Instructions to use hf-tiny-model-private/tiny-random-LongT5ForConditionalGeneration 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-LongT5ForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongT5ForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-tiny-model-private/tiny-random-LongT5ForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff01974cb1dfe0e7d378882137992b389cb516e7b4d6950a9518d4094e6c3a08
- Size of remote file:
- 4.49 MB
- SHA256:
- 479762cb10eab2f8f1cd5ebf07bfefcbd06682195a593e2517a355e95ef025ef
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