license: llama2
inference:
parameters:
do_sample: false
max_length: 200
base_model: meta-llama/Llama-2-7b
widget:
- 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):
example_title: read test.csv
- 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):
example_title: get _amount columns
- 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):
example_title: taxi trips
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.