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gemma-4-26B-A4B-it-MLX-8bit
AtomicChat/gemma-4-26B-A4B-it-MLX-8bit
gemma-4-26B-A4B-it-MLX-8bit is a text generation model from AtomicChat. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
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25.2B
26.8 GB on disk
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.safetensors26.8 GB · 100%
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How the weights are stored.
U3225.2B · 100%
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Base models
Model card
From the Hugging Face model README
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<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-26B-A4B-it-MLX-8bit/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>
<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-26B-A4B-it-MLX-8bit/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>
<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/gemma-4-26B-A4B-it-MLX-8bit/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>
</div>
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<img src="https://huggingface.co/AtomicChat/gemma-4-26B-A4B-it-MLX-8bit/resolve/main/hero.png" alt="Gemma 4 26B A4B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>
<div style="display:flex; justify-content:center; gap:0.5em;">
<a href="https://huggingface.co/google/gemma-4-26B-A4B-it"><strong>Base model: google/gemma-4-26B-A4B-it</strong></a>
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Gemma 4 26B A4B, 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
- 25.2B total / 3.8B active per token parameters: the weights this repo quantizes.
- Context length: 256K tokens, as published by Google.
- 30 layers: Mixture-of-Experts, hybrid sliding-window (1024) and global attention.
- Modalities: Text, Image.
- 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-4-26B-A4B-it |
| Parameters | 25.2B total / 3.8B active per token |
| Layers | 30 |
| Experts | 128 routed (top-8) |
| Sliding window | 1024 tokens |
| Context length | 256K tokens |
| Vocabulary | 262K |
| Modalities | Text, Image |
| Architecture | Mixture-of-Experts, 128 experts (top-8), hybrid sliding-window (1024) and global attention, 16 attention heads over 8 KV heads, Gemma4ForConditionalGeneration |
| This repo | MLX weights |
Benchmarks
| Benchmark | Score |
|---|---|
| MMLU Pro | 82.6% |
| AIME 2026 no tools | 88.3% |
| LiveCodeBench v6 | 77.1% |
| Codeforces ELO | 1718 |
| GPQA Diamond | 82.3% |
| Tau2 (average over 3) | 68.2% |
| HLE no tools | 8.7% |
| HLE with search | 17.2% |
| BigBench Extra Hard | 64.8% |
| MMMLU | 86.3% |
| MMMU Pro | 73.8% |
| OmniDocBench 1.5 (average edit distance, lower is better) | 0.149 |
| MATH-Vision | 82.4% |
| MedXPertQA MM | 58.1% |
| MRCR v2 8 needle 128k (average) | 44.1% |
Scores are Google's published results for the base google/gemma-4-26B-A4B-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-4-26B-A4B-it-MLX-8bitand hit Use this model. - mlx-lm:
mlx_lm.generate --model AtomicChat/gemma-4-26B-A4B-it-MLX-8bit --prompt "Hello" --max-tokens 512 - Server:
mlx_lm.server --model AtomicChat/gemma-4-26B-A4B-it-MLX-8bit --port 8080
Best practices
| Parameter | Value |
|---|---|
| temperature | 1.0 |
| top_p | 0.95 |
| top_k | 64 |
Google's recommended sampling configuration for google/gemma-4-26B-A4B-it.
How these were made
- Download
google/gemma-4-26B-A4B-it(original weights). - Convert and quantize with
mlx_lm.converton our pipeline.
License
Original model by Google, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.
- Task
- Text Generation
- Library
- mlx
- Type
- gemma4
- License
- apache-2.0
- Created
- Jul 22, 2026
- Updated
- Jul 22, 2026