nsql-llama-2-7B
Maintainer: NumbersStation
76
🏅
| 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
nsql-llama-2-7B is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks. It is based on Meta's original Llama-2 7B model and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of text-to-SQL pairs. The model was developed by NumbersStation.
Similar models include Natural-SQL-7B by ChatDB, which also focuses on strong performance in text-to-SQL instructions, and the Llama-2 family of models developed by Meta.
Model inputs and outputs
Inputs
- Natural language prompts: The model takes natural language prompts as input, typically in the format of text-to-SQL requests.
- Database schema: The model also requires the database schema, which is provided as part of the input.
Outputs
- SQL queries: The model outputs SQL queries that answer the provided natural language prompts, based on the given database schema.
Capabilities
nsql-llama-2-7B is designed to excel at text-to-SQL generation tasks. It has been trained on a large dataset of SQL queries and text-to-SQL pairs, giving it strong performance in understanding natural language prompts and translating them into accurate SQL queries.
What can I use it for?
You can use nsql-llama-2-7B for a variety of applications that involve generating SQL queries from natural language inputs, such as:
- Intelligent database interfaces: Build applications that allow users to interact with databases using natural language, without requiring them to write SQL directly.
- Automated report generation: Generate SQL queries to extract and summarize data from databases based on user requests.
- SQL code completion: Use the model to suggest or autocomplete SQL statements as users are typing.
Things to try
One interesting aspect of nsql-llama-2-7B is its ability to handle complex, compound questions that other models may struggle with. Try providing the model with multi-part queries or prompts that require reasoning across multiple tables or database concepts, and see how it performs.
You can also experiment with fine-tuning the model on your own dataset of text-to-SQL pairs to further customize its performance for your specific use case.
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
✅
nsql-6B
50
NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks. The NSQL-6B checkpoint included in this repository is based on CodeGen-Multi 6B from Salesforce and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of text-to-SQL pairs. Similar models in this family include the nsql-llama-2-7B and DuckDB-NSQL-7B-v0.1 which are based on Meta's Llama-2 and fine-tuned for SQL generation, as well as the more broadly capable natural-sql-7b model from ChatDB. Model inputs and outputs NSQL-6B is a text-to-text model designed for SQL generation tasks. Given a natural language prompt and database schema, the model can generate valid SQL queries to answer the given question. Inputs Natural language prompts or questions related to a database schema Database schema definition in the form of SQL CREATE TABLE statements Outputs SQL queries that answer the given prompt or question, typically in the form of SELECT statements Capabilities The NSQL-6B model excels at translating natural language questions into SQL queries for a given database schema. It can handle a wide range of SQL constructs, including SELECT, WHERE, JOIN, ORDER BY, GROUP BY, and more. The model has shown strong performance on text-to-SQL benchmarks like Spider and GeoQuery. What can I use it for? NSQL-6B can be a powerful tool for automating the process of converting natural language requests into SQL queries. This can be useful in a variety of applications, such as: Building conversational interfaces for databases, allowing users to query data using natural language Generating SQL code to power business intelligence and reporting tools Assisting developers in quickly prototyping and iterating on database-backed applications Enhancing productivity for data analysts and scientists who need to frequently interact with databases Things to try One interesting aspect of the NSQL model family is the ability to fine-tune the models for specific database systems and use cases. For example, the DuckDB-NSQL-7B-v0.1 model is fine-tuned on DuckDB-specific text-to-SQL pairs, allowing it to generate queries that leverage DuckDB's unique features and extensions. Developers and data professionals could experiment with fine-tuning the NSQL-6B model on their own dataset of SQL queries and database schemas to create a highly customized SQL generation assistant tailored to their specific needs.
Updated Invalid Date
📉
DuckDB-NSQL-7B-v0.1
69
DuckDB-NSQL-7B-v0.1 is an autoregressive open-source large foundation model (FM) designed specifically for SQL generation tasks. It is based on Meta's original Llama-2 7B model and further pre-trained on a dataset of general SQL queries, then fine-tuned on a dataset of DuckDB text-to-SQL pairs. This model is part of the NSQL family of models from motherduckdb. It aims to outperform existing text-to-SQL models by generating valid DuckDB SQL statements beyond just SELECT queries. The model was trained on 200k DuckDB text-to-SQL pairs, synthetically generated and from the NSText2SQL dataset. Model Inputs and Outputs Inputs Natural language instructions or questions about data in a DuckDB database Outputs Valid DuckDB SQL statements to answer the given input prompt, which may include complex queries beyond just SELECT statements. Capabilities The DuckDB-NSQL-7B-v0.1 model has been designed to handle a wide range of SQL generation tasks for DuckDB databases. Unlike traditional text-to-SQL models, it can generate any valid DuckDB SQL statement, including those for official DuckDB extensions, not just simple SELECT queries. For example, the model can generate SQL to create new tables, insert data, update records, and more, in addition to complex analytical queries. This makes it a versatile tool for working with DuckDB databases, beyond just querying the data. What Can I Use It For? The DuckDB-NSQL-7B-v0.1 model is well-suited for building applications and tools that interact with DuckDB databases using natural language. This could include: Developing conversational interfaces for DuckDB data analysis Automating DuckDB database management tasks through natural language commands Integrating DuckDB functionality into no-code/low-code platforms Enhancing business intelligence and data exploration workflows By leveraging the model's capabilities to generate complex DuckDB SQL, developers can create more powerful and user-friendly data-driven applications. Things to Try One interesting aspect of the DuckDB-NSQL-7B-v0.1 model is its ability to generate SQL statements beyond just SELECT queries. Try providing the model with prompts that require complex database operations, such as: Creating a new table from a CSV file Updating multiple records based on a filter condition Performing joins and aggregations across multiple tables Calling DuckDB extension functions in the generated SQL Observe how the model handles these more advanced SQL use cases and see if it can generate correct and effective solutions. This can help you understand the limits of the model's capabilities and explore new ways to leverage it in your DuckDB-powered applications.
Updated Invalid Date
🔎
natural-sql-7b
95
The natural-sql-7b model by ChatDB is a powerful text-to-SQL generation model that outperforms other models of similar size in its space. It has excellent performance on complex, compound SQL questions and can handle tasks that other models struggle with. The model is trained to convert natural language instructions into SQL queries, making it a valuable tool for non-technical users to interact with databases. Similar models include pipSQL-1.3b by PipableAi, which also focuses on text-to-SQL generation, and the SQLCoder and SQLCoder2 models developed by Defog, which are state-of-the-art large language models for natural language to SQL conversion. Model inputs and outputs Inputs Natural language instructions**: The model takes in natural language questions or instructions and converts them into SQL queries. Outputs SQL queries**: The model generates SQL queries based on the provided natural language input. Capabilities The natural-sql-7b model has exceptional performance in text-to-SQL tasks, outperforming models of similar size. It can handle complex, compound questions that often trip up other models. For example, the model can generate SQL queries to find the total revenue from customers in New York compared to San Francisco, including the difference between the two. What can I use it for? The natural-sql-7b model is a valuable tool for non-technical users to interact with databases. It can be used in a variety of applications, such as: Business intelligence and data analysis**: Users can ask natural language questions about the data in their database and get the corresponding SQL queries, allowing them to quickly generate insights without needing to learn SQL. Customer support**: The model can be used to build chatbots that can help customers find information in a database by understanding their natural language requests. Productivity tools**: The model can be integrated into productivity software, allowing users to quickly generate SQL queries to extract the data they need. Things to try One interesting aspect of the natural-sql-7b model is its ability to handle complex, compound questions. Try asking the model questions that involve multiple steps or conditions, such as "Find the top 3 best-selling products by revenue, but only for products with a price above the average product price." The model should be able to generate the appropriate SQL query to answer this type of complex question. Another interesting thing to try is fine-tuning the model on a specific database schema or domain. By training the model on data more closely related to the task at hand, you may be able to further improve its performance and tailor it to your specific needs.
Updated Invalid Date
🏋️
Llama-2-7b-chat-hf
146
Llama-2-7b-chat-hf is a 7B parameter large language model (LLM) developed by Meta. It is part of the Llama 2 family of models, which range in size from 7B to 70B parameters. The Llama 2 models are pretrained on a diverse corpus of publicly available data and then fine-tuned for dialogue use cases, making them optimized for assistant-like chat interactions. Compared to open-source chat models, the Llama-2-Chat models outperform on most benchmarks and are on par with popular closed-source models like ChatGPT and PaLM in human evaluations for helpfulness and safety. Model inputs and outputs Inputs Text**: The Llama-2-7b-chat-hf model takes natural language text as input. Outputs Text**: The model generates natural language text as output. Capabilities The Llama-2-7b-chat-hf model demonstrates strong performance on a variety of natural language tasks, including commonsense reasoning, world knowledge, reading comprehension, and math problem-solving. It also exhibits high levels of truthfulness and low toxicity in generation, making it suitable for use in assistant-like applications. What can I use it for? The Llama-2-7b-chat-hf model is intended for commercial and research use in English. The fine-tuned Llama-2-Chat versions can be used to build interactive chatbots and virtual assistants that engage in helpful and informative dialogue. The pretrained Llama 2 models can also be adapted for a variety of natural language generation tasks, such as summarization, translation, and content creation. Things to try Developers interested in using the Llama-2-7b-chat-hf model should carefully review the responsible use guide provided by Meta, as large language models can carry risks and should be thoroughly tested and tuned for specific applications. Additionally, users should follow the formatting guidelines for the chat versions, which include using INST and > tags, BOS and EOS tokens, and proper whitespacing and linebreaks.
Updated Invalid Date