Instructions to use BM-K/KoChatBART with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BM-K/KoChatBART with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("BM-K/KoChatBART") model = AutoModelForSeq2SeqLM.from_pretrained("BM-K/KoChatBART") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1db101679f14aedb160e8bfd5d851cbcfb684810079c4425b35da45e635dc08a
- Size of remote file:
- 558 MB
- SHA256:
- 15e950861ee2aa400021cf879e966b6fdf7f1abd626a1f5a11daf3b364537e46
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.