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Note — quantized via mlx-lm withmodel_typeset togemma4. The source declaresmodel_type: gemma4_unified(Gemma4UnifiedForConditionalGeneration), which current mlx-lm does not map; it was converted by settingmodel_typetogemma4(text configgemma4_text), which loads on mlx-lm's Gemma 4 path. Any vision/multimodal weights from the unified checkpoint are not included — this is a text-only MLX model. Post-quantization generation was sanity-checked (correct arithmetic + reasoning, stops on EOS).
| This model | FP16 baseline | |
|---|---|---|
| Peak memory (GB) | N/A | N/A |
| Disk size (MB) | 11742 | 22842 |
pip install mlx-lm1from mlx_lm import load, generate
2
3model, tokenizer = load("sahilchachra/gemma-4-12B-coder-fable5-composer2.5-v1-mxfp8-mlx")
4response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256, verbose=True)| Model | Variant |
|---|---|
| sahilchachra/gemma-4-12B-coder-fable5-composer2.5-v1-mxfp4-mlx | Block float MX FP4 |
| sahilchachra/gemma-4-12B-coder-fable5-composer2.5-v1-mxfp8-mlx | Block float MX FP8 ← this model |