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:
- 4aee76999064c57fef2ea64eadd41415aa3cbf10738ca678a2e38e9e03a82e48
- Size of remote file:
- 88 Bytes
- SHA256:
- 3795f7e25416905c2d186d4e24bc9ef422a18d5170c670f53e815edbdd6dfb55
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.