Instructions to use marlex7/straightforwardKD-Mini-from-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marlex7/straightforwardKD-Mini-from-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marlex7/straightforwardKD-Mini-from-Base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marlex7/straightforwardKD-Mini-from-Base") model = AutoModelForSequenceClassification.from_pretrained("marlex7/straightforwardKD-Mini-from-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from marlex7/straightforwardKD-Mini-from-Base: direct link, hf CLI and curl.
- Browser
- Download file 4.79 kB
-
https://huggingface.co/marlex7/straightforwardKD-Mini-from-Base/resolve/main/training_args.bin
- Command line
-
hf download hf://marlex7/straightforwardKD-Mini-from-Base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/marlex7/straightforwardKD-Mini-from-Base/resolve/main/training_args.bin
4.79 kB
- Xet hash:
- 921a91e069711e036f7f99e67f85f9969b9bc0e0f805d8ee5fed73daa70f7955
- Size of remote file:
- 4.79 kB
- SHA256:
- 78e5c927b9029920d536d4351f7c79e8c132804d6f01ce039609935d977aceab
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.