đ Defog SQLCoder
Defog's SQLCoder is a cutting - edge large language model (LLM) designed to convert natural language questions into SQL queries, providing a powerful solution for data - related tasks.
Interactive Demo | đ¤ HF Repo | âžī¸ Colab | đĻ Twitter
đ Quick Start
SQLCoder-34B is a 34B parameter model. On our sql-eval framework, it outperforms gpt-4
and gpt-4-turbo
in natural language to SQL generation tasks and significantly surpasses all popular open - source models. It is fine - tuned on a base CodeLlama model.
⨠Features
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 |

Results by question category
We classified each generated question into one of 5 categories. The table below shows 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 |
đĻ Installation
No specific installation steps are provided in the original README.
đģ Usage Examples
You can use SQLCoder via the transformers
library by downloading our model weights from the Hugging Face repo. Here is the sample code for inference on a sample database schema.
python inference.py -q "Question about the sample database goes here"
You can also use a demo on our website here
đ Documentation
Training
Defog was trained on more than 20,000 human - curated questions, which 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 and evaluation framework.
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 â 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
đ 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. In short, you can use and modify the model for any purpose â 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.