Gemma-3n-E2B-GGUF Open-source Model - Multiple quantization types balance size and performance, free to deploy

Gemma 3n E2B GGUF

Developed by mradermacher

A static quantized version of the Google Gemma-3n-E2B model, offering various quantization types to balance model size and performance.

Large Language Model

Transformers

English#Multimodal quantization #Speech-to-text conversion #Lightweight and efficient

Downloads 207

Release Time : 7/4/2025

Model Overview

This project provides a quantized version of the Google Gemma-3n-E2B model, supporting multiple tasks, including automatic speech recognition, automatic speech translation, audio-to-text conversion, and video-to-text conversion.

Model Features

Multi-domain application

Supports multiple tasks such as automatic speech recognition, automatic speech translation, audio-to-text conversion, and video-to-text conversion.

Multiple quantization types

Provides quantized versions of different sizes and qualities to meet the needs of different scenarios.

Quick usage

Refer to TheBloke's README for the usage of GGUF files.

Model Capabilities

Automatic speech recognition

Automatic speech translation

Audio-to-text conversion

Video-to-text conversion

Use Cases

Speech processing

Automatic speech recognition

Convert speech to text

Automatic speech translation

Translate the speech of one language into the text of another language

Multimedia processing

Audio-to-text conversion

Convert audio content to text

Video-to-text conversion

Convert the audio content in the video to text

🚀 Gemma-3n-E2B Quantized Model

This project offers static quantizations of the Google Gemma-3n-E2B model, providing various quantized versions for efficient use. It's designed to assist in tasks like automatic speech recognition, translation, and text-to-text conversion.

🚀 Quick Start

If you're new to using GGUF files, check TheBloke's READMEs for detailed instructions, including how to handle multi - part files.

✨ Features

Multiple Quantized Versions: Offers a range of quantized versions, sorted by size, to meet different performance and quality requirements.
Visual Comparison: Includes a graph comparing lower - quality quant types for easy selection.
Community Insights: Provides links to community thoughts on quantization.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

No code examples are provided in the original README.

📚 Documentation

About

This is a static quantization of the Google Gemma-3n-E2B model. Weighted/imatrix quants are available at mradermacher/gemma-3n-E2B-i1-GGUF.

Provided Quants

The quants are sorted by size (not necessarily quality. IQ - quants are often preferable over similar sized non - IQ quants).

Link	Type	Size/GB	Notes
GGUF	Q2_K	2.0
GGUF	Q3_K_S	2.3
GGUF	Q3_K_M	2.4	lower quality
GGUF	Q3_K_L	2.5
GGUF	IQ4_XS	2.7
GGUF	Q4_K_S	2.8	fast, recommended
GGUF	Q4_K_M	2.9	fast, recommended
GGUF	Q5_K_S	3.3
GGUF	Q5_K_M	3.3
GGUF	Q6_K	3.8	very good quality
GGUF	Q8_0	4.9	fast, best quality
GGUF	f16	9.0	16 bpw, overkill

Here is a graph by ikawrakow comparing some lower - quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

For answers to common questions or to request quantization of other models, visit mradermacher/model_requests.

🔧 Technical Details

No detailed technical implementation details are provided in the original README.

📄 License

The model uses the Gemma license.

Thanks

The author thanks nethype GmbH for server access and workstation upgrades, enabling this work in free time.

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