-
Notifications
You must be signed in to change notification settings - Fork 530
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add registry for ICL datasets #1252
Conversation
Hi @sanjari-orb could you please add a PR description describing the change? Thank you! |
Hi @dakinggg Sorry this was still a draft because I was still trying to get it to work. But thanks for the comments, I'll take them into account and update the PR soon. |
No worries, thanks for the contribution! |
Hi @dakinggg could you point me to the steps to run the unit tests locally? |
Please see the makefile here (https://github.com/mosaicml/llm-foundry/blob/main/Makefile). Sorry there aren't better instructions! CPU testsmake test Multi CPU testsmake test-dist Single GPU testsmake test-gpu Multi GPU testsmake test-dist-gpu |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks! Had a couple comments. Also, once the tests are passing, I'll run this PR through our regression tests to check and make sure we aren't accidentally changing any evals.
646813a
to
1e37fd7
Compare
3b48ace
to
1981981
Compare
@dakinggg Is there a linter I can use to fix the code quality checks? |
@sanjari-orb yeah, running |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Successful regression test run name: llm-foundry-regression-tests-runner-ActFp3
LGTM, thank you for the contribution!
@dakinggg I'm not sure why the PR GPU tests failed: https://github.com/mosaicml/llm-foundry/actions/runs/9514022193/job/26225300100?pr=1252. Could you take a look? |
Purpose of PR: Create a registry for ICL eval dataset types. This will allow users to create custom in-context learning datasets and add them to the registry to run custom ICL evaluations during training.