microsoft / microsoft/TransformerCompression
adapt the lm_eval tool
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- Dominant language
- Python
- Stars
- 460
- Forks
- 58
- Avg merge
- 8h 40m
- Merged PRs (30d)
- 1
Description
How to adapt the lm_eval tool to the model after opt rotation and pruning in the paper
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First steps
- Read the whole issue, then the project's contributing guide.
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- Fork the repository and make your change on a branch.
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Research direction
The issue mentions adapting the lm_eval tool after OPT rotation and pruning, but names no files, tests, or entry points. Start by locating the lm_eval integration and the paper's rotation and pruning implementation, then determine the expected evaluation path. Done should mean the adapted tool evaluates the transformed model correctly, with validation covering the relevant model state.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100