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RichardErkhov/
motherduckdb_-_DuckDB-NSQL-7B-v0.1-gguf

Name: motherduckdb_-_DuckDB-NSQL-7B-v0.1-gguf
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DuckDB-NSQL-7B-v0.1 - GGUF

Model creator: https://huggingface.co/motherduckdb/
Original model: https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1/

Name	Quant method	Size
DuckDB-NSQL-7B-v0.1.Q2_K.gguf	Q2_K	2.36GB
DuckDB-NSQL-7B-v0.1.IQ3_XS.gguf	IQ3_XS	2.6GB
DuckDB-NSQL-7B-v0.1.IQ3_S.gguf	IQ3_S	2.75GB
DuckDB-NSQL-7B-v0.1.Q3_K_S.gguf	Q3_K_S	2.75GB
DuckDB-NSQL-7B-v0.1.IQ3_M.gguf	IQ3_M	2.9GB
DuckDB-NSQL-7B-v0.1.Q3_K.gguf	Q3_K	3.07GB
DuckDB-NSQL-7B-v0.1.Q3_K_M.gguf	Q3_K_M	3.07GB
DuckDB-NSQL-7B-v0.1.Q3_K_L.gguf	Q3_K_L	3.35GB
DuckDB-NSQL-7B-v0.1.IQ4_XS.gguf	IQ4_XS	3.4GB
DuckDB-NSQL-7B-v0.1.Q4_0.gguf	Q4_0	3.56GB
DuckDB-NSQL-7B-v0.1.IQ4_NL.gguf	IQ4_NL	3.58GB
DuckDB-NSQL-7B-v0.1.Q4_K_S.gguf	Q4_K_S	3.59GB
DuckDB-NSQL-7B-v0.1.Q4_K.gguf	Q4_K	3.8GB
DuckDB-NSQL-7B-v0.1.Q4_K_M.gguf	Q4_K_M	3.8GB
DuckDB-NSQL-7B-v0.1.Q4_1.gguf	Q4_1	3.95GB
DuckDB-NSQL-7B-v0.1.Q5_0.gguf	Q5_0	4.33GB
DuckDB-NSQL-7B-v0.1.Q5_K_S.gguf	Q5_K_S	4.33GB
DuckDB-NSQL-7B-v0.1.Q5_K.gguf	Q5_K	4.45GB
DuckDB-NSQL-7B-v0.1.Q5_K_M.gguf	Q5_K_M	4.45GB
DuckDB-NSQL-7B-v0.1.Q5_1.gguf	Q5_1	4.72GB
DuckDB-NSQL-7B-v0.1.Q6_K.gguf	Q6_K	5.15GB
DuckDB-NSQL-7B-v0.1.Q8_0.gguf	Q8_0	6.67GB

Original model description:

license: llama2 inference: parameters: do_sample: false max_length: 200 base_model: meta-llama/Llama-2-7b widget:

text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):" example_title: "read test.csv"
text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):" example_title: "get _amount columns"
text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.\n\n### Input:\nHere is the database schema that the SQL query will run on:\nCREATE TABLE rideshare (\n hvfhs_license_num varchar,\n dispatching_base_num varchar,\n originating_base_num varchar,\n request_datetime timestamp,\n on_scene_datetime timestamp,\n pickup_datetime timestamp,\n dropoff_datetime timestamp,\n trip_miles double,\n trip_time bigint,\n\n);\n\n### Question:\nget longest trip in december 2022\n\n### 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:

python

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:

python

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:

python

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.

RichardErkhov/motherduckdb_-_DuckDB-NSQL-7B-v0.1-gguf

Original model description:

DuckDB-NSQL-7B

Model Description

Training Data

Evaluation Data

Training Procedure

Intended Use and Limitations

How to Use

RichardErkhov/
motherduckdb_-_DuckDB-NSQL-7B-v0.1-gguf