Llama 2-7B Fine-Tuned for Text-to-SQL

This model is a fine-tuned version of the Llama 2-7B model, specifically adapted for Text-to-SQL tasks. The model was trained to generate SQL queries from natural language questions, providing a robust solution for systems that need to translate user queries into executable SQL code.

Model Details

Model Name: Llama 2-7B Fine-Tuned for Text-to-SQL
Base Model: Llama 2-7B
Model Developers: Fine-tuned by MertML
License: Custom commercial license. Please refer to the repository for terms.
Intended Use: Designed for generating SQL queries from natural language input. Ideal for applications in databases, conversational agents, and data analysis tools.

Model Architecture

Llama 2-7B is an autoregressive language model based on the transformer architecture. The fine-tuned version has been specifically adapted for the Text-to-SQL task, trained to convert user-written questions into valid and executable SQL queries using supervised fine-tuning.

Intended Use Cases

Translating natural language queries into SQL queries, suitable for database query generation, business intelligence applications, and conversational agents that interact with databases.

Out-of-Scope Uses

While this model is capable of text generation, it is fine-tuned specifically for Text-to-SQL tasks and may not perform well for general-purpose language generation tasks.

Training Data

The model was fine-tuned using the refined-sql-create-context dataset, which contains natural language queries, corresponding table schemas, and the correct SQL queries. This dataset was preprocessed to ensure that all queries were valid and executable on a MySQL database.

Training Data Size: 11,632 samples, split into training, validation, and test sets (80%, 10%, 10%).
Data Source: SQL-create-context dataset (refined for this task).
Data Preprocessing: Ambiguities in table schemas were resolved, invalid SQL queries were removed, and normalization was performed on SQL formatting for consistent evaluation.

Model Performance

The fine-tuned Llama 2-7B on Text-to-SQL demonstrated significant improvements over the base model in generating syntactically correct and contextually relevant SQL queries. Performance was evaluated on a set of queries with varying levels of difficulty, and the model was benchmarked against the refined-sql-create-context datasets.

Evaluation Metrics

Accuracy: Measures the percentage of generated SQL queries that are syntactically and semantically correct.
Execution Success Rate: Measures the percentage of SQL queries that execute successfully against a database.
Response Quality: Assesses the relevance and correctness of the generated SQL queries in context.