miStudio: Mechanistic Interpretability Workbench
An end-to-end workbench for training, understanding, and using mechanistic-interpretability sparse autoencoders (SAEs) — with HuggingFace and Neuronpedia integration and real-time model steering.
miStudio is an end-to-end mechanistic interpretability platform built to replace the fragmented tooling that AI-safety research usually depends on. The work of understanding what happens inside a model — training sparse autoencoders, discovering features, labeling them, and proving what they do — is normally scattered across Jupyter notebooks and one-off scripts. miStudio consolidates all of it into a single, database-backed workbench, so researchers can go from a hypothesis to a proven intervention in a fraction of the usual time.
The research pipeline
miStudio supports the full sparse-autoencoder (SAE) workflow in one place:
- Select and pull models & datasets — direct HuggingFace integration brings models and data into the environment without extra manual steps.
- Train SAEs — train new sparse autoencoders against the model activations you care about.
- Discover features — surface the interpretable features the SAE has learned.
- Label features — annotate what each feature appears to mean.
- Test with causal intervention — confirm a feature’s role by intervening on it and observing the effect, not just correlating.
- Export — push results back out for sharing and further analysis.
Because the platform is database-backed, every experiment is tracked — no more guessing which notebook produced which result.
Real-time steering
A standout capability is robust, real-time steering. miStudio lets you steer model behavior across multiple features, multiple strengths, and multiple prompts at once — the practical way to test and refine hypotheses about what a feature actually means. Instead of theorizing about a feature in isolation, you can watch how amplifying or suppressing it changes real generations.
Built to connect
miStudio is designed to fit into the wider interpretability ecosystem:
- HuggingFace — pull models and datasets in; push newly trained SAEs back out for the community to use.
- Neuronpedia — push labeling results directly into a Neuronpedia instance for deeper analysis and collaboration.
A companion server: miLLM
miStudio pairs with MechInterp LLM Server (miLLM). Each application stands on its own, but together they form a complementary workflow: miStudio handles the discovery, training, labeling, testing, and export of interpretable features, while miLLM puts those features to work in a serving context.
Explore miStudio
- Documentation — The Researcher’s Journey | miStudio Manual
- Overview video — MechInterp Studio on YouTube
- YouTube channel — @miStudio-hitsai
- Source code — github.com/hitsainet/miStudio
