edmicho / mm-probe-kit

What is mm-probe-kit?

mm-probe-kit is a Python toolkit for inspecting multimodal large language models. It gives you utilities to peek inside vision-language systems — examining attention patterns, extracting hidden states across layers, measuring text-image alignment, and running causal tracing experiments. Instead of rewriting the same hook-and-cache boilerplate every time you want to study a model like LLaVA or Qwen-VL, you get a clean wrapper plus probe functions that do the heavy lifting. The package includes a CLI for running end-to-end probing from a config file, plus matplotlib helpers for overlaying attention maps on images.

Why is it gaining traction?

Most interpretability tooling assumes decoder-only language models. Multimodal models add a vision encoder and connector layer, and the interesting behavior often happens across those boundaries — something existing tools make painful to study. mm-probe-kit bridges that gap with model-specific wrappers that handle the quirks of different architectures, plus standard probes for attention rollout, entropy, and cross-modality alignment. The "hackable" claim in the description is genuine: probes are plain Python functions you can copy into a notebook and modify. No framework overhead, no abstraction layers to fight through.

Who should use this?

This is built for ML researchers and interpretability practitioners who work with multimodal models and want to run quick experiments without scaffolding a custom pipeline. If you're comparing attention patterns across LLaVA, Qwen-VL, or BLIP-2 families, or need to patch activations for causal tracing, this saves real setup time. Academic papers on modality alignment or feature probing would benefit from the standard metrics it provides. Not for production use — it's experimental tooling with minimal test coverage.

Verdict

At 37 stars with a 0.90 credibility score, this is a small, promising project from an individual developer. The code is clean and focused, but maturity is low — limited test coverage and a sparse README with no example notebooks. Worth trying if you need quick multimodal probing for research, but treat it as a starting point rather than a stable dependency. The hackability is the real value here: copy what you need, adapt the rest.