Instructions to use silmi224/led-risalah_data_v12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/led-risalah_data_v12 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/led-risalah_data_v12") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/led-risalah_data_v12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmi224/led-risalah_data_v12: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/silmi224/led-risalah_data_v12/resolve/main/training_args.bin
- Command line
-
hf download hf://silmi224/led-risalah_data_v12/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmi224/led-risalah_data_v12/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 03552735ea47c011f45a0f66c1cdfedb41688e8184c9743bc98b0ec8ab36b212
- Size of remote file:
- 5.24 kB
- SHA256:
- 62ffe669d7d6caf3e9b9e4df62b7ea81b6259d0ec278c883a090af7eee224bc2
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