This repository is now significantly outdated. You should use the repository at
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.
SQLCoder is a 15B parameter model that slightly outperforms
gpt-3.5-turbo
for natural language to SQL generation tasks on our
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
. You can also use a demo on our website
here
, or run SQLCoder in Colab
here
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
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
Runs of defog sqlcoder on huggingface.co
239
Total runs
-2
24-hour runs
-14
3-day runs
-32
7-day runs
6
30-day runs
More Information About sqlcoder huggingface.co Model
sqlcoder huggingface.co is an AI model on huggingface.co that provides sqlcoder's model effect (), which can be used instantly with this defog sqlcoder model. huggingface.co supports a free trial of the sqlcoder model, and also provides paid use of the sqlcoder. Support call sqlcoder model through api, including Node.js, Python, http.
sqlcoder huggingface.co is an online trial and call api platform, which integrates sqlcoder's modeling effects, including api services, and provides a free online trial of sqlcoder, you can try sqlcoder online for free by clicking the link below.
sqlcoder is an open source model from GitHub that offers a free installation service, and any user can find sqlcoder on GitHub to install. At the same time, huggingface.co provides the effect of sqlcoder install, users can directly use sqlcoder installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
