Edit model card

DuckDB-NSQL-7B

Model Description

NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.

In this repository we are introducing a new member of NSQL, DuckDB-NSQL. It's 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 DuckDB text-to-SQL pairs.

Training Data

200k DuckDB text-to-SQL pairs, synthetically generated using Mixtral-8x7B-Instruct-v0.1, guided by the DuckDB v0.9.2 documentation. And text-to-SQL pairs from NSText2SQL that were transpiled to DuckDB SQL using sqlglot.

Evaluation Data

We evaluate our models on a DuckDB-specific benchmark that contains 75 text-to-SQL pairs. The benchmark is available here.

Training Procedure

DuckDB-NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using 80GB A100s, leveraging data and model parallelism. We fine-tuned for 10 epochs.

Intended Use and Limitations

The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputs. In contrast to existing text-to-SQL models, the SQL generation is not contrained to SELECT statements, but can generate any valid DuckDB SQL statement, including statements for official DuckDB extensions.

How to Use

Example 1:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)

text = """### Instruction:
Your task is to generate valid duckdb SQL to answer the following question.

### Input:

### Question:
create a new table called tmp from test.csv

### Response (use duckdb shorthand if possible):
"""

input_ids = tokenizer(text, return_tensors="pt").input_ids

generated_ids = model.generate(input_ids, max_length=500)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

Example 2:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)

text = """### Instruction:
Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.

### Input:
Here is the database schema that the SQL query will run on:
CREATE TABLE taxi (
    VendorID bigint,
    tpep_pickup_datetime timestamp,
    tpep_dropoff_datetime timestamp,
    passenger_count double,
    trip_distance double,
    fare_amount double,
    extra double,
    tip_amount double,
    tolls_amount double,
    improvement_surcharge double,
    total_amount double,
);

### Question:
get all columns ending with _amount from taxi table

### Response (use duckdb shorthand if possible):"""

input_ids = tokenizer(text, return_tensors="pt").input_ids

generated_ids = model.generate(input_ids, max_length=500)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

Example 3:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)

text = """### Instruction:
Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.

### Input:
Here is the database schema that the SQL query will run on:
CREATE TABLE rideshare (
    hvfhs_license_num varchar,
    dispatching_base_num varchar,
    originating_base_num varchar,
    request_datetime timestamp,
    on_scene_datetime timestamp,
    pickup_datetime timestamp,
    dropoff_datetime timestamp,
    trip_miles double,
    trip_time bigint,

);

### Question:
get longest trip in december 2022

### Response (use duckdb shorthand if possible):
"""

input_ids = tokenizer(text, return_tensors="pt").input_ids

generated_ids = model.generate(input_ids, max_length=500)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

For more information (e.g., run with your local database), please find examples in this repository.

Downloads last month
2,193
Safetensors
Model size
6.74B params
Tensor type
BF16
ยท

Finetuned from

Spaces using motherduckdb/DuckDB-NSQL-7B-v0.1 2