OpenEuroLLM / OpenEuroLLM/Taskboard
Data mixture comparison
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Description
Goal
Following a discussion in model exploration meeting, we wanted to raise data mixure comparison, to understand the effect of mix changes.
The approach: Pareto front points to select to measure effect of changed dataset mix
Description
(More details about the issue)
Deliverable scope
(What is the expected outcome for this issue to be complete)
Dependencies
(Blocking issues, PRs, or external factors)
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the model exploration meeting discussion referenced in the issue; no files, tests, or entry points are named. Clarify the dataset mixes, Pareto-front selection method, evaluation criteria, and required deliverable before implementation, since completion is not defined in the current issue.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100