Charlie Doern 0751a960a5 feat: make training config fields optional (#1861) ... # What does this PR do? Today, supervised_fine_tune itself and the `TrainingConfig` class have a bunch of required fields that a provider implementation might not need. for example, if a provider wants to handle hyperparameters in its configuration as well as any type of dataset retrieval, optimizer or LoRA config, a user will still need to pass in a virtually empty `DataConfig`, `OptimizerConfig` and `AlgorithmConfig` in some cases. Many of these fields are intended to work specifically with llama models and knobs intended for customizing inline. Adding remote post_training providers will require loosening these arguments, or forcing users to pass in empty objects to satisfy the pydantic models. Signed-off-by: Charlie Doern <cdoern@redhat.com>		2025-04-12 01:13:45 -07:00
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__init__.py	API Updates (#73)	2024-09-17 19:51:35 -07:00
post_training.py	feat: make training config fields optional (#1861)	2025-04-12 01:13:45 -07:00