Instructions to use efederici/it5-small-lfqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efederici/it5-small-lfqa with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("efederici/it5-small-lfqa") model = AutoModelForSeq2SeqLM.from_pretrained("efederici/it5-small-lfqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from efederici/it5-small-lfqa: direct link, hf CLI and curl.
- Browser
- Download file 308 MB
-
https://huggingface.co/efederici/it5-small-lfqa/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://efederici/it5-small-lfqa/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/efederici/it5-small-lfqa/resolve/main/flax_model.msgpack
308 MB
- Xet hash:
- 5ad8328383d0bcd604d50a0d14717cb4678c6bfd15c2adbcd99e0f45f9733be9
- Size of remote file:
- 308 MB
- SHA256:
- b6b4b6faa548a73f17ad46a3db14b4b68eaab613ff87fbc15c1f25acb765edfb
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