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:
- 9c81de53ca15cfe375263775818ecd78552eacc22a82b6adc3c87826e24ae04f
- Size of remote file:
- 88 Bytes
- SHA256:
- 90b795bc3c592b0b1bcd6960f357ff650907815303a2da065865502903602ac2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.