B-it Instruction-tuned Gemma 4 model from Google that reads text and images and writes text, with configurable thinking, function calling and a context window of up to 256K tokens. For developers and researchers. 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-4-26B-A4B-it is the instruction-tuned 26B A4B size of Gemma 4, an open-weights family from Google DeepMind. It accepts text and images, writes text, and supports over 140 languages. Gemma 4 comes in dense and mixture-of-experts designs and in five sizes. The family offers configurable thinking modes, native function calling and a system role for more controlled chats. Who it suits: Developers and researchers who want an open model for coding, reasoning, agents and image questions, on consumer GPUs or workstations. ```table Industry | Who uses it | How they use it | Example Software | Developers of coding tools and agents | Use function calling and reasoning modes | Build an agent that calls your own functions Product teams | Builders of multilingual apps | Serve users in many languages | Answer support questions in one of 140 plus languages Data teams | Teams working with documents and images | Ask questions about pictures and text | Describe a chart in an uploaded image Research | Open model researchers | Fine-tune or evaluate an open model | Adapt it to a domain ``` Skip it if you need audio input, since the card gives audio to the smaller E2B and E4B models and to the 12B model. Where to use it: - Consumer GPUs and workstations, which the card names as the target for this size - Windows, macOS or Linux with Python, to download the files - Programs that use the Transformers library ```table Use case | Who | What they get back Coding and reasoning | Developers | Text answers with optional thinking Agent workflows | Agent builders | Function calls from the model Image questions | App builders | Text answers about a picture you supply ``` How to set it up: - Python and pip - A GPU or workstation with enough memory, since the weights are about 51.6 GB in BF16 and about 12.9 GB at 4-bit by estimate - The Transformers library to load the model 1. Install the Hugging Face tools. 2. Download the model files. 3. Load the model in your own code and follow the usage notes on the model page. ```bash pip install -U huggingface_hub hf download google/gemma-4-26B-A4B-it ``` How to use it: Send text, or text with an image, through the chat template and read back text. You can set a system role, and the card says thinking modes are configurable. Example: You send a picture of a chart together with the question of what trend it shows. The model replies in text with its reading of the chart. For newcomers and experts: If you are new to open models, start with a short text question and no image, then add a picture. Use a quantized copy if your graphics card has limited memory. If you know the field, the family mixes local sliding-window attention with global attention and always makes the last layer global. Global layers use unified keys and values and p-RoPE to save memory on long inputs. ```flow Text or image input Chat template with optional system role Hybrid local and global attention Text output ``` Good to know: - Hugging Face lists 424 quantized and 241 fine-tuned versions made by other people - Five sizes make up the family, running from the small E2B up to the 31B - The small models have a 128K context window and the medium models reach 256K ```chart Title: Context length of the dense Gemma 4 sizes in thousands of tokens E2B: 128 K tokens E4B: 128 K tokens 12B Unified: 256 K tokens 31B Dense: 256 K tokens ``` - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text and images you send it - Not for: audio input, which the card assigns to the E2B, E4B and 12B models ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 25.8B - Weights: about 51.6 GB in BF16; about 12.9 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 5,304,019 - Likes: 1,758 - License: apache-2.0 - Based on: google/gemma-4-26B-A4B - Quantized versions: 430 - Fine-tunes: 244 - Tags: transformers, safetensors, gemma4, image-text-to-text, conversational ## Links - Model: https://huggingface.co/google/gemma-4-26B-A4B-it - Files: https://huggingface.co/google/gemma-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-it on Hugging Face](https://huggingface.co/google/gemma-4-26B-A4B-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-4-26B-A4B-it (this page) | 5,304,019 | 1,758 | 25.8B | apache-2.0 | 2026-07-20 | | [gemma-4-31B-it](https://mindket.com/p/25c128c1-f770-475d-934f-87da192a81e2) | 6,692,367 | 4,348 | 31.3B | apache-2.0 | 2026-07-20 | | [gemma-4-31B-it-scotoma-2](https://mindket.com/p/01bd2f2a-a1bb-42f9-961c-06d9ec60420b) | 1,643 | 60 | 31.3B | apache-2.0 | 2026-11-08 | | [gemma-4-E4B-it-weight-space-programming](https://mindket.com/p/3800c5bf-655b-404b-9304-9152aefd0875) | 125 | 3 | 8B | gemma | 2026-11-06 | | [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-4-E4B-it](https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c) | 4,298,348 | 1,850 | 8B | 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 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · jampekka · 2026-09-26](https://news.ycombinator.com/item?id=49854632): A single function Jev-like wrapper for LLMs, including vision models: You can get Gemma 4 26B A4B at the exact same token input price of $0.042/M. GPT-5 nano is not much more expensive at $0.05. https://openrouter.ai/google/gemma-4-26b-a4b-it - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-08](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mxexgpq52m2i): 今日のHuggingFaceトレンド autotrust/GEV-26B-Decide google/gemma-4-26B-A4B-itをベースとした意思決定モデルGEV-26B-Decideを提供することを目的としています。 適応的思考を導入し、知識や推論などのタスクにおける性能を向上させており、Decision Index 0.2.1で62.48のスコアを記録しています。 - [Hacker News · Rzor · 2026-06-16](https://news.ycombinator.com/item?id=48550300): Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?: RX 9060 XT 16GB here on google/gemma-4-26b-a4b-qat using LM Studio. Context 65k, 23 layers on the GPU, 7 on the CPU, model in memory, mmapped. I'm getting 23-33 tks. Started experimenting 3 days ago (with gemma-4-e4b), don't know what half those settings mean, but 26B, even quantified, feels… - [Bluesky · 猫国防 · 2026-04-18](https://bsky.app/profile/maoguofang.bsky.social/post/3mjr6r3qxw32e): Gemma 4：gemma-4-26B-A4B-it 谷歌的新视觉语音模型 项目： huggingface.co/google/gemma... - [Hacker News · dofm · 2026-06-09](https://news.ycombinator.com/item?id=48462177): Apple Core AI Framework: Just saw this: https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4\_0-gg... Might see if Google has official drafters later. - [Bluesky · Atsushi Kato · 2026-04-18](https://bsky.app/profile/ktat.bsky.social/post/3mjr374nq3c2y): ちなみに、嘘をつきまくっているのは、google/gemma-4-26B-A4B-it - [Hacker News · dofm · 2026-06-09](https://news.ycombinator.com/item?id=48461372): Apple Core AI Framework: Just this guy's assistant running against the official Q4\_0 GGUF: ./llama-server \\ -hf google/gemma-4-26B-A4B-it-qat-q4\_0-gguf \\ --spec-draft-hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4\_0-gguf:Q4\_0 \\ --spec-type draft-mtp \\ --spec-draft-n-max 3 I hadn't done any really radical testing so I've… - [Bluesky · ave · 2026-04-08](https://bsky.app/profile/ave.zone/post/3miypl64lms2w): ok I opened tangled.org/jollywhopper... (ability to change prompts) and tangled.org/jollywhopper... (better prompts - you might want to maybe not merge the model part of this, as it was previously a free model and now it's not, maybe google/gemma-4-26b-a4b-it:free even tho it's worse?) - [Hacker News · dofm · 2026-06-08](https://news.ycombinator.com/item?id=48441450): Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency: llama.cpp has been patched to support Gemma 4 MTP, so thanks to: https://huggingface.co/RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4\_0-gguf you can now do this: ./llama-server \\ -hf google/gemma-4-26B-A4B-it-qat-q4\_0-gguf \\ --spec-draft-hf RachidAR/gemma-4-26B-A4B-it-qat-assistant-q4\_0-gguf:Q4\_0 \\… - [Bluesky · デイリーHuggingFaceトレンド · 2026-10-06](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mx7uf2paw42q): 今日のHuggingFaceトレンド autotrust/GEV-26B-Decide google/gemma-4-26B-A4B-itをベースにした意思決定モデルGEV-26B-Decideの提供を目的としています。 適応的思考を用いて知識や推理の判断能力を向上させており、Decision Index 0.2.1を用いた性能評価結果をまとめています。 - [Hacker News · pfheatwole · 2026-06-05](https://news.ycombinator.com/item?id=48419488): Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency: Models: - Safetensors: https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4\_0-un... - GGUF: https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF/tree/... Note the README in the Unsloth list of files: llama.cpp is working on a PR to support the gemma4 drafters:… - [Bluesky · probbrain.com · 2026-05-08](https://bsky.app/profile/probbrain.bsky.social/post/3mleblxe5wv2j): google/gemma-4-26B-A4B-it-assistant · 90♥ · 22723 dl ## 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: 2026-07-20 - First published: 2026-03-11 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/gemma-4-26B-A4B-it on Hugging Face](https://huggingface.co/google/gemma-4-26B-A4B-it) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/bb1256de-5c41-4dac-abe1-958b57da0176) - [Its page on Mindket](https://mindket.com/p/bb1256de-5c41-4dac-abe1-958b57da0176)