czurita/nsql-llama-2-7B-sharded-bf16-2GB
The czurita/nsql-llama-2-7B-sharded-bf16-2GB model is a 7 billion parameter Llama-2 based autoregressive language model, developed by NumbersStation, specifically optimized for text-to-SQL generation tasks. This sharded version is designed for low-RAM environments, making it accessible for platforms like Colab or Kaggle. It excels at converting natural language prompts and table schemas into SQL queries, particularly for generating SELECT statements.
Loading preview...
NSQL-Llama-2-7B Sharded Version
This model, czurita/nsql-llama-2-7B-sharded-bf16-2GB, is a sharded, bfloat16 version of the original NumbersStation/nsql-llama-2-7B model. It is specifically reconfigured for environments with limited RAM, such as Google Colab or Kaggle, while retaining its core capabilities.
Key Capabilities
- SQL Generation: Designed to convert natural language questions and provided database schemas into SQL queries.
- Llama-2 Base: Built upon Meta's Llama-2 7B architecture, providing a strong foundation for language understanding.
- Specialized Training: Pre-trained on 1 million SQL queries from The Stack and fine-tuned on over 20 public text-to-SQL datasets.
- Evaluation: Performance is evaluated on standard text-to-SQL benchmarks like Spider and GeoQuery.
- Optimized Output: Best suited for generating
SELECTqueries based on a defined prompt format.
Good For
- Text-to-SQL Applications: Ideal for developers building applications that require converting natural language into executable SQL.
- Low-Resource Environments: Its sharded, bfloat16 format makes it suitable for deployment and experimentation in environments with constrained memory.
- Database Interaction: Facilitating easier interaction with databases by automating SQL query generation from user prompts.