Instructions to use hf-tiny-model-private/tiny-random-LEDForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LEDForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LEDForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-tiny-model-private/tiny-random-LEDForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c69b6125ade9a1b403deaa8bf41abd2171a18c03148e6c60c830661f6ecd59fd
- Size of remote file:
- 1.23 MB
- SHA256:
- fffe40e21dc040fe93d58963a552345ebafe861325ff9b662b09031cc40b9d81
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