SQLCoder-7B is a 7B parameter model that 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. When fine-tuned on a given schema, it also outperforms
gpt-4
SQLCoder-7B is fine-tuned on a base Mistral-7B model.
Results on novel datasets not seen in training
model
perc_correct
gpt4-2023-10-04
82.0
defog-sqlcoder2
74.5
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
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. 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 license terms.
Training
SQLCoder 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.
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
sqlcoder2-15b
sqlcoder-7b
gpt-3.5
claude-2
claude-instant
gpt-3
date
72
76
64
68
52
48
32
group_by
91.4
80
82.9
77.1
71.4
71.4
71.4
order_by
82.9
77.1
74.3
68.6
74.3
74.3
68.6
ratio
80
60
54.3
37.1
57.1
45.7
25.7
join
82.9
77.1
74.3
71.4
65.7
62.9
57.1
where
80
77.1
74.3
74.3
62.9
60
54.3
Using SQLCoder
You can use SQLCoder via the
transformers
library by downloading our model weights from the Hugging Face repo. We have added sample code for
inference
on a
sample database schema
.
python inference.py -q "Question about the sample database goes here"# Sample question:# Do we get more revenue from customers in New York compared to customers in San Francisco? Give me the total revenue for each city, and the difference between the two.
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 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.
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-7b on huggingface.co
150
Total runs
3
24-hour runs
2
3-day runs
-8
7-day runs
27
30-day runs
More Information About sqlcoder-7b huggingface.co Model
sqlcoder-7b huggingface.co is an AI model on huggingface.co that provides sqlcoder-7b's model effect (), which can be used instantly with this defog sqlcoder-7b model. huggingface.co supports a free trial of the sqlcoder-7b model, and also provides paid use of the sqlcoder-7b. Support call sqlcoder-7b model through api, including Node.js, Python, http.
sqlcoder-7b huggingface.co is an online trial and call api platform, which integrates sqlcoder-7b's modeling effects, including api services, and provides a free online trial of sqlcoder-7b, you can try sqlcoder-7b online for free by clicking the link below.
defog sqlcoder-7b online free url in huggingface.co:
sqlcoder-7b is an open source model from GitHub that offers a free installation service, and any user can find sqlcoder-7b on GitHub to install. At the same time, huggingface.co provides the effect of sqlcoder-7b install, users can directly use sqlcoder-7b installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
