Instructions to use brainworkup/LFM2.5-2.6B-oQ8e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use brainworkup/LFM2.5-2.6B-oQ8e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download brainworkup/LFM2.5-2.6B-oQ8e --local-dir LFM2.5-2.6B-oQ8e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
LFM2.5-2.6B-oQ8e
This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization.
The effective average is 8.501 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 | 2.697B | 99.990% | 2.669 GiB | 8.50 |
| BF16 | 0.000B | 0.010% | 0.000 GiB | 16.00 |
| Total | 2.697B | 100% | 2.669 GiB | 8.501 |
Storage Breakdown
| Component | Logical weights | Storage |
|---|---|---|
| Language model | 2.697B | 2.866 GB / 2.669 GiB |
| Total | 2.697B | 2.866 GB / 2.669 GiB |
- Downloads last month
- 56
Model size
3B params
Tensor type
U32
ยท
BF16 ยท
Hardware compatibility
Log In to add your hardware
8-bit
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support