Instructions to use Pranavz/lfm2-capy-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pranavz/lfm2-capy-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Pranavz/lfm2-capy-lora", device_map="auto") - PEFT
How to use Pranavz/lfm2-capy-lora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c5c9fe9803a49850b42acfe61fbe420bf7b478ca172712a088d9f493b75ba62
- Size of remote file:
- 5.65 kB
- SHA256:
- dbf46869ef3d104ddec4121e456cf5faf6cc5551f5091d3ee5c8b655b18faf22
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.