Instructions to use imxly/t5-pegasus-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imxly/t5-pegasus-small with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("imxly/t5-pegasus-small") model = AutoModelForSeq2SeqLM.from_pretrained("imxly/t5-pegasus-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from imxly/t5-pegasus-small: direct link, hf CLI and curl.
- Browser
- Download file 381 MB
-
https://huggingface.co/imxly/t5-pegasus-small/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://imxly/t5-pegasus-small/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/imxly/t5-pegasus-small/resolve/main/flax_model.msgpack
381 MB
- Xet hash:
- 63c5a66b627730a6c693c060c812bccc07b1e3bcf4489ccc9f97a5a5d35d7920
- Size of remote file:
- 381 MB
- SHA256:
- 8897a4aca777557958a3fec833257b8a681e2a2697c86268b8897616d4000d25
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.