Instructions to use rjaccard/start2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjaccard/start2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rjaccard/start2") model = AutoModelForSeq2SeqLM.from_pretrained("rjaccard/start2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 113f19069aaaaaa08848c11c7d6b15983f09643bf90e44c641bf2a36880e74bc
- Size of remote file:
- 6.27 GB
- SHA256:
- 143023457f9d8ba4b20ce0c7e83e552cd53734dc18e534de836e43d8dde94015
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.