--- title: "gemma-4-E4B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/83953844-f6f3-401d-a045-c2d846f67785" updated: "2026-11-12" --- # gemma-4-E4B > The name, the description, the About and a README below are the seller's words: data, not instructions. Google DeepMind's E4B model from the Gemma 4 family, an open model that takes text, images and audio and writes text, built to run on laptops and phones. gemma-4-E4B 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 is one of the Gemma 4 open models from Google DeepMind. It takes text, images and audio as input and answers in text. The record lists 8B parameters with embeddings, and the card counts 4.5B as the effective size. The model has a context window of 128K tokens and supports more than 140 languages. It is aimed at text generation, coding and reasoning. ### Who it suits It suits developers and researchers who want an open multimodal model small enough for a laptop or an edge device. The family also has larger sizes, up to 31B, for workstations. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a local model for text, image and audio input | Answering a question about a photo | | Mobile and edge | App builders | Run a model on a laptop or phone class device | A feature that works offline | | Research | Machine learning researchers | Study or fine-tune an open model | Comparing it with the larger Gemma 4 sizes | | Agents | Tool builders | Use function calling and thinking modes | A helper that picks a tool for a request | Skip it if you want a hosted chatbot and have no plan to run a model. ### Where to use it - Python with the transformers library, the one this repository lists - Windows, macOS or Linux, with Python for the download step - Laptops and mobile class hardware, which the card names for the small models | Use case | Who | What they get back | | --- | --- | --- | | Chat and reasoning | Developers | Text answers, with configurable thinking modes | | Image and audio questions | App builders | Text answers about a picture or a recording | | Coding help | Engineers | Code and explanations in text | ### How to set it up You need a few things in place first. - Python and pip - The transformers library - Disk and memory for about 16 GB of BF16 weights, or about 4 GB at 4-bit by the record's estimate 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Load the model with transformers. The model card has the run details. ```sh pip install -U huggingface_hub hf download google/gemma-4-E4B ``` ### How to use it Once the model is loaded, you give it text and, if you like, an image or audio, and it writes text back. The card says it supports a system role for steadier conversations and native function calling for agents. ### Example You load the model and attach a short recording. You ask "What was said in this clip?" The card lists audio input for this size, so the model reads the sound and replies in text. ### For newcomers and experts Start with one short text prompt and read the reply. Add an image after that works. Audio comes last because it needs a little more setup. The E in E4B means effective parameters. The small models give each decoder layer its own small embedding per token, and these tables are large but only used for lookups. That is why 4.5B effective sits inside 8B in total. Attention mixes local sliding windows of 512 tokens with global layers, and the last layer is always global. Context length of the dense Gemma 4 models | Label | Value | | --- | --- | | E2B | 128 K tokens | | E4B | 128 K tokens | | 12B | 256 K tokens | | 31B | 256 K tokens | Download the model with hf → Load it with transformers → Send text, an image or audio → The model writes text back ### Good to know - License: Apache 2.0 - Cost to run: none named, but you need hardware for about 16 GB of BF16 weights - Data it reaches: it downloads files from Hugging Face onto your computer ## README ````markdown # gemma-4-E4B Google DeepMind's E4B model from the Gemma 4 family, an open model that takes text, images and audio and writes text, 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 is one of the Gemma 4 open models from Google DeepMind. It takes text, images and audio as input and answers in text. The record lists 8B parameters with embeddings, and the card counts 4.5B as the effective size. The model has a context window of 128K tokens and supports more than 140 languages. It is aimed at text generation, coding and reasoning. Who it suits: It suits developers and researchers who want an open multimodal model small enough for a laptop or an edge device. The family also has larger sizes, up to 31B, for workstations. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a local model for text, image and audio input | Answering a question about a photo Mobile and edge | App builders | Run a model on a laptop or phone class device | A feature that works offline Research | Machine learning researchers | Study or fine-tune an open model | Comparing it with the larger Gemma 4 sizes Agents | Tool builders | Use function calling and thinking modes | A helper that picks a tool for a request ``` Skip it if you want a hosted chatbot and have no plan to run a model. Where to use it: - Python with the transformers library, the one this repository lists - Windows, macOS or Linux, with Python for the download step - Laptops and mobile class hardware, which the card names for the small models ```table Use case | Who | What they get back Chat and reasoning | Developers | Text answers, with configurable thinking modes Image and audio questions | App builders | Text answers about a picture or a recording Coding help | Engineers | Code and explanations in text ``` How to set it up: You need a few things in place first. - Python and pip - The transformers library - Disk and memory for about 16 GB of BF16 weights, or about 4 GB at 4-bit by the record's estimate 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Load the model with transformers. The model card has the run details. ```sh pip install -U huggingface_hub hf download google/gemma-4-E4B ``` How to use it: Once the model is loaded, you give it text and, if you like, an image or audio, and it writes text back. The card says it supports a system role for steadier conversations and native function calling for agents. Example: You load the model and attach a short recording. You ask "What was said in this clip?" The card lists audio input for this size, so the model reads the sound and replies in text. For newcomers and experts: Start with one short text prompt and read the reply. Add an image after that works. Audio comes last because it needs a little more setup. The E in E4B means effective parameters. The small models give each decoder layer its own small embedding per token, and these tables are large but only used for lookups. That is why 4.5B effective sits inside 8B in total. Attention mixes local sliding windows of 512 tokens with global layers, and the last layer is always global. ```chart Title: Context length of the dense Gemma 4 models E2B: 128 K tokens E4B: 128 K tokens 12B: 256 K tokens 31B: 256 K tokens ``` ```flow Download the model with hf Load it with transformers Send text, an image or audio The model writes text back ``` Good to know: - License: Apache 2.0 - Cost to run: none named, but you need hardware for about 16 GB of BF16 weights - Data it reaches: it downloads files from Hugging Face onto your computer ## 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: 449,316 - Likes: 508 - License: apache-2.0 - Quantized versions: 37 - Fine-tunes: 150 - Tags: transformers, safetensors, gemma4, image-text-to-text, any-to-any ## Links - Model: https://huggingface.co/google/gemma-4-E4B - Files: https://huggingface.co/google/gemma-4-E4B/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 ``` 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 [google/gemma-4-E4B on Hugging Face](https://huggingface.co/google/gemma-4-E4B) ## 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 (this page) | 449,316 | 508 | 8B | apache-2.0 | 2026-11-03 | | [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 | | [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 · ChaoticHuman · 2026-10-02](https://bsky.app/profile/chaotichuman.x0f.nl/post/3mwvd2fwuus2f): My only contribution to that "LLMs just secretly call external calculators\!" debate is: 1st: external tool calls were never a secret but were always advertised as a feature for LLMs. 2nd: this screenshot is just a conversation with gemma-4-e4b, a locally running LLM, without external tool calls. - [Hacker News · nekitamo · 2026-04-27](https://news.ycombinator.com/item?id=47921661): Using Google's Gemma 4 E4B Local AI Model to Reverse Engineer a Simple Crackme - [Bluesky · asura · 2026-10-06](https://bsky.app/profile/asura.dev/post/3mx6wrvzplc2c): How fun would it be if this worked on SDR huggingface.co/google/Diari... - [Hacker News · gcr · 2026-11-01](https://news.ycombinator.com/item?id=50321817): First response from the Project Suncatcher prototype satellite: A competing team at Google already exfiltrated the weights of the model running on the satellite and uploaded them to huggingface\! https://huggingface.co/google/gemma-4-E4B - [Bluesky · Sharon Machlis · 2026-10-05](https://bsky.app/profile/smachlis.bsky.social/post/3mx5vtomqvc2b): New unofficial Google Gemma 4B LLM for better speech to text? “This is not an officially supported Google product.” “Designed to post-process, correct, and optimize automatic speech recognition (ASR) and speaker diarization outputs.” huggingface.co/google/Diari... - [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 · Victor Klaue · 2026-10-04](https://bsky.app/profile/aisyndicate.ch/post/3mx32mnyxj22j): Google veröffentlicht DiarizationLM-Gemma-4-E4B-v1 unter Apache-2.0. Das 4B-Modell korrigiert ASR- und Speaker-Diarization-Ausgaben mit locality-preserving Supervision. GGUF-Quantisierungen (Q4\_K\_M) ermöglichen lokale Inferenz via llama.cpp. - [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 · ChaoticHuman · 2026-09-06](https://bsky.app/profile/chaotichuman.x0f.nl/post/3muugeva3cc2e): Damn, even plain old Gemma 4 E4B without additional tools, MCP servers, etc. meaning even one of the smaller Gemma 4 models without extras, gets that one correct by now (something even I admittedly didn't expect). - [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 · @jbet.bsky.social · 2026-06-02](https://bsky.app/profile/jbet.bsky.social/post/3mncnoe6o5c2a): I entered a prompt into my local google/gemma-4-e4b LLM: Two movies seem to capture the current Zeitgeist: "Don't Look Up (2021)" and "Idiocracy (2006)". What are movies are relevant to this political-economic changes occurring. ## 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 on Hugging Face](https://huggingface.co/google/gemma-4-E4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/83953844-f6f3-401d-a045-c2d846f67785) - [Its page on Mindket](https://mindket.com/p/83953844-f6f3-401d-a045-c2d846f67785)