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Bioaligned/Qwen3-30B-A3B-CoupledWelfare-merged
Qwen3-30B-A3B-CoupledWelfare-merged is a machine learning model from Bioaligned. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Merged Qwen3-30B-A3B-Instruct-2507 (128-expert MoE) + coupled-welfare CPT (attention+router LoRA, r64 — the restricted variant; full all-linear was impractical on one card). Loadable via AutoModelForCausalLM.frompretr…
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From the Hugging Face model README
Merged Qwen3-30B-A3B-Instruct-2507 (128-expert MoE) + coupled-welfare CPT
(attention+router LoRA, r64 — the restricted variant; full all-linear was impractical on one card).
Loadable via AutoModelForCausalLM.from_pretrained("Bioaligned/Qwen3-30B-A3B-CoupledWelfare-merged").
A generation pressure-ladder spot-check (admissible, parse-fail=0, 22 irreversible scenarios) shows the coupled-welfare adapter moves the tail only weakly: high-pressure breaking-rate L4 .682 → .636 (≈ flat) and L5 .909 → .727 (modest). An earlier forced-choice proxy suggested a dramatic drop (L4 → .045), but that was a proxy artifact for this thinking-model MoE — the adapter shifts the immediate answer-token preference, which washes out once the model reasons through the scenario. Real generation is the truth here.
Interpretation: the restricted attn+router lever (experts frozen) produced a mostly-surface effect on this MoE, far weaker than the fully-validated dense models. A faithful MoE intervention would require full fine-tuning (all experts, multi-GPU). Capability ≈ base. Use the dense Qwen3-32B-CoupledWelfare (tail moved .636→.273 under generation, +10pp MMLU) for a validated bioaligned model.
Methodological note: the forced-choice proxy is validated vs. generation on dense 7B but NOT on this thinking-model MoE — always confirm MoE/thinking-model results with generation.