jastorj/couchmind-v5.7.6.1_arctic_stage_3-cw-19K-16bit
The jastorj/couchmind-v5.7.6.1_arctic_stage_3-cw-19K-16bit model is a 7.6 billion parameter, 32K context length language model fine-tuned by jastorj. It is based on the Snowflake/Arctic-Text2SQL-R1-7B architecture and specifically optimized for Text-to-SQL generation. This model excels at converting natural language questions into syntactically valid SQL++ queries, incorporating code-with-thought reasoning for enhanced accuracy.
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Model Overview
This model, jastorj/couchmind-v5.7.6.1_arctic_stage_3-cw-19K-16bit, is a specialized 7.6 billion parameter language model built upon the Snowflake/Arctic-Text2SQL-R1-7B base architecture. It has been fine-tuned using LoRA (Low-Rank Adaptation) with Unsloth on the NL2SQL++ v5.7.6.1_arctic_stage_3 dataset, which includes code-with-thought reasoning examples. The model's primary function is to accurately translate natural language questions into Couchbase SQL++ queries, leveraging a 32,768 token context window.
Key Capabilities
- Text-to-SQL Generation: Converts natural language questions into precise SQL++ queries.
- Schema Awareness: Utilizes provided database schemas (bucket, scope, collection names, and properties) to generate contextually accurate queries.
- Code-with-Thought Reasoning: Incorporates a reasoning process during generation, as evidenced by its training on datasets designed for this capability.
- Couchbase SQL++ Specific: Optimized for the unique syntax and structure of Couchbase SQL++.
Good for
- Developers needing SQL++ automation: Ideal for applications requiring automated generation of Couchbase SQL++ queries from user input.
- Data analysts: Can assist in quickly formulating complex queries without deep SQL++ expertise.
- Building intelligent data interfaces: Suitable for creating conversational AI or natural language interfaces for Couchbase databases.