Instructions to use brainworkup/LFM3-1.7B-MLX-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use brainworkup/LFM3-1.7B-MLX-oQ8e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download brainworkup/LFM3-1.7B-MLX-oQ8e --local-dir LFM3-1.7B-MLX-oQ8e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
LFM3-1.7B-MLX-oQ8e
This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization.
The effective average is 8.502 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 | 1.728B | 99.978% | 1.710 GiB | 8.50 |
| BF16 | 0.000B | 0.022% | 0.001 GiB | 16.00 |
| Total | 1.729B | 100% | 1.711 GiB | 8.502 |
Storage Breakdown
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 1.729B | 1.837 GB / 1.711 GiB |
| Total | 1.729B | 1.837 GB / 1.711 GiB |
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Model size
2B params
Tensor type
U32
ยท
BF16 ยท
Hardware compatibility
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8-bit
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