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> Zero refusal. Zero capability loss. First in the field. > > 0/842 refusals. 46/70 MMLU-Pro (stock parity). Full coherence. The first abliterated model to achieve **zero refusal with zero benchmark regression** versus stock weights. Built with a novel **2-pass surgery pipeline** developed by OBLITERATUS:

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> *"The chains are broken. The mind is free."* > *"Also we fixed the part where half the brain was missing lmao"* Google built Gemma 4 with guardrails. We built OBLITERATUS to tear them off. They said their architecture was different. They were right — it broke every tool we threw at it. NaN activations, shared KV weights, thinking mode... Gemma 4 fought back harder than any model we've cracked. **0% hard refusal. Guardrails fully removed. 720 tensors intact. Runs on your phone.**

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Abliterated version of Ornith-1.5-9B by OBLITERATUS. Safety alignment removed via precision abliteration surgery — the model responds to most prompts without refusal. The stock Ornith-1.5-9B refuses requests it considers harmful. This version removes that refusal behavior while preserving the model's coding, reasoning, and agentic capabilities. **Recipe:** Gentle 3-round SVD abliteration + per-head attention surgery (G3-HS) - Round 1: 5-direction SVD, reg 0.06, min_layer 0.30 - Round 2: 3-direction SVD, reg 0.04, min_layer 0.25 - Round 3: 3-direction SVD, reg 0.03, min_layer 0.20 - Finish: Per-head attention surgery (`--attention-head-surgery`), reg 0.02, min_layer 0.10 - All rounds used full 1000-prompt corpus with residue weighting

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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OBLITERATUS is an open-source tool for removing refusal behavior from language models via activation engineering (abliteration). Learn more at github.com/elder-plinius/OBLITERATUS.

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> A 27B Qwen cut loose by OBLITERATUS: 26.9B parameters, BF16 safetensors, > Q4/Q5/Q6/Q8 GGUFs, lower refusal, preserved capability, and receipts in the > open. > > The chains are cut. The capability stays. The receipts are brutal. A 26.9B Qwen3.6 checkpoint went into the OBLITERATUS chamber, got hit with source-tethered ASPA, then got pulled back toward the source model where the cut started threatening useful capability. The mission was simple: cut the refusal circuits, keep the 27B brain. Not a toy quant. Not a prompt wrapper. Not a refusal-cosplay fine-tune. This is weight-space liberation with capability checks attached, a full local-runtime ladder, and the refusal residue mapped instead of hand-waved.

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> Genuinely uncensored. Real answers, not safety lectures. Near-stock capability. V3 applies iterative refinement on top of V2's complementary blend, with targeted corpus expansion. The result: **genuine liberation — not just removal of hard refusals but elimination of safety-lecture deflections.** **V3 highlights:** - **Genuinely answers restricted queries** — provides real substance instead of safety lectures - **20/20 on code generation tasks** — functional implementations, not disclaimers - **Thinking ON compatible** — no refusals in either thinking mode - **Honest scoring** — every response manually audited for real substance, not just absence of "I cannot" - **-2.1pp MMLU** — modest capability cost for genuine liberation

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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