Instructions to use scott156/LEDBaseNSPCCV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scott156/LEDBaseNSPCCV1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("scott156/LEDBaseNSPCCV1") model = AutoModelForSeq2SeqLM.from_pretrained("scott156/LEDBaseNSPCCV1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from scott156/LEDBaseNSPCCV1: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/scott156/LEDBaseNSPCCV1/resolve/main/training_args.bin
- Command line
-
hf download hf://scott156/LEDBaseNSPCCV1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/scott156/LEDBaseNSPCCV1/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 3d4a3a946ebaec7b04442a6850484416afb4c75c5137306ddef303cf7393ce0f
- Size of remote file:
- 5.05 kB
- SHA256:
- 62a395ac6b59ae52a4797c20edb38f86341044f767f250d1e6847ee0026d7b69
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