Instructions to use SparseLLM/training-log with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/training-log with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SparseLLM/training-log", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2208bc88370738112f2ebea741069ccdedac369f4d00adfffb71733a9fc76b79
- Size of remote file:
- 88 Bytes
- SHA256:
- 999b51f484bbd7650a4c8d7456b8ae4e544bd1beb3d937e0869acc8f2af86b74
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.