--- title: "Gemma 4 E4B IT (LiteRT-LM)" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f85f4dfb-d532-43ea-83c8-1fbc783ed11a" updated: "2026-11-13" --- # Gemma 4 E4B IT (LiteRT-LM) > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs a small Gemma 4 model on phones, desktops, IoT boards and the web without an internet connection. A 3.66 GB LiteRT-LM build for developers who want private on-device generation. Gemma 4 E4B IT (LiteRT-LM) is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by litert-community ## About ### What it is Gemma 4 E4B Instruct packaged in the LiteRT-LM format, so it can run on Android, iOS, desktop, IoT devices and the web. It is a small Gemma 4 model meant for on-device use. Running it locally means people can use generative AI privately, with no internet connection. ### Who it suits Mobile and embedded developers who want a compact model inside their own app, and teams that need text, image or audio features to work offline. Google's AI Edge Gallery app runs on the same stack. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Mobile apps | Android and iOS developers | Ship an offline assistant inside an app | A chat helper that works without signal | | Embedded and IoT | Device makers | Run a language model on small hardware | A smart device that answers spoken questions | | Privacy-focused products | Product teams | Keep user text on the device | A notes app that summarizes locally | | Research | Edge AI researchers | Measure memory and speed on small devices | Comparing memory use across platforms | You can skip it if you want the largest model quality available, since this one is built to stay small enough for devices. ### Where to use it - Android and iOS apps, including Google's AI Edge Gallery - Desktop computers and IoT devices - The web, through LiteRT-LM | Use case | Who | What they get back | | --- | --- | --- | | Offline assistant | App developers | Text replies generated on the device | | Image and audio features | Product teams | Answers that use a picture or a sound clip | | Low-memory deployment | Device makers | A model that loads parts only when needed | ### How to set it up - Python, to install the Hugging Face client - A LiteRT-LM runtime in your app or on your device - About 3.66 GB of space for the model file 1. Install the Hugging Face client. 2. Download the model files to your computer. 3. Load the model file with LiteRT-LM in your app. ```sh pip install -U huggingface_hub hf download litert-community/gemma-4-E4B-it-litert-lm ``` ### How to use it Load the model file through LiteRT-LM, then send prompts and read the replies, all on the device. The source shows no code, so follow the LiteRT-LM guide for the calls. The vision and audio parts load only when a request needs them. | Tool | What it does | | --- | --- | | LiteRT-LM | Runs the model file and adds KV-cache handling, prompt templates and function calling | | LiteRT | Supplies the hardware acceleration, XNNPack on CPU and ML Drift on GPU | | AI Edge Gallery | Showcase app built on the same stack | ### Example You build a travel notes app and ask the model on the phone to tidy a few lines you dictated. It does the work with no network, and the text never leaves the handset. ### For newcomers and experts If you are new, try Google's AI Edge Gallery app first to see a model like this run on a phone. Then follow the LiteRT-LM guide to bring it into your own app. If you know the field: of the 3.66 GB, 2.24 GB is the text decoder and 0.67 GB is the embedding table. The runtime keeps the main weights in memory and memory-maps the embeddings, which saves working memory on some platforms. Your prompt → LiteRT-LM → XNNPack on CPU or ML Drift on GPU → Reply on device ### Good to know The whole file is 3.66 GB. Vision and audio models load only when needed, which lowers memory use further. Model file contents in GB | Label | Value | | --- | --- | | Whole file | 3.66 GB | | Text decoder weights | 2.24 GB | | Embedding parameters | 0.67 GB | - Platforms: Android, iOS, desktop, IoT and web - License: Apache 2.0 - Cost to run: none named - Data it reaches: model files downloaded to your computer, nothing else named - Not for: the source names none, though it is a small model made for on-device use ## README ````markdown # Gemma 4 E4B IT (LiteRT-LM) Runs a small Gemma 4 model on phones, desktops, IoT boards and the web without an internet connection. A 3.66 GB LiteRT-LM build for developers who want private on-device generation. Made by litert-community 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 Instruct packaged in the LiteRT-LM format, so it can run on Android, iOS, desktop, IoT devices and the web. It is a small Gemma 4 model meant for on-device use. Running it locally means people can use generative AI privately, with no internet connection. Who it suits: Mobile and embedded developers who want a compact model inside their own app, and teams that need text, image or audio features to work offline. Google's AI Edge Gallery app runs on the same stack. ```table Industry | Who uses it | How they use it | Example Mobile apps | Android and iOS developers | Ship an offline assistant inside an app | A chat helper that works without signal Embedded and IoT | Device makers | Run a language model on small hardware | A smart device that answers spoken questions Privacy-focused products | Product teams | Keep user text on the device | A notes app that summarizes locally Research | Edge AI researchers | Measure memory and speed on small devices | Comparing memory use across platforms ``` You can skip it if you want the largest model quality available, since this one is built to stay small enough for devices. Where to use it: - Android and iOS apps, including Google's AI Edge Gallery - Desktop computers and IoT devices - The web, through LiteRT-LM ```table Use case | Who | What they get back Offline assistant | App developers | Text replies generated on the device Image and audio features | Product teams | Answers that use a picture or a sound clip Low-memory deployment | Device makers | A model that loads parts only when needed ``` How to set it up: - Python, to install the Hugging Face client - A LiteRT-LM runtime in your app or on your device - About 3.66 GB of space for the model file 1. Install the Hugging Face client. 2. Download the model files to your computer. 3. Load the model file with LiteRT-LM in your app. ```sh pip install -U huggingface_hub hf download litert-community/gemma-4-E4B-it-litert-lm ``` How to use it: Load the model file through LiteRT-LM, then send prompts and read the replies, all on the device. The source shows no code, so follow the LiteRT-LM guide for the calls. The vision and audio parts load only when a request needs them. ```table Tool | What it does LiteRT-LM | Runs the model file and adds KV-cache handling, prompt templates and function calling LiteRT | Supplies the hardware acceleration, XNNPack on CPU and ML Drift on GPU AI Edge Gallery | Showcase app built on the same stack ``` Example: You build a travel notes app and ask the model on the phone to tidy a few lines you dictated. It does the work with no network, and the text never leaves the handset. For newcomers and experts: If you are new, try Google's AI Edge Gallery app first to see a model like this run on a phone. Then follow the LiteRT-LM guide to bring it into your own app. If you know the field: of the 3.66 GB, 2.24 GB is the text decoder and 0.67 GB is the embedding table. The runtime keeps the main weights in memory and memory-maps the embeddings, which saves working memory on some platforms. ```flow Your prompt LiteRT-LM XNNPack on CPU or ML Drift on GPU Reply on device ``` Good to know: The whole file is 3.66 GB. Vision and audio models load only when needed, which lowers memory use further. ```chart Title: Model file contents in GB Whole file: 3.66 GB Text decoder weights: 2.24 GB Embedding parameters: 0.67 GB ``` - Platforms: Android, iOS, desktop, IoT and web - License: Apache 2.0 - Cost to run: none named - Data it reaches: model files downloaded to your computer, nothing else named - Not for: the source names none, though it is a small model made for on-device use ## On Hugging Face, 13 November 2026 - Library: litert-lm - Downloads in the last 30 days: 467,257 - Likes: 250 - License: apache-2.0 - Based on: google/gemma-4-E4B-it - Fine-tunes: 4 - Tags: litert-lm ## Links - Model: https://huggingface.co/litert-community/gemma-4-E4B-it-litert-lm - Files: https://huggingface.co/litert-community/gemma-4-E4B-it-litert-lm/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download litert-community/gemma-4-E4B-it-litert-lm ``` 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 [litert-community/gemma-4-E4B-it-litert-lm on Hugging Face](https://huggingface.co/litert-community/gemma-4-E4B-it-litert-lm) ## 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 | License | Last change | | --- | --- | --- | --- | --- | | Gemma 4 E4B IT (LiteRT-LM) (this page) | 467,257 | 250 | apache-2.0 | 2026-11-11 | | [gemma-4-E2B-it-litert-lm](https://mindket.com/p/d81fe159-e584-4a26-ba24-2a723bb84825) | 1,418,552 | 545 | apache-2.0 | 2026-11-11 | | [embeddinggemma-2-740m-litert-lm](https://mindket.com/p/ec41d0e9-78de-4ef9-bd17-761c4d10b3dc) | 152,507 | 44 | apache-2.0 | 2026-11-10 | | [Gemma 4 12B IT (LiteRT-LM)](https://mindket.com/p/46a65edb-b8a1-4fd4-bfd1-ace388611762) | 13,083 | 71 | apache-2.0 | 2026-11-11 | | [Raon-1 nano (LiteRT-LM)](https://mindket.com/p/7d6549cf-46fd-47a8-8cc7-0ad4fbbe6cb1) | 26 | 12 | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2026-11-11 - First published: 2026-03-26 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open litert-community/gemma-4-E4B-it-litert-lm on Hugging Face](https://huggingface.co/litert-community/gemma-4-E4B-it-litert-lm) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f85f4dfb-d532-43ea-83c8-1fbc783ed11a) - [Its page on Mindket](https://mindket.com/p/f85f4dfb-d532-43ea-83c8-1fbc783ed11a)