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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5adf9c7c8a7cc9ae3a410c5fa5c0887e99a2ae1990a9a20cb859d571328d8906
- Size of remote file:
- 1.34 MB
- SHA256:
- 6b44bc87ec818de0f43f0f55ab54a51a8b0f2b3b2abb6a04ba0056284dd66027
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