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--- |
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license: other |
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language: |
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- en |
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metrics: |
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- code_eval |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- code |
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inference: false |
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--- |
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# ARCHIVE NOTICE |
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This repository is now significantly outdated. You should use the repository at [sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) instead. It is significantly better and consumes fewer GPU resources. |
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# Defog SQLCoder |
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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) | [♾️ Colab](https://colab.research.google.com/drive/1z4rmOEiFkxkMiecAWeTUlPl0OmKgfEu7) | [🐦 Twitter](https://twitter.com/defogdata) |
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## TL;DR |
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SQLCoder is a 15B parameter model that slightly outperforms `gpt-3.5-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. It also significantly outperforms `text-davinci-003`, a model that's more than 10 times its size. |
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SQLCoder is fine-tuned on a base StarCoder model. |
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## Results on novel datasets not seen in training |
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| model | perc_correct | |
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| gpt-4 | 74.3 | |
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| defog-sqlcoder | 64.6 | |
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| gpt-3.5-turbo | 60.6 | |
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| defog-easysql | 57.1 | |
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| text-davinci-003 | 54.3 | |
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| wizardcoder | 52.0 | |
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| starcoder | 45.1 | |
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## License |
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The model weights have a `CC BY-SA 4.0` license, with OpenRAIL-M clauses for responsible use attached. 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 `CC BY-SA 4.0` license terms. |
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## Training |
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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. |
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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. |
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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. |
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## Results by question category |
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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. |
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| query_category | gpt-4 | defog-sqlcoder | gpt-3.5-turbo | defog-easy | text-davinci-003 | wizard-coder | star-coder | |
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|-|-|-|-|-|-|-|-| |
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| group_by | 82.9 | 77.1 | 71.4 | 62.9 | 62.9 | 68.6 | 54.3 | |
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| order_by | 71.4 | 65.7 | 60.0 | 68.6 | 60.0 | 54.3 | 57.1 | |
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| ratio | 62.9 | 57.1 | 48.6 | 40.0 | 37.1 | 22.9 | 17.1 | |
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| table_join | 74.3 | 57.1 | 60.0 | 54.3 | 51.4 | 54.3 | 51.4 | |
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| where | 80.0 | 65.7 | 62.9 | 60.0 | 60.0 | 60.0 | 45.7 | |
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## Using SQLCoder |
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You can use SQLCoder via the `transformers` library by downloading our model weights from the HuggingFace repo. We have added sample code for inference [here](./inference.py). You can also use a demo on our website [here](https://defog.ai/sqlcoder-demo), or run SQLCoder in Colab [here](https://colab.research.google.com/drive/13BIKsqHnPOBcQ-ba2p77L5saiepTIwu0#scrollTo=ZpbVgVHMkJvC) |
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## Hardware Requirements |
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SQLCoder has been tested on an A100 40GB GPU with `bfloat16` weights. You can also load an 8-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 |
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- [ ] Train the model on more data, with higher data variance |
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- [ ] Tune the model further with Reward Modelling and RLHF |
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- [ ] Pretrain a model from scratch that specializes in SQL analysis |
