feat: make training config fields optional

[cdoern]

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.