[ github ] Introduce github models action workflow for summarizing Pull Requests #3583
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- For easier code review for reviewers, adding `ai-describe` tag will evoke AI-generated summary of the Pull Request. - Current backend model is gpt-4o-mini - Model will automatically select additional context files in markdown, considering which changes made in the commits in the PR. - Model will refer to commti messages, code diffs and context file, and formulate a simple summary in guided format in prompt.md - Note that this generated file is only a AI-generated file, therefore needs to be reviewed with caution **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <[email protected]>
- Simple and intuitive **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <[email protected]>
skykongkong8
requested review from
DonghakPark,
EunjuYang,
SeoHyungjun,
again4you,
anyj0527,
baek2sm,
djeong20,
dkjung,
gichan-jang,
haehun,
jaeyun-jung,
jihochu,
jijoongmoon,
leemgs,
lhs8928,
myungjoo,
songgot and
wooksong
as code owners
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- Fix module classifier for nntrainer paths - Structured comments - Remove dummy workflow **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: skykongkong8 <[email protected]>
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This PR do something like: skykongkong8#50
🎯 What & Why
This pull request introduces new AI-generated PR description capabilities for the nntrainer project. It includes documentation and structure to facilitate automated generation of PR summaries based on changes made, aiming to enhance the review process. The main motivation is to streamline PR reviews by providing concise summaries that capture the essence of changes across various modules of the repository.
💡 Key Changes
🔎 Pointers for Reviewer
This review will play a critical role in ensuring the intended improvements facilitate effective and efficient code reviews while maintaining the quality of the nntrainer codebase!😁