Comments (3)
VassilisVassiliadis
commented on July 25, 2024
I can take a stab at the above if you like!
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VassilisVassiliadis
commented on July 25, 2024
Ideally I'd also like to measure the time it takes to load the model so that I can predict how long it would take to instantiate the weights that I produce. This would probably involve returning both the output of trainer.train() plus some extra information that the sft_trainer.train method measures (e.g. model_load_time)
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dushyantbehl
commented on July 25, 2024
@VassilisVassiliadis this can be closed now...if you run into any error when testing it let me know.
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Related Issues (20)
- feat: Add custom logging operation
- bug: Test case for “loss not available”
- feat: Add metric for PerProcessState
- feat: Extend trainer controller capabilities to have fine-grained control in multi-node-multi-gpu scenario
- bug: Modify compute() function signature in test case to make arguments unnamed
- bug: Modify should_perform_action_xyz() signature in test case to make arguments unnamed
- bug: Modify should_() signature in test case to make arguments unnamed
- bug: conditionals should use “is” instead of ==
- bug: Assert string formats in testcases
- bug: Added comma after argument in `TrainerControllerCallback`
- bug: Pass kwargs to __init__ within derived controls and change operations constructor to accept name and kwargs
- feat: support custom tokenizers
- feat: support pretokenised datasets HOT 1
- feat: support adding attention mask when not present for pretokenized data
- feat: resizing the embedding layer to the power of 2 or multiple of 8
- feat: use native python logger instead of transformers logger
- feat: Allow specifying logging level from the CLI HOT 1
- fix: freeze to specific set of versions with reliable tighter lower and upper bounds. HOT 2
- Upgrading to TRL>0.9 Causes Incompatibility with Extended TrainingArguments HOT 1
- NCCL ENV support
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