Instructions to use brainworkup/d1-Omni-2B-MLX-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use brainworkup/d1-Omni-2B-MLX-oQ8e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download brainworkup/d1-Omni-2B-MLX-oQ8e --local-dir d1-Omni-2B-MLX-oQ8e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
d1-Omni-2B-MLX-oQ8e
This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization.
The effective average is 9.536 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 | 1.868B | 86.187% | 1.848 GiB | 8.50 |
| BF16 | 0.299B | 13.813% | 0.558 GiB | 16.00 |
| Total | 2.167B | 100% | 2.406 GiB | 9.536 |
Storage Breakdown
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 1.868B | 1.985 GB / 1.849 GiB |
| Vision | 0.189B | 0.379 GB / 0.353 GiB |
| Audio | 0.110B | 0.219 GB / 0.204 GiB |
| Total | 2.167B | 2.583 GB / 2.406 GiB |
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Model size
2B params
Tensor type
BF16
ยท
U32 ยท
Hardware compatibility
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8-bit
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