Instructions to use ernie-research/ernie-code-560m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ernie-research/ernie-code-560m with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ernie-research/ernie-code-560m") model = AutoModelForSeq2SeqLM.from_pretrained("ernie-research/ernie-code-560m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cf9d1cfb9fcdd232c72d3a9b3c2757396fe25a437c7d733239e5e76a830b1fb
- Size of remote file:
- 1.16 GB
- SHA256:
- 873d46d06c3c2419f1aebe22205d71a01198fa7a3248ccaa917d8c948413bdeb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.