Numbers Station Text to SQL model for DuckDB.
NSQL is a family of autoregressive open-source foundational models (FMs) that are particularly designed for SQL generation tasks. We are thrilled to introduce DuckDB-NSQL in this repository, an FM tailored for local DuckDB SQL analytics tasks. All model weights can be found on HuggingFace.
| Model Name | Size | Link |
|---|---|---|
| motherduckdb/DuckDB-NSQL-7B-v0.1 | 7B | link |
| motherduckdb/DuckDB-NSQL-7B-v0.1-GGUF | 7B | link |
To install all the necessary dependencies, please run
pip install -r requirements.txt
Please refer to the examples in the examples/ folder to learn how to connect to a local DuckDB and directly query your data. A simple notebook is provided in the examples/ directory for reference.
To host the model with llama.cpp, please execute the following:
# Import necessary modules
from llama_cpp import Llama
from wurlitzer import pipes
# Set up client with model path and context size
with pipes() as (out, err):
client = Llama(
model_path="DuckDB-NSQL-7B-v0.1-q8_0.gguf",
n_ctx=2048,
)To load the DuckDB database and query against it, please execute the following:
# Import necessary modules
import duckdb
from utils import generate_sql
# Connect to DuckDB database
con = duckdb.connect("nyc.duckdb")
# Sample question for SQL generation
question = "alter taxi table and add struct column with name test and keys a:int, b:double"
# Generate SQL, check validity, and print
sql = generate_sql(question, con, client)
print(sql)The training data for this model consists of two parts: 1) 200k synthetically generated DuckDB SQL queries, based on the DuckDB v.0.9.2 documentation, and 2) labeled text-to-SQL pairs from NSText2SQL transpiled to DuckDB SQL using sqlglot.
Please refer to the eval/ folder to check the details for evaluating the model against our proposed DuckDB benchmark.
We would like to express our appreciation to all authors of the evaluation scripts. Their work made this project possible.