# SMAC3 Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/smac3
> Markdown URL: https://aitinkerers.org/technologies/smac3.md
> Technology record last updated: 2026-02-25T10:19:04Z
> Generated: 2026-09-22T22:38:09Z

SMAC3 is a high-performance Python tool for Bayesian hyperparameter optimization and algorithm configuration.

SMAC3 (Sequential Model-based Algorithm Configuration) is a robust Python framework for hyperparameter optimization and general algorithm tuning. Built by the AutoML group at the University of Freiburg, it uses a Random Forest surrogate model to navigate complex search spaces. This architecture handles diverse parameter types (categorical, continuous, and conditional) more effectively than standard Gaussian Processes. You can deploy SMAC3 to optimize deep learning learning rates or fine-tune SAT solver heuristics: it balances exploration and exploitation to find peak performance across high-dimensional landscapes.

- Official technology site: https://github.com/automl/SMAC3
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [AutoRAG](https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s)

Automatic hyperparameter optimization of your retrieval augmented generation pipeline. Stop fiddling around manually with chunk size, top k and so on. Let the machine do the tuning of your retrieval settings. Inspired by hyperparameter optimization in conventional ML.

- Event context: AI Tinkerers Berlin - March 21 — 2024-03-21 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_4eSc1tq148s

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