Defog.ai has released SQLCoder, a cutting-edge model for translating inquiries in natural language into database queries. Regarding generic SQL schemas in Postgres, SQLCoder greatly beats all major open-source models. When optimized for a specific database schema, it performs better than gpt-4.
The model’s size is such that it may be executed in 16-bit floats on a single A100-40GB or an 8-bit quantized high-end consumer GPU (such as an RTX 3090/4090). The evaluation mechanism for LLM-generated SQL is likewise being made open-source. Evaluating SQL code can be difficult. Researchers want to conduct extensive, public, and reproducible testing to push the limits of open-source text-to-SQL systems.
The model weights are licensed under CC BY-SA 4.0. The model is free for both personal and commercial use. If you change the consequences (by fine-tuning, for instance), you must release those changes as open source under the same license.
SQLCoder is an optimized version of StarCoder that uses 15B parameters. SQLCoder has been fine-tuned on progressively challenging SQL queries created by hand. Database schema-specific tuning allows it to achieve or exceed the performance of GPT-4.
Researchers have used SQLCoder with enterprise customers in the healthcare, financial services, and government sectors in the past three months. Self-hosted models are the sole option for customers who do not want sensitive data to leave their servers when employing LLMs.
The model was refined in two phases by the research team. They honed StarCoder’s foundational model using only our mild to moderate queries. The resulting defog-easy model was then fine-tuned on difficult and extremely difficult questions to produce SQLcoder. Defog In our benchmarking, the SQLCoder outperforms nearly every popular model except GPT-4. In particular, it outperforms models more than ten times its size, such as the gpt-3.5-turbo and the text-da-vinci-003. These outcomes only represent the performance of SQLCoder on general SQL databases and not on specific database schemas. When SQLCoder is optimized for particular database schemas, it can outperform OpenAI’s GPT-4 while incurring less latency.
An open-source version of SQLCoder can be found at https://github.com/defog-ai/sqlcoder. It has many potential applications, such as:
Putting it through its paces on a home turf
Putting it in the cloud
Having it work with other programs
SQLCoder is a robust program that may streamline and automate data processing operations. Query the database easily using SQLCoder, which translates the natural language questions into SQL queries.
Using SQLCoder can help you in a variety of ways.
SQLCoder’s accuracy is such that it can construct correct and efficient SQL queries.
SQLCoder is efficient in that it can produce SQL queries rapidly and effortlessly.
SQLCoder produces queries that are idiomatic or written by the rules of SQL.
SQLCoder’s adaptability means that it can be modified to suit the requirements of your program.
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The post Meet SQLCoder: An New Open-Sourced and State of the Art Model for Converting Natural Language Questions to SQL Queries appeared first on MarkTechPost.
Defog.ai has released SQLCoder, a cutting-edge model for translating inquiries in natural language into database queries. Regarding generic SQL schemas in Postgres, SQLCoder greatly beats all major open-source models. When optimized for a specific database schema, it performs better than gpt-4. The model’s size is such that it may be executed in 16-bit floats on
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