forked from phoenix-oss/llama-stack-mirror
0751a960a5
# 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> |
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| .. | ||
| apis | ||
| cli | ||
| distribution | ||
| models | ||
| providers | ||
| strong_typing | ||
| templates | ||
| __init__.py | ||
| env.py | ||
| log.py | ||
| schema_utils.py | ||