Instructions to use hf-internal-testing/tiny-random-LEDForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LEDForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LEDForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-LEDForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 885fe8beaa8d362575b44aa68675ed3e54170f3dc87d250e6348d0b9c3880809
- Size of remote file:
- 1.23 MB
- SHA256:
- a0ae9b1db57609912433f420d25d837e3794c577ff0ed6c3b83fad928cecd027
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