Instructions to use sshleifer/student-bart-base-3-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/student-bart-base-3-3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/student-bart-base-3-3") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/student-bart-base-3-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/student-bart-base-3-3: direct link, hf CLI and curl.
- Browser
- Download file 180 MB
-
https://huggingface.co/sshleifer/student-bart-base-3-3/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/student-bart-base-3-3/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/student-bart-base-3-3/resolve/main/flax_model.msgpack
180 MB
- Xet hash:
- 41129b1b89fef3f6dd405842b9436490a04c7d8a0946929d244244406d2139d1
- Size of remote file:
- 180 MB
- SHA256:
- 9abca096b459261d8b558df51b7fe394a46e6cd1655cec9c66f63d50b502d3eb
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