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

huggingface.co
Total runs: 291
24-hour runs: 10
7-day runs: -9
30-day runs: -568
Model's Last Updated: July 23 2026
text-generation

Introduction of gemma-4-E2B-it-MLX-4bit

Model Details of gemma-4-E2B-it-MLX-4bit


Gemma 4 E2B

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.

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
  1. Download google/gemma-4-E2B-it (original weights).
  2. 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.

Runs of AtomicChat gemma-4-E2B-it-MLX-4bit on huggingface.co

291
Total runs
10
24-hour runs
16
3-day runs
-9
7-day runs
-568
30-day runs

More Information About gemma-4-E2B-it-MLX-4bit huggingface.co Model

More gemma-4-E2B-it-MLX-4bit license Visit here:

https://choosealicense.com/licenses/apache-2.0

gemma-4-E2B-it-MLX-4bit huggingface.co

gemma-4-E2B-it-MLX-4bit huggingface.co is an AI model on huggingface.co that provides gemma-4-E2B-it-MLX-4bit's model effect (), which can be used instantly with this AtomicChat gemma-4-E2B-it-MLX-4bit model. huggingface.co supports a free trial of the gemma-4-E2B-it-MLX-4bit model, and also provides paid use of the gemma-4-E2B-it-MLX-4bit. Support call gemma-4-E2B-it-MLX-4bit model through api, including Node.js, Python, http.

gemma-4-E2B-it-MLX-4bit huggingface.co Url

https://huggingface.co/AtomicChat/gemma-4-E2B-it-MLX-4bit

AtomicChat gemma-4-E2B-it-MLX-4bit online free

gemma-4-E2B-it-MLX-4bit huggingface.co is an online trial and call api platform, which integrates gemma-4-E2B-it-MLX-4bit's modeling effects, including api services, and provides a free online trial of gemma-4-E2B-it-MLX-4bit, you can try gemma-4-E2B-it-MLX-4bit online for free by clicking the link below.

AtomicChat gemma-4-E2B-it-MLX-4bit online free url in huggingface.co:

https://huggingface.co/AtomicChat/gemma-4-E2B-it-MLX-4bit

gemma-4-E2B-it-MLX-4bit install

gemma-4-E2B-it-MLX-4bit is an open source model from GitHub that offers a free installation service, and any user can find gemma-4-E2B-it-MLX-4bit on GitHub to install. At the same time, huggingface.co provides the effect of gemma-4-E2B-it-MLX-4bit install, users can directly use gemma-4-E2B-it-MLX-4bit installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

gemma-4-E2B-it-MLX-4bit install url in huggingface.co:

https://huggingface.co/AtomicChat/gemma-4-E2B-it-MLX-4bit

Url of gemma-4-E2B-it-MLX-4bit

gemma-4-E2B-it-MLX-4bit huggingface.co Url

Provider of gemma-4-E2B-it-MLX-4bit huggingface.co

AtomicChat
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