[288 of 512 routed experts kept per layer - 44% of experts pruned] from inclusionAI/Ling-3.0-flash (124B total / 5.1B active).

Method: one-shot REAP (Router-weighted Expert Activation Pruning) - experts scored by router-gate-value × output-L2-norm over calibration data, lowest-scoring deleted. No fine-tuning, no recovery training.

Calibration: 1M tokens, 50/25/25 ultrachat / wikitext / code

BF16 safetensors. Loads with trust_remote_code=True (custom bailing_hybrid / BailingMoeV3 code). Research artifact - quantized builds live in the sibling -GGUF repo.

2026-08-21: chat_template.jinja updated to support reasoning_effort (low = no thinking, high = thinking, default unchanged).