Instructions to use Suva/query_builder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Suva/query_builder with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Suva/query_builder") model = AutoModelForSeq2SeqLM.from_pretrained("Suva/query_builder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d9354370a6994a4daf963e48bc2ce3b933ec1c584deb80b81d7a5bfe37b4753
- Size of remote file:
- 242 MB
- SHA256:
- d787605bcfa9135626227ae693fcf07603763bc61609336d78b437bb16143ac3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.