Instructions to use scott156/LEDLargeNSPCCV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scott156/LEDLargeNSPCCV1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("scott156/LEDLargeNSPCCV1") model = AutoModelForMultimodalLM.from_pretrained("scott156/LEDLargeNSPCCV1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28c0ed74766e29466ae6d11a4572807243e0550b0416d667566542bbaaa70b94
- Size of remote file:
- 1.84 GB
- SHA256:
- 3387cc0874521c10dcae79ae15f8f66d2b6591048b3bb0beb6189aaa8583e177
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.