repralign
repralign is a reusable Python toolkit for layer-wise representation alignment analysis in multimodal models.
It is designed for workflows where you want to:
- extract intermediate features from candidate and reference models
- normalize or pool those features into comparable representations
- compute layer-wise similarity with metrics such as CKA and CKNNA
- save CSV, JSON, and PNG outputs for later inspection
What It Supports Today
- forward-hook based feature extraction from configurable layers
- pooling modes:
cls,mean_tokens,flatten_mean - linear CKA and neighbor-overlap CKNNA
- batched extraction across tensor batches or image datasets
- semantic reference workflows through Hugging Face vision encoders
- generation-reference workflows through Diffusers pipelines such as SD3
- CLI commands for listing layers, extracting features, running analysis, and plotting
What It Is Good For
repralign is a good fit when you want to run paper-style alignment studies such as:
- candidate model vs semantic reference model
- candidate model vs generation-oriented reference model
- layer-wise similarity curves across a representation encoder
TUNA-Style Analysis Status
repralign can now support the same class of analysis workflow as the TUNA representation-alignment section:
- compare one candidate model against multiple reference models
- compute layer-wise similarity curves
- use semantic and generation-oriented references in the same experiment
What it does not provide automatically is a turnkey, official TUNA experiment package. To run a TUNA-like study in practice, you still need to supply:
- the actual candidate model adapter
- the exact layer selections you want to analyze
- the chosen semantic reference model
- the chosen generation reference model
- the dataset and preprocessing settings for your experiment
So the current status is:
- the analysis framework is ready
- a TUNA-style experiment is feasible
- exact paper-matching setup still depends on your model-specific configuration