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:
- 9fb9c5ab37c75a9fcd522e270a5ae2d221803e7f5454ed7b719e909d0465929e
- Size of remote file:
- 1.25 MB
- SHA256:
- e13eada4dbd3f5c27c019db9e2303cfaea5cd238fb1e6dd595454d47731345ac
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