Instructions to use Chirayu/nl2kql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chirayu/nl2kql with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Chirayu/nl2kql") model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/nl2kql", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Chirayu/nl2kql: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/Chirayu/nl2kql/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Chirayu/nl2kql@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Chirayu/nl2kql/resolve/refs%2Fpr%2F1/pytorch_model.bin
892 MB
- Xet hash:
- 19a10f914cd0e6d405bb80a8a75acbb0a2d1415a052b8cd49d32d640cd502d1a
- Size of remote file:
- 892 MB
- SHA256:
- 308928dcedce05616db872533b8dafbd6de84ef9c754d469c5ca2ee06d6407db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.