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AtomicChat/
gemma-4-E2B-it-MLX-4bit

Name: gemma-4-E2B-it-MLX-4bit
Availability: InStock

google/gemma-4-E2B-it

quantized

apache-2.0

4-bit

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Base model: google/gemma-4-E2B-it

Gemma 4 E2B, self-quantized to MLX by Atomic Chat. Built straight from Google's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

2.3B effective (5.1B with embeddings) parameters: the weights this repo quantizes.
Context length: 128K tokens, as published by Google.
35 layers: Dense decoder, hybrid sliding-window (512) and global attention.
Modalities: Text, Image, Audio.
Full imatrix ladder: every quant is calibrated with an importance matrix.
Reasoning: All models in the family are designed as highly capable reasoners, with configurable thinking modes.
Diverse & Efficient Architectures: Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.

[!NOTE] These MLXs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

Model Overview

Property	Value
Base model	`google/gemma-4-E2B-it`
Parameters	2.3B effective (5.1B with embeddings)
Layers	35
Sliding window	512 tokens
Context length	128K tokens
Vocabulary	262K
Modalities	Text, Image, Audio
Architecture	Dense decoder, hybrid sliding-window (512) and global attention, 8 attention heads over 1 KV head, `Gemma4ForConditionalGeneration`
This repo	MLX weights

Benchmarks

Benchmark	Score
MMLU Pro	60.0%
AIME 2026 no tools	37.5%
LiveCodeBench v6	44.0%
Codeforces ELO	633
GPQA Diamond	43.4%
Tau2 (average over 3)	24.5%
BigBench Extra Hard	21.9%
MMMLU	67.4%
MMMU Pro	44.2%
OmniDocBench 1.5 (average edit distance, lower is better)	0.290
MATH-Vision	52.4%
MedXPertQA MM	23.5%
CoVoST	33.47
FLEURS (lower is better)	0.09
MRCR v2 8 needle 128k (average)	19.1%

Scores are Google's published results for the base google/gemma-4-E2B-it, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Get started

Atomic Chat: search AtomicChat/gemma-4-E2B-it-MLX-4bit and hit Use this model.
mlx-lm: mlx_lm.generate --model AtomicChat/gemma-4-E2B-it-MLX-4bit --prompt "Hello" --max-tokens 512
Server: mlx_lm.server --model AtomicChat/gemma-4-E2B-it-MLX-4bit --port 8080

Best practices

Parameter	Value
temperature	1.0
top_p	0.95
top_k	64

Google's recommended sampling configuration for google/gemma-4-E2B-it.

How these were made

Download google/gemma-4-E2B-it (original weights).
Convert and quantize with mlx_lm.convert on our pipeline.

License

Original model by Google, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.

AtomicChat/gemma-4-E2B-it-MLX-4bit