sqlcoder2
Maintainer: defog
105
🌿
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
sqlcoder2 is a state-of-the-art language model developed by Defog for converting natural language questions to SQL queries. It outperforms popular models like gpt-3.5-turbo and text-davinci-003 on benchmark tasks, and can significantly outperform gpt-4 when fine-tuned on a given database schema.
The model is built on top of a base StarCoder model and has been fine-tuned on over 20,000 human-curated SQL questions covering 10 different database schemas. Notably, the training data did not include any of the schemas used in the evaluation framework, allowing for testing on truly novel datasets.
Model inputs and outputs
Inputs
- Natural language questions: The model takes natural language questions about data stored in a database as input.
- Database schema information: The model also accepts information about the database schema that the SQL query will run against, such as table and column definitions.
Outputs
- SQL queries: The model outputs SQL queries that aim to answer the input natural language question based on the provided database schema.
Capabilities
sqlcoder2 demonstrates strong performance on a variety of SQL-related tasks, including date filtering, group-by and aggregation, order-by, ratio calculations, table joins, and where clause filtering. It is particularly adept at more complex queries involving multiple steps and operations.
What can I use it for?
sqlcoder2 can be a valuable tool for non-technical users who need to interact with and analyze data stored in SQL databases. By allowing them to ask questions in natural language and generating the corresponding SQL, it enables self-service analytics without requiring SQL expertise.
Some potential use cases include:
- Powering a conversational analytics interface for business users
- Automating the generation of SQL reports and dashboards
- Enabling data exploration and discovery for data scientists and analysts
Things to try
One interesting aspect of sqlcoder2 is its ability to outperform even large language models like gpt-4 when fine-tuned on a specific database schema. This suggests that targeted fine-tuning on relevant data can significantly boost the model's performance for domain-specific tasks.
Developers could explore fine-tuning sqlcoder2 on their own database schemas to unlock its full potential within their organization's data infrastructure.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🤷
sqlcoder
299
sqlcoder is a state-of-the-art large language model developed by Defog for converting natural language questions to SQL queries. It slightly outperforms the popular gpt-3.5-turbo model on natural language to SQL generation tasks, and significantly outperforms all other popular open-source models like text-davinci-003 and wizardcoder. The model was fine-tuned on a base StarCoder model. Model inputs and outputs The sqlcoder model takes in natural language questions as input and generates SQL queries as output. The model was trained on a diverse dataset of over 10,000 human-curated questions spanning 10 different database schemas. Inputs Natural language questions about querying databases Outputs SQL queries that correspond to the input natural language questions Capabilities The sqlcoder model demonstrates strong performance on a variety of SQL query types, including where, group by, order by, ratio, join, and more. It significantly outperforms other models like gpt-3.5-turbo and text-davinci-003 on these tasks. What can I use it for? The sqlcoder model is well-suited for building analytics tools that allow non-technical users to easily explore and understand data stored in SQL databases. By translating natural language questions into SQL, the model empowers users to quickly get insights without needing SQL expertise. Things to try One interesting aspect of sqlcoder is its ability to handle increasingly complex SQL queries as the training data difficulty increases. The model's performance jumps by 7 percentage points when fine-tuned on the "hard" and "extra hard" questions in addition to the "easy" and "medium" ones. This suggests the model could potentially be further improved by continued fine-tuning on more challenging data.
Updated Invalid Date
💬
sqlcoder-7b
59
sqlcoder-7b is a 7 billion parameter model developed by Defog that is designed for converting natural language questions into SQL queries. It is a state-of-the-art language model that outperforms popular open-source models like GPT-3.5 and even GPT-4 on natural language to SQL generation tasks. The model is fine-tuned on a base Mistral-7B model. Compared to similar models like sqlcoder2 and sqlcoder-34b-alpha, sqlcoder-7b has slightly lower performance but consumes fewer GPU resources, making it more accessible for users with less powerful hardware. The maintainer, Defog, has also developed larger models like sqlcoder2 and sqlcoder-34b-alpha that offer even better performance. Model inputs and outputs Inputs Natural language question**: The model takes as input a natural language question about data stored in a database. Outputs SQL query**: The model outputs a SQL query that can be used to retrieve the data to answer the input question. Capabilities sqlcoder-7b is highly capable at translating natural language questions into accurate SQL queries. It performs particularly well on questions involving group-by, order-by, and date-based operations, outperforming GPT-4 and other popular models. The model also handles complex queries involving joins and ratio calculations effectively. What can I use it for? You can use sqlcoder-7b as an analytics tool to empower non-technical users to explore data stored in SQL databases. By allowing users to ask questions in plain language and generating the corresponding SQL queries, the model can make data more accessible and enable faster insights. This model could be particularly useful for customer-facing applications, business intelligence tools, or data exploration platforms where end-users need to query data without writing SQL directly. Things to try Try providing the model with a variety of natural language questions covering different database schema and query types. Observe how the model performs on complex queries involving aggregations, joins, and advanced SQL constructs. You can also experiment with fine-tuning the model on your own dataset to improve its performance on your specific use case.
Updated Invalid Date
👁️
sqlcoder-34b-alpha
166
sqlcoder-34b-alpha is a state-of-the-art language model developed by defog for converting natural language questions to SQL queries. It is a 34B parameter model that outperforms gpt-4 and gpt-4-turbo on natural language to SQL generation tasks, and significantly outperforms other popular open-source models like gpt-3.5 and text-davinci-003. The model is fine-tuned on a base CodeLlama model, and has been trained on over 20,000 human-curated SQL questions covering a diverse range of schemas. It demonstrates strong performance across various SQL query types like GROUP BY, ORDER BY, WHERE, and JOIN. Similar models include the earlier sqlcoder and sqlcoder-70b-alpha models, which also aim to provide powerful text-to-SQL capabilities. Model inputs and outputs Inputs Natural language question**: A free-form text question describing the desired SQL query. Database schema**: The schema of the database tables the query should run against, provided as a set of SQL CREATE TABLE statements. Outputs SQL query**: The generated SQL query that answers the natural language question, based on the provided database schema. Capabilities The sqlcoder-34b-alpha model demonstrates strong performance on a wide range of SQL query types. It can handle complex queries involving aggregations, joins, filters, and ordering, outperforming even large language models like GPT-4 on these tasks. The model's capabilities are particularly impressive when it comes to generating queries involving GROUP BY and ORDER BY clauses, where it achieves over 90% accuracy. It also performs well on queries requiring table joins and ratio calculations. What can I use it for? sqlcoder-34b-alpha is primarily intended to be used as an analytics tool, allowing non-technical users to easily query and explore data stored in SQL databases. By translating natural language questions into SQL, the model can empower business users, data analysts, and others to gain insights from their data without requiring deep SQL expertise. Some potential use cases for the model include: Self-service data exploration and reporting for business users Easier access to data for non-technical stakeholders Streamlining data-driven decision making Things to try One interesting aspect of the sqlcoder-34b-alpha model is its ability to handle a wide variety of SQL query types. Rather than focusing on a narrow set of common queries, the model has been trained to handle more complex and varied SQL constructs. To get a sense of the model's capabilities, you could try providing it with natural language questions involving advanced SQL features like window functions, subqueries, or set operations. See how the model performs on these more complex queries, and how its outputs compare to those of other SQL generation models. Another interesting area to explore would be the model's performance on queries against novel database schemas that were not included in the training data. This could help assess the model's generalization abilities and its potential to be applied to a wide range of real-world SQL use cases.
Updated Invalid Date
👁️
sqlcoder-70b-alpha
190
The sqlcoder-70b-alpha is a capable large language model for natural language to SQL generation, developed by Defog, Inc. It outperforms all generalist models, including GPT-4, on text to SQL tasks. The model was fine-tuned from the CodeLlama-70B model. Similar models include the defog-sqlcoder-7b-2 and the starcoder and starcoder2-15b models, which are also large language models with capabilities for code generation. Model inputs and outputs The sqlcoder-70b-alpha model takes natural language text as input and generates SQL queries as output. This makes it useful for tasks like data exploration and analytics, where users can describe their information needs in plain language and have the model translate that to the corresponding SQL. Inputs Natural language text describing an information need or data request Outputs SQL queries that can be executed against a database to retrieve the requested information Capabilities The sqlcoder-70b-alpha model is highly capable at translating natural language descriptions into accurate and executable SQL queries. It outperforms other generalist language models on this task, making it a valuable tool for users who need to interact with databases but may not be skilled in SQL. What can I use it for? The sqlcoder-70b-alpha model is intended to be used by non-technical users to explore and understand data stored in SQL databases. It can act as an analytics assistant, allowing users to describe their information needs in plain language and have the model generate the relevant SQL queries. However, the model has not been trained to handle malicious requests, so it should only be used by users with read-only access to databases. It is not suitable for use as a database administration tool. Things to try One interesting thing to try with the sqlcoder-70b-alpha model is to provide it with a series of natural language prompts describing different information needs, and observe how it translates those prompts into SQL. This can help you understand the model's strengths and limitations in handling various types of data requests. You can also experiment with providing the model with prompts that combine multiple pieces of information, such as filtering, grouping, and ordering, to see how it handles more complex SQL queries.
Updated Invalid Date