Distill or prune model to save training time
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optimization
research
- Dominant language
- Python
- Stars
- 39
- Forks
- 7
- Avg merge
- 1d 9h
- Merged PRs (30d)
- 5
Description
If we can distill or prune the NLLB-200 shortly after starting fine tuning, we may be able to dramatically reduce (50% or more) the training and inferencing time needed. It could even do something like this:
- Take the 3.3GB model and train for 1000 steps on the 2x A100's. Prune and save.
- Load the model on a single A100 and finish training and inferencing.
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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.
- Open a pull request that references the issue number.
Research direction
The issue names no files, tests, or entry points. Start by locating the NLLB-200 fine-tuning pipeline and its model save/load flow, then investigate how a 1,000-step run on two A100s could be followed by pruning or distillation. Done should include a working experiment and measured reductions in training and inference time.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100