Defog's SQLCoder is a state-of-the-art LLM for converting natural language questions to SQL queries.
Interactive Demo | 🤗 HF Repo | ♾️ Colab | 🐦 Twitter
SQLCoder is a 15B parameter model that outperforms gpt-3.5-turbo for natural language to SQL generation tasks on our sql-eval framework, and significantly outperforms all popular open-source models. It also significantly outperforms text-davinci-003, a model that's more than 10 times its size.
SQLCoder is fine-tuned on a base StarCoder model.
| model | perc_correct |
|---|---|
| gpt-4 | 74.3 |
| defog-sqlcoder | 64.6 |
| gpt-3.5-turbo | 60.6 |
| defog-easysql | 57.1 |
| text-davinci-003 | 54.3 |
| wizardcoder | 52.0 |
| starcoder | 45.1 |
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.
Defog was trained on 10,537 human-curated questions across 2 epochs. These questions were based on 10 different schemas. None of the schemas in the training data were included in our evaluation framework.
Training happened in 2 phases. The first phase was on questions that were classified as "easy" or "medium" difficulty, and the second phase was on questions that were classified as "hard" or "extra hard" difficulty.
The results of training on our easy+medium data were stored in a model called defog-easy. We found that the additional training on hard+extra-hard data led to a 7 percentage point increase in performance.
You can read more about our training approach and evaluation framework.
We classified each generated question into one of 5 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.
| query_category | gpt-4 | defog-sqlcoder | gpt-3.5-turbo | defog-easy | text-davinci-003 | wizard-coder | star-coder |
|---|---|---|---|---|---|---|---|
| group_by | 82.9 | 77.1 | 71.4 | 62.9 | 62.9 | 68.6 | 54.3 |
| order_by | 71.4 | 65.7 | 60.0 | 68.6 | 60.0 | 54.3 | 57.1 |
| ratio | 62.9 | 57.1 | 48.6 | 40.0 | 37.1 | 22.9 | 17.1 |
| table_join | 74.3 | 57.1 | 60.0 | 54.3 | 51.4 | 54.3 | 51.4 |
| where | 80.0 | 65.7 | 62.9 | 60.0 | 60.0 | 60.0 | 45.7 |
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 on a sample database schema.
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, or run SQLCoder in Colab here
SQLCoder has been tested on an A100 40GB GPU with bfloat16 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.
- documentation of our pruning algorithm that prunes the size of your metadata schema to just the relevant columns
- ggml-quantized model that you can run on a Macbook
- Open-source the v1 model weights
- 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