https://github.com/defog-ai/sqlcoder
SoTA LLM for converting natural language questions to SQL queries
https://github.com/defog-ai/sqlcoder
Last synced: 5 days ago
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SoTA LLM for converting natural language questions to SQL queries
- Host: GitHub
- URL: https://github.com/defog-ai/sqlcoder
- Owner: defog-ai
- License: apache-2.0
- Created: 2023-08-17T21:19:32.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-23T03:06:26.000Z (11 months ago)
- Last Synced: 2025-04-03T22:37:09.588Z (13 days ago)
- Language: Jupyter Notebook
- Size: 1.39 MB
- Stars: 3,693
- Watchers: 36
- Forks: 238
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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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.
## 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
[](https://star-history.com/#defog-ai/sqlcoder&Date)