Pipableai
Models by this creator
🤔
pip-sql-1.3b
72
The pip-sql-1.3b model, developed by PipableAI, is a 1.3 billion parameter SQL model that outperforms most SQL expert models and even GPT-3.5 on popular benchmarks. It is a distilled version of the DeepSeek base model, trained using a combination of softmax cross entropy, modified policy gradient, and Q loss in an EM setup. This novel training approach has enabled the model to achieve exceptional performance on text-to-SQL tasks. Compared to similar models like distilbert-base-cased-distilled-squad, sqlcoder-70b-alpha, and sqlcoder, the pip-sql-1.3b model stands out for its significant performance improvements on SQL-related tasks. It leverages a unique training approach to deliver state-of-the-art results, making it a valuable tool for analysts and developers working with SQL databases. Model inputs and outputs Inputs Schema**: The schema of the database that the SQL query will be executed against. Question**: The natural language question that the model will attempt to translate into a SQL query. Outputs SQL query**: The SQL query generated by the model based on the provided schema and question. Capabilities The pip-sql-1.3b model excels at translating natural language questions into SQL queries. It outperforms most SQL expert models and even GPT-3.5 on popular benchmarks like Semantic Evaluation for Text-to-SQL with Distilled Test Suites and Defog SQL-Eval. For example, on the Semantic Evaluation benchmark, the pip-sql-1.3b model achieves an overall accuracy of 42.1% on the "hard" and "extra" difficulty questions, significantly higher than the 31% accuracy of GPT-3.5. What can I use it for? The pip-sql-1.3b model can be a valuable tool for developers, analysts, and anyone working with SQL databases. It can be used to quickly generate SQL queries based on natural language questions, saving time and effort. This can be particularly useful for non-technical users who need to extract data from a database but are not proficient in SQL. Additionally, the model's strong performance on SQL-related tasks makes it a compelling choice for building applications that require natural language processing capabilities for database interactions, such as chatbots, voice assistants, or data visualization tools. Things to try One interesting aspect of the pip-sql-1.3b model is its use of a novel training approach that combines softmax cross entropy, modified policy gradient, and Q loss in an EM setup. This approach has enabled the model to achieve exceptional performance on text-to-SQL tasks, outperforming even much larger models like GPT-3.5. Researchers and developers interested in advancing the state of the art in natural language processing for database interactions could explore ways to further refine or build upon this training approach. Additionally, testing the model's performance on a wider range of SQL-related tasks or evaluating its robustness to different types of database schemas and queries could provide valuable insights into its capabilities and limitations.
Updated 5/28/2024
🚀
pip-library-etl-1.3b
44
The pip-library-etl-1.3b model, created by PipableAI, is a text-to-text AI model designed for a variety of natural language processing tasks. It is comparable in performance to much larger language models like GPT-3.5 on tasks like function call generation, automated documentation, and module documentation. The model was developed using softmax cross entropy, a modified form of policy gradient, and Q loss, optimized in an EM setup. Model inputs and outputs Inputs Natural language prompts**: The model can accept natural language prompts or instructions as input, such as questions, commands, or descriptions of a task. Outputs Generated text**: The model outputs generated text that responds to or completes the input prompt. This can include code snippets, function calls, documentation, or other relevant text. Capabilities The pip-library-etl-1.3b model excels at a variety of text-to-text tasks. It can generate Python function calls based on provided questions and docstrings or undocumented code, automatically generate comprehensive docstrings for Python functions, and create documentation for all methods and functions within a given module or package. These capabilities can streamline the development process and help maintain well-documented codebases. What can I use it for? The pip-library-etl-1.3b model can be useful for developers and teams looking to automate various text-to-text tasks in their software development workflows. It can help with prototyping code snippets, generating example function calls, and creating comprehensive documentation, saving time and effort. Developers could integrate the model into their existing tools and processes to enhance productivity and efficiency. Things to try One interesting aspect of the pip-library-etl-1.3b model is its ability to generate function calls and documentation based on natural language prompts. You could try providing the model with a variety of questions or prompts related to your codebase, such as "Generate a function call to fetch data from a database" or "Create a docstring for a Python function that calculates the area of a circle." Observe how the model responds and see if the generated output is useful and relevant to your needs.
Updated 9/6/2024