Instructions to use vishal-carvia/flan-t5-small-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vishal-carvia/flan-t5-small-samsum with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vishal-carvia/flan-t5-small-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("vishal-carvia/flan-t5-small-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 934300f992e9c11855dcec1542f5f737e822552667e493bde0bc8bd89e67e40e
- Size of remote file:
- 4.16 kB
- SHA256:
- 4c7632ad6391746f81966f7928b333a502d785b67ec43372dca99a602fa34b05
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