---
base_model: google/gemma-4-31B-it
library_name: transformers
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
pipeline_tag: image-text-to-text
datasets:
- nohurry/Opus-4.6-Reasoning-3000x-filtered
tags:
  - vLLM
  - SGLang
---

<div align="center">
  <img src=https://ai.google.dev/gemma/images/gemma4_banner.png>
</div>
This is an unofficial quantized version of google/gemma-4-31B-it.

### 🧠 Quantization Framework

[GPTQModel](https://github.com/ModelCloud/GPTQModel)

## 🗺️ Quantization Method

[FOEM (AAAI 2026)](https://ojs.aaai.org/index.php/AAAI/article/view/40123)

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](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered).

## 📋 Usage Example

This model can be deployed using standard frameworks such as **vLLM**, just like other **GPTQModel**-quantized models.