Instructions to use scaryrawr/Flint-oQ6e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use scaryrawr/Flint-oQ6e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download scaryrawr/Flint-oQ6e --local-dir Flint-oQ6e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Flint-oQ6e
This model was quantized using oQ (oMLX v0.7.2) mixed-precision quantization.
The effective average is 6.556 bits/weight for the complete checkpoint.
Quantization and Bit Distribution
| Storage format | Logical weights | Share of total | Tensor storage | Effective bits/weight |
|---|---|---|---|---|
| Affine 6-bit, group 64 | 38.736B | 97.364% | 29.312 GiB | 6.50 |
| Affine 8-bit, group 128 | 0.296B | 0.743% | 0.284 GiB | 8.25 |
| Affine 8-bit, group 64 | 0.728B | 1.830% | 0.721 GiB | 8.50 |
| BF16 | 0.025B | 0.063% | 0.046 GiB | 16.00 |
| Total | 39.785B | 100% | 30.363 GiB | 6.556 |
Storage Breakdown
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 39.785B | 32.602 GB / 30.363 GiB |
| Total | 39.785B | 32.602 GB / 30.363 GiB |
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Model size
40B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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6-bit
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