Instructions to use michaelnath/scrappy_code_to_code_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelnath/scrappy_code_to_code_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("michaelnath/scrappy_code_to_code_model") model = AutoModelForSeq2SeqLM.from_pretrained("michaelnath/scrappy_code_to_code_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c13a0eac6f4df06e28b57fceeec38e516e6a9b75f8ff064b60b42b634690bf4
- Size of remote file:
- 892 MB
- SHA256:
- 1e901b731729c7116c56bda0cc507b05e51c1456d333ee6a57e9ca215c143f8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.