# Text-to-SQL

Prem-1B-SQL is a 1-billion-parameter text-to-SQL model developed by Prem AI, designed for local deployment and supports running on low-end GPU and CPU devices.

A text-to-SQL generation language model specifically designed for Postgres, Redshift, and Snowflake databases, with performance comparable to cutting-edge general-purpose models.

A Mistral-7B fine-tuned text-to-SQL model that converts natural language queries into SQL statements

A text-to-SQL model distilled from Llama 1B, outperforming ChatGPT and Claude in multiple benchmarks

Natural-SQL-7B is a text-to-SQL model developed by ChatDB.ai, fine-tuned based on Deepseek-coder-6.7b-instruct, excelling in parsing complex questions to generate SQL queries

A 7B-parameter Llama 2 fine-tuned text-to-SQL model that converts natural language questions into SQL queries

A Text-to-SQL model optimized based on the CodeLLaMa 13B architecture, fine-tuned with 100,000 SQL generation instructions to achieve efficient conversion from natural language to SQL queries

A lightweight model fine-tuned on T5-small that generates SQL queries from natural language with table structures, supporting multi-table join operations.

CodeS-7B is a large language model optimized for SQL generation, based on incremental pre-training of StarCoderBase-7B, supporting a maximum context length of 8,192 tokens.

This is an upgraded text-to-SQL conversion model based on the FLAN-T5 architecture, specifically designed to convert natural language questions into SQL queries.

NSQL-6B is an autoregressive large language model base specifically designed for SQL generation tasks, pre-trained and fine-tuned based on Salesforce's CodeGen-Multi 6B model.

This is a text-to-SQL translation model trained via AutoTrain, capable of converting natural language queries into SQL statements.

This is a Flan-T5 based text-to-SQL conversion model that supports generating SQL queries from natural language questions and table schemas.

A fine-tuned text-to-SQL conversion model based on T5-3B architecture, significantly improving structured query generation accuracy through PICARD constrained decoding