Instructions to use EEsu/codeT5-adam-trial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EEsu/codeT5-adam-trial with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("EEsu/codeT5-adam-trial") model = AutoModelForSeq2SeqLM.from_pretrained("EEsu/codeT5-adam-trial", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from EEsu/codeT5-adam-trial: direct link, hf CLI and curl.
- Browser
- Download file 121 MB
-
https://huggingface.co/EEsu/codeT5-adam-trial/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://EEsu/codeT5-adam-trial/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/EEsu/codeT5-adam-trial/resolve/main/last-checkpoint/optimizer.pt
121 MB
- Xet hash:
- 2e55a0d3e90095305babd220931587e6e8be09fca79b2d418198cb880a5104ab
- Size of remote file:
- 121 MB
- SHA256:
- afd1f4d76a2addd80e3181d89997df1a58f9ffb4b1c52e20f08010d329c6795b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.