Instructions to use griffin/redress-clinical-hallucination-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use griffin/redress-clinical-hallucination-generator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("griffin/redress-clinical-hallucination-generator") model = AutoModelForSeq2SeqLM.from_pretrained("griffin/redress-clinical-hallucination-generator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6716b07615acc2b1ad6d7057acdf3b949cfd13e2e3025645f6cfa1fca8873041
- Size of remote file:
- 558 MB
- SHA256:
- 48e231e91bfa7703aca68b207a57a139d21ebe9c1c4ccba36aaaa1db25ff1a94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.