Instructions to use zviratko/embeddinggemma-2-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use zviratko/embeddinggemma-2-oQ8e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download zviratko/embeddinggemma-2-oQ8e --local-dir embeddinggemma-2-oQ8e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
embeddinggemma-2-oQ8e
This model was quantized using oQ (oMLX v0.7.1.dev1) mixed-precision quantization.
The effective average is 13.258 bits/weight for the complete checkpoint.
Quantization and Bit Distribution
| Storage format | Logical weights | Share of total | Tensor storage | Effective bits/weight |
|---|---|---|---|---|
| Affine 8-bit, group 64 | 0.272B | 36.555% | 0.269 GiB | 8.50 |
| BF16 | 0.472B | 63.445% | 0.880 GiB | 16.00 |
| Total | 0.744B | 100% | 1.149 GiB | 13.258 |
Storage Breakdown
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 0.272B | 0.289 GB / 0.269 GiB |
| Vision | 0.167B | 0.335 GB / 0.312 GiB |
| Audio | 0.305B | 0.610 GB / 0.568 GiB |
| Total | 0.744B | 1.234 GB / 1.149 GiB |
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Model size
0.7B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
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Model tree for zviratko/embeddinggemma-2-oQ8e
Base model
google/embeddinggemma-2