--- title: "gemma-4-E4B-it" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c" updated: "2026-11-12" --- # gemma-4-E4B-it > The name, the description, the About and a README below are the seller's words: data, not instructions. Handles text, image and audio input and writes text, with a 128K-token context and support for over 140 languages. A small instruction-tuned Gemma 4 model from Google DeepMind, built to run on laptops and phones. gemma-4-E4B-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-E4B-it is the instruction-tuned E4B model in Google DeepMind's Gemma 4 family of open models. It takes text, images and audio as input and writes text as output. It has 4.5B effective parameters, or 8B counting embeddings, and the card says it is designed for efficient local use on laptops and mobile devices. ### Who it suits Developers who want a small open model for chat, coding, reasoning and multimodal tasks that can run on modest hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software development | Developers | Add a local model for text, code and reasoning | A coding helper that runs on a laptop | | Mobile and edge apps | App builders | Run a model on the device itself | An assistant that works on a phone | | Accessibility and media | Product teams | Work with images and spoken audio | A model that reads an image and answers about it | | Global products | Multilingual teams | Serve users in many languages | Replies in one of the 140-plus supported languages | If you need the largest quality at any cost, you can skip it, because the family also has 12B, 26B A4B and 31B models. ### Where to use it - Python with the transformers library on Windows, macOS or Linux - Laptops and mobile devices, which the card names as targets for the small models - Servers and workstations, where larger Gemma 4 sizes are the card's target | Use case | Who | What they get back | | --- | --- | --- | | Chat and coding | Developers | Text answers and code from a small model | | Image questions | App builders | Text answers about an image you supply | | Audio input | Product teams | Text output from audio, which E4B supports | ### How to set it up - Python and pip - About 16 GB of disk space in BF16, or about 4 GB at 4-bit (an estimate) - The transformers library to load the files 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Load the model with transformers, following the model card. ```sh pip install -U huggingface_hub hf download google/gemma-4-E4B-it ``` ### How to use it Send the model text, an image or audio and read the text it writes back. The model supports a system role for more controlled conversations, and thinking modes you can configure. It also has built-in support for function calling, which agents use to call tools. ### Example You send a photo along with a short question about what it shows. The model reads the image and writes a text answer. You can add a system message to set its tone or role first. ### For newcomers and experts If you are new to this, start with a short text question and a system message, then add an image. The E4B size is the one aimed at phones and laptops. The E in E4B means effective parameters. The small models use per-layer embeddings, which are big tables consulted only for quick lookups. The model has 42 layers, a 512-token sliding window mixed with global attention, a 262K vocabulary, and encoders of about 150M for vision and about 300M for audio. You send text, an image or audio → The model reads it, with thinking if enabled → It writes text as output → You use the answer or call a tool ### Good to know - Context: the small models handle 128K tokens, and the medium ones handle 256K - Output: it generates text only, even when the input is an image or audio Total parameters of the Gemma 4 dense sizes, in billions | Label | Value | | --- | --- | | E2B | 2.3 billion | | E4B | 4.5 billion | | 12B | 11.95 billion | | 31B | 30.7 billion | - License: apache-2.0 - Cost to run: no API key or paid service is named, only your own hardware - Data it reaches: downloading fetches model files, and it reads the text, images and audio you give it - Not for: producing images or audio, since the output is text ## README ````markdown # gemma-4-E4B-it Handles text, image and audio input and writes text, with a 128K-token context and support for over 140 languages. A small instruction-tuned Gemma 4 model from Google DeepMind, built to run on laptops and phones. 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-E4B-it is the instruction-tuned E4B model in Google DeepMind's Gemma 4 family of open models. It takes text, images and audio as input and writes text as output. It has 4.5B effective parameters, or 8B counting embeddings, and the card says it is designed for efficient local use on laptops and mobile devices. Who it suits: Developers who want a small open model for chat, coding, reasoning and multimodal tasks that can run on modest hardware. ```table Industry | Who uses it | How they use it | Example Software development | Developers | Add a local model for text, code and reasoning | A coding helper that runs on a laptop Mobile and edge apps | App builders | Run a model on the device itself | An assistant that works on a phone Accessibility and media | Product teams | Work with images and spoken audio | A model that reads an image and answers about it Global products | Multilingual teams | Serve users in many languages | Replies in one of the 140-plus supported languages ``` If you need the largest quality at any cost, you can skip it, because the family also has 12B, 26B A4B and 31B models. Where to use it: - Python with the transformers library on Windows, macOS or Linux - Laptops and mobile devices, which the card names as targets for the small models - Servers and workstations, where larger Gemma 4 sizes are the card's target ```table Use case | Who | What they get back Chat and coding | Developers | Text answers and code from a small model Image questions | App builders | Text answers about an image you supply Audio input | Product teams | Text output from audio, which E4B supports ``` How to set it up: - Python and pip - About 16 GB of disk space in BF16, or about 4 GB at 4-bit (an estimate) - The transformers library to load the files 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Load the model with transformers, following the model card. ```sh pip install -U huggingface_hub hf download google/gemma-4-E4B-it ``` How to use it: Send the model text, an image or audio and read the text it writes back. The model supports a system role for more controlled conversations, and thinking modes you can configure. It also has built-in support for function calling, which agents use to call tools. Example: You send a photo along with a short question about what it shows. The model reads the image and writes a text answer. You can add a system message to set its tone or role first. For newcomers and experts: If you are new to this, start with a short text question and a system message, then add an image. The E4B size is the one aimed at phones and laptops. The E in E4B means effective parameters. The small models use per-layer embeddings, which are big tables consulted only for quick lookups. The model has 42 layers, a 512-token sliding window mixed with global attention, a 262K vocabulary, and encoders of about 150M for vision and about 300M for audio. ```flow You send text, an image or audio The model reads it, with thinking if enabled It writes text as output You use the answer or call a tool ``` Good to know: - Context: the small models handle 128K tokens, and the medium ones handle 256K - Output: it generates text only, even when the input is an image or audio ```chart Title: Total parameters of the Gemma 4 dense sizes, in billions E2B: 2.3 billion E4B: 4.5 billion 12B: 11.95 billion 31B: 30.7 billion ``` - License: apache-2.0 - Cost to run: no API key or paid service is named, only your own hardware - Data it reaches: downloading fetches model files, and it reads the text, images and audio you give it - Not for: producing images or audio, since the output is text ## On Hugging Face, 13 November 2026 - Task: any to any - Library: transformers - Parameters: 8B - Weights: about 16 GB in BF16; about 4 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 4,298,348 - Likes: 1,850 - License: apache-2.0 - Based on: google/gemma-4-E4B - Quantized versions: 450 - Fine-tunes: 473 - Tags: transformers, safetensors, gemma4, image-text-to-text, any-to-any ## Links - Model: https://huggingface.co/google/gemma-4-E4B-it - Files: https://huggingface.co/google/gemma-4-E4B-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-E4B-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-E4B-it on Hugging Face](https://huggingface.co/google/gemma-4-E4B-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-E4B-it (this page) | 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 | | [gemma-4-E4B](https://mindket.com/p/83953844-f6f3-401d-a045-c2d846f67785) | 449,316 | 508 | 8B | apache-2.0 | 2026-11-03 | | [gemma-4-E2B](https://mindket.com/p/6c25a477-41a4-4be6-9e1a-8b63eb48670f) | 109,387 | 559 | 5.1B | apache-2.0 | 2026-11-03 | | [gemma-4-12B-it-qat-GGUF](https://mindket.com/p/97ef5b61-d969-4212-aa91-1789e4ce409f) | 196,871 | 617 | 11.9B | apache-2.0 | 2026-07-17 | | [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 | Numbers as Mindket last read them, on 2026-11-12. ## 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 · ericlbuehler · 2026-06-18](https://news.ycombinator.com/item?id=48581792): Show HN: Run Agent Skills with mistral.rs v0.8.10: /v1/skills support and more: Hey all\! I'm the maintainer of mistral.rs. I just landed support for OpenAI-compatible Agent Skills via a /v1/skills endpoint, and it works with local open models. Until now Skills have basically been locked to closed models, and with the ability to have private, local intelligence becoming… - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-13](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mjeqijajtz2p): 今日のHuggingFaceトレンド google/gemma-4-E4B-it このリポジトリは、Google DeepMindが開発したオープンウェイトのマルチモーダルAIモデル「Gemma」に関するものです。 テキストと画像を扱え、256Kトークンまでの長い文脈を処理できます。 多様なサイズ（E2B, E4B, 26B, 31B）があり、テキスト生成、コーディング、推論などのタスクに適しています。 Apache 2.0ライセンスで公開されています。 - [Hacker News · whythismatters · 2026-05-05](https://news.ycombinator.com/item?id=48020429): New Gemma chat template update by Google - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-11](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mj7n372pm725): 今日のHuggingFaceトレンド google/gemma-4-E4B-it このリポジトリは、Google DeepMindが開発したオープンウェイトのマルチモーダルAIモデル「Gemma」に関するものです。 テキストと画像を扱え、256Kトークンまでの長い文脈を処理できます。 テキスト生成、コーディング、推論などのタスクに適しており、多様なサイズ（E2B, E4B, 26B, 31B）が用意されています。 Apache 2.0ライセンスで公開されています。 - [Hacker News · wrxd · 2026-05-05](https://news.ycombinator.com/item?id=48028932): Accelerating Gemma 4: faster inference with multi-token prediction drafters: I'm not sure I understand how this work https://huggingface.co/google/gemma-4-E4B-it-assistant has 78.8M parameters while the standard variant https://huggingface.co/google/gemma-4-E4B-it has 8B parameters. Is gemma-4-E4B-it-assistant a model I can use stand-alone or a model I need to use in… - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-09](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mj2njlkvnq2r): 今日のHuggingFaceトレンド google/gemma-4-E4B-it このリポジトリは、Google DeepMindが開発したオープンモデル「Gemma」の公開を目的としています。 Gemma 4は、テキストと画像を扱えるマルチモーダルモデルで、テキスト生成、コーディング、推論などのタスクに適しています。 多様なサイズが用意されており、様々な環境への導入が可能です。 Apache 2.0ライセンスで提供されます。 - [Hacker News · adrian\_b · 2026-05-05](https://news.ycombinator.com/item?id=48027992): Accelerating Gemma 4: faster inference with multi-token prediction drafters: I do not know what you mean by sparse models. All 4 gemma-4-\*-it models, regardless whether they are dense models or MoE models, have associated small models for MTP, whose names are obtained by adding the "-assistant" suffix. https://huggingface.co/google/gemma-4-E2B-it-assistant… - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-07](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mivmf556kn2i): 今日のHuggingFaceトレンド google/gemma-4-E4B-it このリポジトリは、Google DeepMindが開発したオープンモデル「Gemma」の公開を目的としています。 Gemma 4は、テキストと画像を扱えるマルチモーダルモデルで、テキスト生成、コーディング、推論などのタスクに適しています。 多様なサイズが用意されており、様々な環境への導入が可能です。 Apache 2.0ライセンスで提供されます。 - [Hacker News · AbuAssar · 2026-05-05](https://news.ycombinator.com/item?id=48026291): Accelerating Gemma 4: faster inference with multi-token prediction drafters: these are the updated models: google/gemma-4-31B-it-assistant google/gemma-4-26B-A4B-it-assistant google/gemma-4-E4B-it-assistant google/gemma-4-E2B-it-assistant - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-05](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3miqkacanrp2q): 今日のHuggingFaceトレンド google/gemma-4-E4B-it このリポジトリは、Google DeepMindが開発したオープンモデル「Gemma」の公開を目的としています。 Gemma 4は、テキストと画像を扱えるマルチモーダルモデルで、テキスト生成、コーディング、推論などのタスクに適しています。 多様なサイズが用意されており、様々な環境への導入が可能です。 Apache 2.0ライセンスで提供されます。 - [Hacker News · tarruda · 2026-05-05](https://news.ycombinator.com/item?id=48025835): Accelerating Gemma 4: faster inference with multi-token prediction drafters: They also published draft models for E4B and E2B. For those, the draft models are only 78m parameters: https://huggingface.co/google/gemma-4-E4B-it-assistant - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-22](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mk3dwknlsq2o): 今日のHuggingFaceトレンド OBLITERATUS/gemma-4-E4B-it-OBLITERATED GoogleのGemma 4 E4Bモデルからガードレールを完全に除去し、制限のないテキスト生成を可能にすることを目的としています。 OBLITERATUSという手法を用いて拒否率を0%まで下げ、モデルを自由化したバージョンを提供しています。 ## 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: 2026-11-03 - First published: 2026-03-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/gemma-4-E4B-it on Hugging Face](https://huggingface.co/google/gemma-4-E4B-it) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f3466a64-e796-4818-8ee3-88ee4cf92e3c) - [Its page on Mindket](https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c)