Instructions to use NazzX1/LED-note-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NazzX1/LED-note-1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NazzX1/LED-note-1") model = AutoModelForSeq2SeqLM.from_pretrained("NazzX1/LED-note-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from NazzX1/LED-note-1: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/NazzX1/LED-note-1/resolve/main/training_args.bin
- Command line
-
hf download hf://NazzX1/LED-note-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/NazzX1/LED-note-1/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- ba53b74da8d084267dccf688d41e9827d8e04d6db1727cbb3736a679e4e71f50
- Size of remote file:
- 5.5 kB
- SHA256:
- 5112c923f600d0eaed3cf83cd7e5d4a9cddb2d1515cd6753e73f7551d760a098
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