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name: lora_efficiency_evaluation description: Evaluate LoRA parameter efficiency, rank constraints, merge checks, and proxy recovery metrics.

Lora Efficiency Evaluation

When to use

Use this skill to compare LoRA trainable parameter counts with full fine-tuning and to validate a reduced recovery trace. It should be used after a LoRA run has produced loss and parameter evidence.

Inputs

• Base matrix or named parameters relevant to the module.
• LoRA rank r, scaling alpha, and small task examples when training or evaluation is needed.

Outputs

• Deterministic JSON-serializable mechanism checks.
• Updated LoRA parameters or validation metrics, depending on the module.

Workflow

1. Keep pretrained/base weights immutable unless explicitly performing a merge copy for inference.
2. Represent the adaptation as delta_W = (alpha / r) * B @ A.
3. Verify that only LoRA matrices are treated as task-specific trainable parameters.
4. For recovery, log rank, loss before and after, parameter changes, and merge equivalence.

Validation

Run python -m pytest tests or validate the skill tree with the Distiller module-to-skill validator. The included tests use only the Python standard library.

Limitations

This skill provides the reusable mechanism; full paper benchmark scores still require real pretrained models, datasets, and training infrastructure.