Instructions to use hf-internal-testing/tiny-random-LongT5ForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LongT5ForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LongT5ForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-internal-testing/tiny-random-LongT5ForConditionalGeneration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3664fb9df0c6c97e92a2a702457696c567baba14176f6c7d6798bccb1a695981
- Size of remote file:
- 4.61 MB
- SHA256:
- af923d1564765d8003fdc4381c157089f14a875e8f7a1ada3e138fccff63b179
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