Gemma 4 12B, 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
- 11.95B parameters: the weights this repo quantizes.
- Context length: 256K tokens, as published by Google.
- 48 layers: Dense decoder, hybrid sliding-window (1024) 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
Benchmarks
Scores are Google's published results for the base google/gemma-4-12B-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-4-12b-it-MLX-4bit and hit Use this model.
- mlx-lm:
mlx_lm.generate --model AtomicChat/gemma-4-12b-it-MLX-4bit --prompt "Hello" --max-tokens 512
- Server:
mlx_lm.server --model AtomicChat/gemma-4-12b-it-MLX-4bit --port 8080
Best practices
Google's recommended sampling configuration for google/gemma-4-12B-it.
How these were made
- Download
google/gemma-4-12B-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.