Instructions to use schokoro/ruadapt-simplification-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schokoro/ruadapt-simplification-lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("schokoro/ruadapt-simplification-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from schokoro/ruadapt-simplification-lora: direct link, hf CLI and curl.
- Browser
- Download file 12.4 MB
-
https://huggingface.co/schokoro/ruadapt-simplification-lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://schokoro/ruadapt-simplification-lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/schokoro/ruadapt-simplification-lora/resolve/main/tokenizer.json
12.4 MB
- Xet hash:
- 7f1781190c25892fae9aace1769378a5a7fcb682f76ca9b79b9b3bd1455bd2aa
- Size of remote file:
- 12.4 MB
- SHA256:
- 5408c0e76d30d5f9348593818dcaafdd45dbe64ba19fcd5fb92c2677c15f4cf1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.