Qwen3.6-27B sparse autoencoders go public

This is a solid interpretability release, not a consumer-facing product. The value is in making Qwen3.6-27B easier to inspect, probe, and potentially steer with feature-level tooling instead of opaque activations.

• –Sparse autoencoders are used to decompose model internals into features people can label, visualize, and edit
• –The release includes public weights in `sae_lens` format, so researchers can plug them into existing interpretability workflows
• –Training on three layers gives coverage across early, middle, and late residual streams, which matters because feature structure shifts by depth
• –The stated metrics and caveats are useful: the author is positioning this as reproducible research infrastructure, not a polished benchmark victory lap
• –For developers, the practical use case is debugging behavior, building feature dashboards, and experimenting with causal interventions on the model