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--- |
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license: llama2 |
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inference: |
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parameters: |
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do_sample: false |
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max_length: 200 |
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base_model: meta-llama/Llama-2-7b |
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widget: |
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- 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):" |
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example_title: "read test.csv" |
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- 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):" |
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example_title: "get _amount columns" |
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- 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):" |
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example_title: "taxi trips" |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/DuckDB-NSQL-7B-v0.1-GGUF |
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This is quantized version of [motherduckdb/DuckDB-NSQL-7B-v0.1](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1) created using llama.cpp |
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# Original Model Card |
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# DuckDB-NSQL-7B |
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## Model Description |
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NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks. |
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In this repository we are introducing a new member of NSQL, DuckDB-NSQL. It's based on Meta's original [Llama-2 7B model](https://huggingface.co/meta-llama/Llama-2-7b) 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. |
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## Training Data |
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200k DuckDB text-to-SQL pairs, synthetically generated using [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), guided by the DuckDB v0.9.2 documentation. And text-to-SQL pairs from [NSText2SQL](https://huggingface.co/datasets/NumbersStation/NSText2SQL) that were transpiled to DuckDB SQL using [sqlglot](https://github.com/tobymao/sqlglot). |
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## Evaluation Data |
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We evaluate our models on a DuckDB-specific benchmark that contains 75 text-to-SQL pairs. The benchmark is available [here](https://github.com/NumbersStationAI/DuckDB-NSQL/). |
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## Training Procedure |
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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. |
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## Intended Use and Limitations |
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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. |
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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. |
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## How to Use |
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Example 1: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1") |
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model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16) |
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text = """### Instruction: |
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Your task is to generate valid duckdb SQL to answer the following question. |
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### Input: |
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### Question: |
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create a new table called tmp from test.csv |
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### Response (use duckdb shorthand if possible): |
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""" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids |
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generated_ids = model.generate(input_ids, max_length=500) |
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
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``` |
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Example 2: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1") |
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model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16) |
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text = """### Instruction: |
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Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema. |
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### Input: |
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Here is the database schema that the SQL query will run on: |
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CREATE TABLE taxi ( |
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VendorID bigint, |
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tpep_pickup_datetime timestamp, |
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tpep_dropoff_datetime timestamp, |
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passenger_count double, |
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trip_distance double, |
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fare_amount double, |
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extra double, |
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tip_amount double, |
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tolls_amount double, |
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improvement_surcharge double, |
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total_amount double, |
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); |
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### Question: |
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get all columns ending with _amount from taxi table |
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### Response (use duckdb shorthand if possible):""" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids |
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generated_ids = model.generate(input_ids, max_length=500) |
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
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``` |
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Example 3: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1") |
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model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16) |
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text = """### Instruction: |
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Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema. |
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### Input: |
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Here is the database schema that the SQL query will run on: |
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CREATE TABLE rideshare ( |
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hvfhs_license_num varchar, |
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dispatching_base_num varchar, |
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originating_base_num varchar, |
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request_datetime timestamp, |
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on_scene_datetime timestamp, |
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pickup_datetime timestamp, |
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dropoff_datetime timestamp, |
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trip_miles double, |
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trip_time bigint, |
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); |
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### Question: |
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get longest trip in december 2022 |
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### Response (use duckdb shorthand if possible): |
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""" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids |
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generated_ids = model.generate(input_ids, max_length=500) |
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
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``` |
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For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/DuckDB-NSQL). |
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