Instructions to use kktoto/tiny_lr_kk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kktoto/tiny_lr_kk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kktoto/tiny_lr_kk")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kktoto/tiny_lr_kk") model = AutoModelForTokenClassification.from_pretrained("kktoto/tiny_lr_kk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kktoto/tiny_lr_kk: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/kktoto/tiny_lr_kk/resolve/main/training_args.bin
- Command line
-
hf download hf://kktoto/tiny_lr_kk/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kktoto/tiny_lr_kk/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- 32aa6536a878d5ec19560ca9c7994ed1e999ff65dc83cf6a879781fa40c757b9
- Size of remote file:
- 3.18 kB
- SHA256:
- bffdaab7c2ecbef46bbd3c0cd2362332842147d90b9cc0f1dc1fb741570890b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.