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)

## Quant trade-off (MLX lane)

| Bits | Approx size | Use case | Recommendation |
|---|---|---|---|
| 2-bit | ~532 MB | Aggressive quantization | Very low-RAM Macs |
| 3-bit | ~737 MB | Lossy but small | Low-RAM Macs |
| **4-bit** | ~860 MB | Balanced default | **Recommended for most Macs** |
| 5-bit | ~1.0 GB | Higher fidelity | Quality-sensitive |
| 6-bit | ~1.2 GB | Approaching FP16 quality | High-fidelity |
| 8-bit | ~1.5 GB | Near-lossless reference | Fidelity-critical work |

(Current variant — **4bit** — is bolded.)

## Variants in this family

(Showing 18 sibling variants under `majentik/gemma4-e2b-*`. The current variant — `RotorQuant-MLX-4bit` — is **bolded**.)

| Variant | Runtime | Approx size | Use case |
|---|---|---|---|
| [RotorQuant](https://huggingface.co/majentik/gemma4-e2b-rotorquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
| [RotorQuant-AWQ-4bit](https://huggingface.co/majentik/gemma4-e2b-rotorquant-awq-4bit) | transformers | ~1.2 GB | GPU 4-bit (AutoAWQ) |
| [RotorQuant-AWQ-8bit](https://huggingface.co/majentik/gemma4-e2b-rotorquant-awq-8bit) | transformers | ~2.2 GB | GPU 8-bit (AutoAWQ) |
| [RotorQuant-GGUF-IQ4_XS](https://huggingface.co/majentik/gemma4-e2b-rotorquant-gguf-IQ4_XS) | llama.cpp | ~1.7 GB | Lossy 4-bit, low-RAM CPU/edge |
| [RotorQuant-GGUF-Q2_K](https://huggingface.co/majentik/gemma4-e2b-rotorquant-gguf-Q2_K) | llama.cpp | ~1.2 GB | Lossy, low-RAM CPU/edge |
| [RotorQuant-GGUF-Q3_K_M](https://huggingface.co/majentik/gemma4-e2b-rotorquant-gguf-Q3_K_M) | llama.cpp | ~1.6 GB | Smaller 3-bit, CPU-friendly |
| [RotorQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/gemma4-e2b-rotorquant-gguf-Q4_K_M) | llama.cpp | ~2.2 GB | Balanced default |
| [RotorQuant-GGUF-Q5_K_M](https://huggingface.co/majentik/gemma4-e2b-rotorquant-gguf-Q5_K_M) | llama.cpp | ~2.6 GB | Higher fidelity, more RAM |
| [RotorQuant-GGUF-Q8_0](https://huggingface.co/majentik/gemma4-e2b-rotorquant-gguf-Q8_0) | llama.cpp | ~4.2 GB | Near-lossless reference |
| [RotorQuant-MLX-2bit](https://huggingface.co/majentik/gemma4-e2b-rotorquant-mlx-2bit) | mlx-lm | ~655 MB | Apple Silicon, smallest |
| **RotorQuant-MLX-4bit** | mlx-lm | ~1.2 GB | Apple Silicon balanced |
| [RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma4-e2b-rotorquant-mlx-8bit) | mlx-lm | ~2.4 GB | Apple Silicon reference |
| [TurboQuant](https://huggingface.co/majentik/gemma4-e2b-turboquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) |
| [TurboQuant-AWQ-4bit](https://huggingface.co/majentik/gemma4-e2b-turboquant-awq-4bit) | transformers | ~1.2 GB | GPU 4-bit (AutoAWQ) |
| [TurboQuant-AWQ-8bit](https://huggingface.co/majentik/gemma4-e2b-turboquant-awq-8bit) | transformers | ~2.2 GB | GPU 8-bit (AutoAWQ) |
| [TurboQuant-MLX-2bit](https://huggingface.co/majentik/gemma4-e2b-turboquant-mlx-2bit) | mlx-lm | ~655 MB | Apple Silicon, smallest |
| [TurboQuant-MLX-4bit](https://huggingface.co/majentik/gemma4-e2b-turboquant-mlx-4bit) | mlx-lm | ~1.2 GB | Apple Silicon balanced |
| [TurboQuant-MLX-8bit](https://huggingface.co/majentik/gemma4-e2b-turboquant-mlx-8bit) | mlx-lm | ~2.4 GB | Apple Silicon reference |