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gemma-4-E2B-it-int4-foem
Xingyu-Zheng/gemma-4-E2B-it-int4-foem
gemma-4-E2B-it-int4-foem is a image-text-to-text model from Xingyu-Zheng. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
<div align="center" <img src=https://ai.google.dev/gemma/images/gemma4banner.png </div
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From the Hugging Face model README
<div align="center">
<img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
</div>
<div align="left">
<a href=https://ojs.aaai.org/index.php/AAAI/article/view/40123 target="_blank"><img src=https://img.shields.io/badge/Official%20Site-333399.svg?logo=homepage height=22px></a>
<a href=https://huggingface.co/Xingyu-Zheng/gemma-4-E2B-it-int4-foem target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
<a href=https://github.com/Xingyu-Zheng/FOEM target="_blank"><img src= https://img.shields.io/badge/Page-bb8a2e.svg?logo=github height=22px></a>
<a href=https://arxiv.org/abs/2507.11017 target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>
</div>
This is an unofficial quantized version of google/gemma-4-E2B-it.
🧠 Quantization Framework
🗺️ Quantization Method
FOEM is an improved quantization method over GPTQ. The resulting model preserves the same inference structure as GPTQ, ensuring compatibility with existing deployment pipelines while achieving better accuracy.
📚 Calibration Dataset
We randomly sampled 512 examples from nohurry/Opus-4.6-Reasoning-3000x-filtered.
📋 Usage Example
This model can be deployed using standard frameworks such as vLLM and SGLang, just like other GPTQModel-quantized models.
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- gemma4
- License
- apache-2.0
- Created
- Apr 3, 2026
- Updated
- Apr 16, 2026