Instructions to use vmkhoa2000/optimized_fine_tuning_results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vmkhoa2000/optimized_fine_tuning_results with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vmkhoa2000/optimized_fine_tuning_results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vmkhoa2000/optimized_fine_tuning_results: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/vmkhoa2000/optimized_fine_tuning_results/resolve/main/training_args.bin
- Command line
-
hf download hf://vmkhoa2000/optimized_fine_tuning_results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vmkhoa2000/optimized_fine_tuning_results/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 7edc5cb46b70c9608b65e2cd6bd4c0d1fcbbf7585bc9c28d6e1b9ced2bd52a59
- Size of remote file:
- 5.11 kB
- SHA256:
- 9122e5532c79f9214bee0a0d00498c27b23c39370e1e82b54843b1b8b51c97a8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.