Instructions to use Grunzig/gemma-4-12B-it-qat-oQ4e-fp16-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Grunzig/gemma-4-12B-it-qat-oQ4e-fp16-mtp with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gemma-4-12B-it-qat-oQ4e-fp16-mtp Grunzig/gemma-4-12B-it-qat-oQ4e-fp16-mtp
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
gemma-4-12B-it-qat-oQ4e-fp16-mtp
This model was quantized using oQ (oMLX v0.6.3rc1) mixed-precision quantization.
google/gemma-4-12B-it-qat-q4_0-unquantized-assistant MTP head was merged into google/gemma-4-12B-it-qat-q4_0-unquantized.
It is optimized for Mac M1/M2 (fp16).
Quantization details
- Model type: gemma4_unified
- Bits: 4
- Group size: 64
- Format: MLX safetensors
- Non-quant weight dtype: FP16
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Model size
2B params
Tensor type
F16
·
U32 ·
F32 ·
BF16 ·
Hardware compatibility
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4-bit
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Model tree for Grunzig/gemma-4-12B-it-qat-oQ4e-fp16-mtp
Base model
google/gemma-4-12B Finetuned
google/gemma-4-12B-it