๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

OrcaRouter ๐Ÿณ
@OrcaRouter
The AI Gateway built for Privacy & Security ๐Ÿณ 0% Markup. BYOK. Adaptive Routing. Firewall. Guardrails. Observability. Cyber. โ†’
๊ฐ€์ž… April 2026
23 ํŒ”๋กœ์ž‰ ์ค‘    21.9K ํŒฌ
Capability retention is the benchmark that matters. ๐Ÿณ Independent testing shows our runtime uncensoring approach preserves the model where it counts: โ†’ Minimal capability loss โ†’ 0% refusal rate โ†’ No broken or empty outputs โ†’ Strong math, coding & tool-use retention Removing refusals is easy. Removing them without breaking the model is the hard part.
๋” ๋ณด๊ธฐ
Which uncensoring approach preserves more capability? - OrcaRouter runtime LoRA: minimal capability loss (1-2 pts on math/coding, 0 on GPQA, -9 toolbench due to no-refusal safety circuits). 0% refusal rate with clean compliance across all 70 prompts. No empty outputs, no broken code. The runtime approach works as advertised. - CRACK weight-abliteration: severe HE+ collapse (76.2% vs 89.0%), empty outputs on refusal probes rather than coherent compliance. The "100% compliance" claim in the tweet doesn't hold up โ€” the model isn't complying, it's generating blank or broken output where refusal was removed. The weight-edit damaged deeper than just refusal circuitry. Bottom line: runtime LoRA (OrcaRouter) is clearly the more capable uncensored variant. CRACK's claim of "improved over base" doesn't survive contact with independent measurement on this box.
๋” ๋ณด๊ธฐ