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README.md

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+
+---
+
+license: other
+language:
+- en
+metrics:
+- code_eval
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- code
+inference: false
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/sqlcoder-GGUF
+This is quantized version of [defog/sqlcoder](https://huggingface.co/defog/sqlcoder) created using llama.cpp
+
+# Original Model Card
+
+# ARCHIVE NOTICE
+This repository is now significantly outdated.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.
+
+# Defog SQLCoder
+Defog's SQLCoder is a state-of-the-art LLM for converting natural language questions to SQL queries.
+
+[Interactive Demo](https://defog.ai/sqlcoder-demo) | [♾️ Colab](https://colab.research.google.com/drive/1z4rmOEiFkxkMiecAWeTUlPl0OmKgfEu7) | [🐦 Twitter](https://twitter.com/defogdata)
+
+## TL;DR
+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.
+
+SQLCoder is fine-tuned on a base StarCoder model.
+
+## Results on novel datasets not seen in training
+| 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 |
+
+## License
+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.
+
+## Training
+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.
+
+## 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.
+| 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 |
+
+## Using SQLCoder
+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)
+
+## Hardware Requirements
+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.
+
+## Todo
+
+- [x] 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