Instructions to use rdj-034/lab1_random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdj-034/lab1_random with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rdj-034/lab1_random") model = AutoModelForSeq2SeqLM.from_pretrained("rdj-034/lab1_random", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d87a7ed0a245912f3119fb11281c81073b22e1174819fe594d7b2520a35f37d
- Size of remote file:
- 5.97 kB
- SHA256:
- e3f2d96890f5da1b4d9287c31c838cd08a05920bd4e56fc40c4dfdc409ad0963
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.