| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pipeline_tag: text-generation |
| --- |
| |
| # Defog SQLCoder |
| **Updated on Nov 14 to reflect benchmarks for SQLCoder-34B** |
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| Defog's SQLCoder is a state-of-the-art LLM for converting natural language questions to SQL queries. |
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| [Interactive Demo](https://defog.ai/sqlcoder-demo/) | [🤗 HF Repo](https://huggingface.co/defog/sqlcoder-34b-alpha) | [♾️ Colab](https://colab.research.google.com/drive/1z4rmOEiFkxkMiecAWeTUlPl0OmKgfEu7?usp=sharing) | [🐦 Twitter](https://twitter.com/defogdata) |
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| ## TL;DR |
| SQLCoder-34B is a 34B parameter model 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 outperforms all popular open-source models. |
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| SQLCoder-34B is fine-tuned on a base CodeLlama model. |
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| ## Results on novel datasets not seen in training |
| | model | perc_correct | |
| |-|-| |
| | defog-sqlcoder-34b | 84.0 | |
| | gpt4-turbo-2023-11-09 | 82.5 | |
| | gpt4-2023-11-09 | 82.5 | |
| | defog-sqlcoder2 | 77.5 | |
| | gpt4-2023-08-28 | 74.0 | |
| | defog-sqlcoder-7b | 71.0 | |
| | gpt-3.5-2023-10-04 | 66.0 | |
| | claude-2 | 64.5 | |
| | gpt-3.5-2023-08-28 | 61.0 | |
| | claude_instant_1 | 61.0 | |
| | text-davinci-003 | 52.5 | |
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|  |
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| ## 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. |
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| ## 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. |
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| 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 5 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-34b | 80 | 94.3 | 88.6 | 74.3 | 82.9 | 82.9 | |
| | gpt-4 | 68 | 94.3 | 85.7 | 77.1 | 85.7 | 80 | |
| | sqlcoder2-15b | 76 | 80 | 77.1 | 60 | 77.1 | 77.1 | |
| | sqlcoder-7b | 64 | 82.9 | 74.3 | 54.3 | 74.3 | 74.3 | |
| | gpt-3.5 | 68 | 77.1 | 68.6 | 37.1 | 71.4 | 74.3 | |
| | claude-2 | 52 | 71.4 | 74.3 | 57.1 | 65.7 | 62.9 | |
| | claude-instant | 48 | 71.4 | 74.3 | 45.7 | 62.9 | 60 | |
| | gpt-3 | 32 | 71.4 | 68.6 | 25.7 | 57.1 | 54.3 | |
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| <img width="831" alt="image" src="https://github.com/defog-ai/sqlcoder/assets/5008293/79c5bdc8-373c-4abd-822e-e2c2569ed353"> |
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| ## 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. |
| ``` |
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| You can also use a demo on our website [here](https://defog.ai/sqlcoder-demo) |
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| ## 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. |
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| ## Todo |
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| - [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 |
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