README.md

---
base_model: google/gemma-4-E2B
library_name: mlx
tags:
  - rotorquant
  - kv-cache-quantization
  - gemma
  - gemma4
  - multimodal
  - quantized
  - mlx
  - 4bit
license: apache-2.0
pipeline_tag: image-text-to-text
---

# Gemma 4 E2B - RotorQuant MLX 4-bit

**4-bit weight-quantized MLX version** of [google/gemma-4-E2B](https://huggingface.co/google/gemma-4-E2B) with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. RotorQuant delivers 5.3x faster prefill and 28% faster decode compared to TurboQuant. A good balance between model quality and memory efficiency.

Approximate model size: **~1.2 GB**

## Model Specifications

| Property | Value |
|---|---|
| **Base Model** | [google/gemma-4-E2B](https://huggingface.co/google/gemma-4-E2B) |
| **Parameters** | ~2 billion |
| **Architecture** | Dense transformer |
| **Modality** | Multimodal: image + text input, text output |
| **License** | Apache 2.0 |
| **Weight Quantization** | 4-bit (~1.2 GB) |
| **KV-Cache Quantization** | RotorQuant |
| **Framework** | MLX (Apple Silicon) |

## Quickstart

```python
import mlx.core as mx
from mlx_lm import load, generate

model, tokenizer = load("majentik/gemma-4-E2B-RotorQuant-MLX-4bit")

prompt = "The history of artificial intelligence began"
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)
```

For multimodal usage with images:

```python
from mlx_vlm import load, generate

model, processor = load("majentik/gemma-4-E2B-RotorQuant-MLX-4bit")

prompt = "Describe the contents of this image."
output = generate(model, processor, prompt=prompt, image="path/to/image.jpg", max_tokens=512)
print(output)
```

## What is RotorQuant?

[RotorQuant](https://github.com/scrya-com/rotorquant) is a high-performance KV-cache quantization method that achieves significantly better throughput than TurboQuant. Combined with 4-bit weight quantization in MLX, this provides a dual compression strategy with superior KV-cache performance: smaller model weights plus faster compressed KV cache for efficient long-context generation.

Key advantages over TurboQuant:
- **5.3x faster prefill**
- **28% faster decode**
- Equivalent memory savings

## KV-Cache Quantization Comparison

| Method | Prefill Speed | Decode Speed | Memory Savings | Reference |
|---|---|---|---|---|
| **TurboQuant** | 1x (baseline) | 1x (baseline) | High | [arXiv: 2504.19874](https://arxiv.org/abs/2504.19874) |
| **RotorQuant** | **5.3x faster** | **28% faster** | High | [GitHub](https://github.com/scrya-com/rotorquant) |

## Memory Estimates (Gemma 4 E2B)

| Precision | Approximate Size | MLX Variant |
|---|---|---|
| FP16 (original) | ~4 GB | -- |
| 8-bit quantized | ~2 GB | [RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-E2B-RotorQuant-MLX-8bit) |
| **4-bit quantized** | **~1.2 GB** | **This model** |
| 2-bit quantized | ~0.6 GB | [RotorQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-E2B-RotorQuant-MLX-2bit) |

## Hardware Requirements

This model requires approximately 1.2 GB of unified memory. Recommended hardware:
- Apple M1 (8 GB+)
- Apple M2 (8 GB+)
- Apple M3 (8 GB+)
- Apple M4 (8 GB+)
- Any Apple Silicon Mac with 8 GB+ unified memory

## See Also

- [google/gemma-4-E2B](https://huggingface.co/google/gemma-4-E2B) -- Base model
- [majentik/gemma-4-E2B-RotorQuant](https://huggingface.co/majentik/gemma-4-E2B-RotorQuant) -- RotorQuant KV-cache only (transformers)
- [majentik/gemma-4-E2B-RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-E2B-RotorQuant-MLX-8bit) -- MLX 8-bit variant
- [majentik/gemma-4-E2B-RotorQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-E2B-RotorQuant-MLX-2bit) -- MLX 2-bit variant
- [majentik/gemma-4-E2B-TurboQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-E2B-TurboQuant-MLX-4bit) -- TurboQuant MLX 4-bit variant
- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)
- [MLX Framework](https://github.com/ml-explore/mlx)