Instructions to use griffith-bigdata/GRAST-SQL-4B-BIRD-Reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use griffith-bigdata/GRAST-SQL-4B-BIRD-Reranker with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("griffith-bigdata/GRAST-SQL-4B-BIRD-Reranker") model = AutoModelForCausalLM.from_pretrained("griffith-bigdata/GRAST-SQL-4B-BIRD-Reranker") - Notebooks
- Google Colab
- Kaggle
Improve model card with metadata, paper link, and usage example
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding
pipeline_tag: text-rankingandlibrary_name: transformersmetadata. This will enable the "how to use" widget and ensure the model appears in the correct filter on the Hub. - Linking the model to its paper: Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers.
- Including the system flow diagram, links to related datasets and other models.
- Providing sample usage snippets from the GitHub README for applying GRAST-SQL on custom databases.
- Adding the academic citation.
Please review and merge this PR if everything looks good.
thanhdathoang changed pull request status to merged