EmbeddingGemma 2 oQ
Collection
18 items โข Updated
How to use bambocher/embeddinggemma-2-oQ3 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download bambocher/embeddinggemma-2-oQ3 --local-dir embeddinggemma-2-oQ3
This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization.
The effective average is 11.625 bits/weight for the complete checkpoint.
| Storage format | Logical weights | Share of total | Tensor storage | Effective bits/weight |
|---|---|---|---|---|
| Affine 3-bit, group 64 | 0.219B | 29.441% | 0.089 GiB | 3.50 |
| Affine 5-bit, group 64 | 0.037B | 5.001% | 0.024 GiB | 5.50 |
| Affine 6-bit, group 64 | 0.004B | 0.563% | 0.003 GiB | 6.50 |
| Affine 8-bit, group 64 | 0.012B | 1.550% | 0.011 GiB | 8.50 |
| BF16 | 0.472B | 63.445% | 0.880 GiB | 16.00 |
| Total | 0.744B | 100% | 1.007 GiB | 11.625 |
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 0.272B | 0.137 GB / 0.128 GiB |
| Vision | 0.167B | 0.335 GB / 0.312 GiB |
| Audio | 0.305B | 0.610 GB / 0.568 GiB |
| Total | 0.744B | 1.082 GB / 1.007 GiB |
3-bit