defog-sqlcoder-7b-2
Maintainer: nateraw
21
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | No paper link provided |
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Model overview
The defog-sqlcoder-7b-2 is a capable large language model for natural language to SQL generation, developed by the maintainer nateraw. It can be compared to similar models like snowflake-arctic-instruct, an efficient and intelligent open-source language model, and meta-llama-3-8b-instruct, an 8 billion parameter language model from Meta fine-tuned for chat completions.
Model inputs and outputs
The defog-sqlcoder-7b-2 model takes in a variety of inputs, including a question, table metadata, and optional parameters like temperature, top-k filtering, and presence/frequency penalties. The model then generates a SQL query as output to answer the given question based on the provided database schema.
Inputs
- Question: The natural language question to be answered
- Table Metadata: A description of the database schema the query will run on
- Temperature: A value used to modulate the next token probabilities
- Max New Tokens: The maximum number of tokens the model should generate
- Top K: The number of highest probability tokens to consider for generating the output
- Top P: A probability threshold for generating the output
- Presence Penalty: Encourages the model to talk about new topics
- Frequency Penalty: Discourages the model from repeating the same tokens
Outputs
- SQL Query: The generated SQL query to answer the given question
Capabilities
The defog-sqlcoder-7b-2 model is capable of generating SQL queries to answer natural language questions based on a provided database schema. It can handle a variety of query types and can be fine-tuned on specific domains or datasets.
What can I use it for?
The defog-sqlcoder-7b-2 model can be used in a variety of applications that involve translating natural language to SQL, such as building user-friendly database interfaces, automating data analysis tasks, or powering natural language-based data exploration tools. Companies could potentially monetize this model by integrating it into their products or services to provide more accessible data querying capabilities for their customers.
Things to try
One interesting thing to try with the defog-sqlcoder-7b-2 model is to experiment with the various input parameters, such as adjusting the temperature or top-k/top-p values, to see how it affects the generated SQL queries. You could also try fine-tuning the model on a specific database schema or domain to see if it improves the model's performance on that task.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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