tm243 / CodeT5-KDE

CodeT5 LLM fine-tuned with C++ code from KDE

CodeT5 KDE

This model is a CodeT5 model fine-tuned with an example dataset from KDE-C++ code. You can find the dataset here:

https://www.opendocstring.com/tool/

and select full-dataset-KDE-kdeconnect-C++

You are encouraged to improve and extend the dataset.

Demo

Follow this link to try out the live model: https://www.opendocstring.com/#demo

Setup

Make yourself a folder and install the required Python packages:

virtualenv .env

source .env/bin/activate

pip install -r requirements.txt

Before you run the model you need to download the weights:

wget https://www.opendocstring.com/downloads/weights/codet5/saved-pretrained-kde-cpp-multisum-2023-05-10-06.tar.gz

and unpack them. Or use the script:

./download_weights.sh

The weights will be in api/saved-pretrained-kde-...

Run Inference

Inference Test

A python code example for inference:

python inference.py

Local Server

You can connect to this model via the REST api.

Run the local server:

uvicorn api.rest:app --port 7999 --reload

Make a POST request to get the summary of some code.

Browser

Open demo.html in your browser and paste some code. It will make requests to the local server you just started.

Python

import requests

result = requests.post('http://localhost:7999/summary', json={ 'code' : code })
summary = json.loads(result.text)['summary']