---
license: apache-2.0
license_link: https://ai.google.dev/gemma/docs/gemma_4_license
library_name: transformers
pipeline_tag: any-to-any
tags:
  - gemma4
  - gemma
  - google
  - paroquant
base_model:
  - google/gemma-4-E2B-it
---

# Jeethu/gemma-4-E2B-it-PARO

**Pairwise Rotation Quantization for Efficient Reasoning LLM Inference**

<p>
  <a href="https://arxiv.org/abs/2511.10645"><img src="https://img.shields.io/badge/arXiv-2511.10645-b31b1b.svg" alt="Paper"></a>
  <a href="https://paroquant.z-lab.ai"><img src="https://img.shields.io/badge/Blog-ParoQuant-blue" alt="Blog"></a>
  <a href="https://huggingface.co/collections/z-lab/paroquant"><img src="https://img.shields.io/badge/%F0%9F%A4%97-Models-yellow" alt="Models"></a>
  <a href="https://pypi.org/project/paroquant/"><img src="https://img.shields.io/pypi/v/paroquant" alt="PyPI"></a>
</p>

ParoQuant is the state-of-the-art INT4 quantization for LLMs. It closes the accuracy gap with FP16 while running at near-AWQ speed. Supports NVIDIA GPUs (vLLM, Transformers) and Apple Silicon (MLX). For more information, see https://github.com/z-lab/paroquant.

Jeethu/gemma-4-E2B-it-PARO is a 4-bit [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) quantized with ParoQuant.