gemma-4-E4B

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.

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.

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.

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

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:

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.