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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.
