Views
No views yet
gpt-oss-20b MoE base model. The controller decides when to switch the active expert set and which experts to activate, using the options framework with deliberation costs. This reduces expert switch rates from >50% to as low as ~1–4%, while retaining most of the base model's accuracy.eta0.02/, eta0.03/, and eta0.04/.activation_controller.pt — per-layer option controller weights (termination head, selection head, value heads)lora/ — LoRA adapter (rank 16, α = 16) applied to attention projections (q/k/v/o) and expert MLPs (gate_up_proj, down_proj)gpt-oss-20b with the custom transformers patches (see install.sh).eta{X}/lora/.eta{X}/activation_controller.pt.1@article{shen2025temporally,
2 title={Temporally Extended Mixture-of-Experts Models},
3 author={Shen, Zeyu and Henderson, Peter},
4 year={2025}
5}