Instructions to use g-oQ/clef-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use g-oQ/clef-oQ8e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download g-oQ/clef-oQ8e --local-dir clef-oQ8e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
clef-oQ8e
This model was quantized using oQ (oMLX v0.7.1rc1) mixed-precision quantization.
The effective average is 8.661 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 | 26.893B | 97.848% | 26.612 GiB | 8.50 |
| BF16 | 0.591B | 2.152% | 1.102 GiB | 16.00 |
| Total | 27.485B | 100% | 27.713 GiB | 8.661 |
Storage Breakdown
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 26.896B | 28.579 GB / 26.617 GiB |
| Vision | 0.461B | 0.921 GB / 0.858 GiB |
| Joint schema head | 0.128B | 0.256 GB / 0.239 GiB |
| Total | 27.485B | 29.757 GB / 27.713 GiB |
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Model size
27B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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8-bit
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