Defog’s SQLCoder is a state-of-the-art LLM for converting natural language questions to SQL queries.
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 framework, and significantly outperforms all popular open-source models.
https://huggingface.co/defog/sqlcoder-34b-alpha
SQLCoder-34B is fine-tuned on a base CodeLlama model.
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%
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.
You can read more about our training approach and evaluation framework.
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.
Deepseek-coder-33b-instruct is current open source coding SOTA?
I’m interested to know how people are using it, because someone mentioned that it was a Llama 1 with 2048 context, and generally for development I need a lot more than that. I’m not sure how I’d be able to make use of a 2048 context coding model. :(
DeepSeek is not based on any llama training, it’s a 2T token pretrain of their own. 16k context. All this info is at the top of their model card.
Ahhh I apologize, I didn’t realize that it’s a totally new base model, and not built off of any Llama base. I could have sworn I read that it was, but it would appear I was incorrect.
Thanks!