Scheduler implementation of Continual Pre-Training of Large Language Models: How to (re)warm your model? #1273
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winglian
reviewed
Feb 9, 2024
README.md
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| @@ -797,6 +797,7 @@ early_stopping_patience: 3 | |||
| lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine | |||
| lr_scheduler_kwargs: | |||
| cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr | |||
| cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step | |||
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Would be nice to have a link to the arxiv paper referenced here too
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i put paper link to it.
winglian
reviewed
Feb 12, 2024
thank you i add in arguments |
winglian
approved these changes
Feb 12, 2024
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Scheduler implementation of Continual Pre-Training of Large Language Models: How to (re)warm your model? (https://arxiv.org/pdf/2308.04014.pdf)
Description
almost identical to consine min lr but it adds constant ratio which freezes lr at some percent of training step.
Motivation and Context
This scheduler has been proven for continual pretraining.
How has this been tested?
Made test code
Screenshots (if appropriate)
Types of changes
Social Handles (Optional)