Instructions to use sshleifer/tinier_bart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/tinier_bart with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/tinier_bart") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/tinier_bart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/tinier_bart: direct link, hf CLI and curl.
- Browser
- Download file 5.28 MB
-
https://huggingface.co/sshleifer/tinier_bart/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/tinier_bart/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/tinier_bart/resolve/main/flax_model.msgpack
5.28 MB
- Xet hash:
- 798f6c28f04635e02f5597d2f281585eb96daa692ae10828d81ad961be479656
- Size of remote file:
- 5.28 MB
- SHA256:
- 11d261660860c4905d08cbffcec0abd311d35d477cde1158594b6a35b9042b41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.