# sqlcoder

**Repository Path**: seven_ruan/sqlcoder

## Basic Information

- **Project Name**: sqlcoder
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-23
- **Last Updated**: 2025-02-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Defog SQLCoder
Defog's SQLCoder is a family of state-of-the-art LLMs for converting natural language questions to SQL queries.

[Interactive Demo](https://defog.ai/sqlcoder-demo/) | [🤗 HF Repo](https://huggingface.co/defog/llama-3-sqlcoder-8b) | [♾️ Colab](https://colab.research.google.com/drive/1z4rmOEiFkxkMiecAWeTUlPl0OmKgfEu7?usp=sharing) | [🐦 Twitter](https://twitter.com/defogdata)

## TL;DR
SQLCoder is a family of large language models that outperforms `gpt-4` and `gpt-4-turbo` for natural language to SQL generation tasks on our [sql-eval](https://github.com/defog-ai/sql-eval) framework, and significantly outperform all popular open-source models.

![Percentage of correctly generated SQL queries on novel schemas not seen in training (n = 200), with 4 beams (2)](https://github.com/defog-ai/sqlcoder/assets/5008293/22b891db-2201-4b30-a52d-22376ba8ec86)


## Installing SQLCoder
If running on a device with an NVIDIA GPU with more than 16GB VRAM (best performance)
`pip install "sqlcoder[transformers]"`

If running on Apple Silicon (less good performance, because of quantization and lack of beam search)
`CMAKE_ARGS="-DLLAMA_METAL=on" pip install "sqlcoder[llama-cpp]"`

If running on a non-apple silicon computer without GPU access, please run this on Linux/Intel Mac
`CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install "sqlcoder[llama-cpp]"`

And run this on Windows
```bash
$env:CMAKE_ARGS = "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS"
pip install "sqlcoder[llama-cpp]"
```

SQLCoder has not been tested on other platforms yet. Contributions for testing on other platforms are very welcome!

## Running SQLCoder
In your terminal, run
`sqlcoder launch`

With this, you will be able to connect straight to your database, so you can add your metadata and query it visually.

## License
The code in this repo (what little there is of it) is Apache-2 licensed. The model weights have a `CC BY-SA 4.0` license. The TL;DR is that you can use and modify the model for any purpose – including commercial use. However, if you modify the weights (for example, by fine-tuning), you must open-source your modified weights under the same license terms.

## Training
Defog was trained on more than 20,000 human-curated questions. These questions were based on 10 different schemas. None of the schemas in the training data were included in our evaluation framework. 

You can read more about our [training approach](https://defog.ai/blog/open-sourcing-sqlcoder2-7b/) and [evaluation framework](https://defog.ai/blog/open-sourcing-sqleval/).

## Results by question category
We classified each generated question into one of 6 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.
|                | date | group_by | order_by | ratio | join | where |
| -------------- | ---- | -------- | -------- | ----- | ---- | ----- |
| sqlcoder-70b   | 96   | 91.4     | 97.1     | 85.7  | 97.1 | 91.4  |
| sqlcoder-7b-2  | 96   | 91.4     | 94.3     | 91.4  | 94.3 | 77.1  |
| sqlcoder-34b   | 80   | 94.3     | 85.7     | 77.1  | 85.7 | 80    |
| gpt-4          | 72   | 94.3     | 97.1     | 80    | 91.4 | 80    |
| gpt-4-turbo    | 76   | 91.4     | 91.4     | 62.8  | 88.6 | 77.1  |
| natural-sql-7b | 56   | 88.6     | 85.7     | 60    | 88.6 | 80    |
| sqlcoder-7b    | 64   | 82.9     | 74.3     | 54.3  | 74.3 | 74.3  |
| gpt-3.5        | 72   | 77.1     | 82.8     | 34.3  | 65.7 | 71.4  |
| claude-2       | 52   | 71.4     | 74.3     | 57.1  | 65.7 | 62.9  |

## Using SQLCoder
You can use SQLCoder via the `transformers` library by downloading our model weights from the Hugging Face repo. We have added sample code for [inference](./inference.py) on a [sample database schema](./metadata.sql). 
```bash
python inference.py -q "Question about the sample database goes here"

# Sample question:
# Do we get more revenue from customers in New York compared to customers in San Francisco? Give me the total revenue for each city, and the difference between the two.
```

You can also use a demo on our website [here](https://defog.ai/sqlcoder-demo)

## Hardware Requirements
SQLCoder-34B has been tested on a 4xA10 GPU with `float16` weights. You can also load an 8-bit and 4-bit quantized version of the model on consumer GPUs with 20GB or more of memory – like RTX 4090, RTX 3090, and Apple M2 Pro, M2 Max, or M2 Ultra Chips with 20GB or more of memory.

## Todo

- [x] Open-source the v1 model weights
- [x] Train the model on more data, with higher data variance
- [ ] Tune the model further with Reward Modelling and RLHF
- [ ] Pretrain a model from scratch that specializes in SQL analysis

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=defog-ai/sqlcoder&type=Date)](https://star-history.com/#defog-ai/sqlcoder&Date)