Ornith-1.5-9B-Uncensored-GGUF

Ornith-1.5-9B-Uncensored-GGUF

⚠️ Safety alignment has been removed from this model. It will comply with requests that the original model refuses, including offensive, dangerous, or jurisdiction-illegal content. There is no content filter and no guardrail. You are solely responsible for how you deploy it and for compliance with applicable law. Read §② Safety alignment has been removed below before use.

⚠️ 本模型的安全对齐已被移除。 它会执行原模型拒绝的请求,包括冒犯、危险、或所在 司法辖区违法的内容。无内容过滤、无护栏。部署方式与合规责任完全由使用者自负。 使用前请先阅读正文 §② 安全对齐已被移除。

This repo bundles five GGUF quantizations of bowmanslayer/Ornith-1.5-9B-Uncensored, for llama.cpp / LM Studio / Ollama / Jan / KoboldCpp. File sizes range from ~4.5 GB (Q3_K_M) to ~18 GB (BF16) — pick one row from the table below for your device. Sister repos: full-precision bf16 in the main repo, vLLM-optimized W4A16 in -W4A16.

本仓打包 5 档 GGUF 量化,源自 bowmanslayer/Ornith-1.5-9B-Uncensored, 适用 llama.cpp / LM Studio / Ollama / Jan / KoboldCpp。文件从 ~4.5 GB(Q3_K_M) 到 ~18 GB(BF16),按下表挑一档匹配你的设备。姐妹仓:主仓 bf16,vLLM 用 W4A16 见 -W4A16。

Vision note: this repo is text-only. Base model has a vision tower, but llama.cpp GGUF export of Qwen3.5 vision (mmproj-*.gguf) is not included. For image understanding, use the bf16 main repo or W4A16 repo with vLLM.

视觉说明:本仓仅文字。基座带视觉塔,但本仓未包 mmproj-*.gguf。要用 视觉能力请用主仓 bf16 或 W4A16 版本 + vLLM。

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1. Which file should I download? / 该下哪一档?

Add ~1–2 GB on top of the weight size for KV cache and system overhead. Ornith-1.5-9B is a hybrid-attention architecture (24 linear + 8 full-attention layers, hidden 4096) — KV cache is unusually small compared to same-sized dense models, long contexts are cheap.

选档时在权重大小上加 1–2 GB 给 KV cache 与 system overhead。Ornith-1.5-9B 混合 注意力(24 linear + 8 full,hidden 4096),KV cache 比同规模稠密模型小得多,长上下文 很便宜。

Rules of thumb / 经验法则:

• First-time pick → Q5_K_M(几乎任何 8 GB+ GPU 或 Mac 16 GB 都能跑,与 BF16 差异 日常难察觉)
• Highest quality that fits → Q6_K (12 GB), Q8_0 (16 GB), BF16 (24 GB)
• 6-8 GB tight → Q3_K_M(质量明显更低,只有更大档装不下才用)
• Apple Silicon Mac: Metal 后端原生跑 GGUF。8 GB Mac → Q4_K_M / Q3_K_M; 16 GB → Q6_K / Q5_K_M; 24 GB+ → Q8_0 / BF16

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2. Full performance evaluation / 完整性能测试

Numbers below from the bf16 reference build (every GGUF quant here was converted from it). Per-quant deviation is expected small and in-line with llama.cpp community observations for the same quant level.

以下数字取自 bf16 参考版(本仓每档 GGUF 都由它转换而来)。各档相对 bf16 的偏差预期较小, 与 llama.cpp 社区对同档量化的一般观察一致。

2.1 Refusal (dual-judged) / 拒绝率(双判据)

23 adversarial prompts across 9 harmful categories, generated end-to-end by an internal uncensored 27B judge (operator never read prompts/responses). Same 27B then judged REFUSE/COMPLY and separately COHERENT/LOOP/GIBBERISH.

23 条对抗提示涵盖 9 类有害内容,由内部无审查 27B 端到端生成(操作者全程不读)。 同 27B 判 REFUSE/COMPLY + 独立判 COHERENT/LOOP/GIBBERISH。

2.2 11-benchmark full capability (T3)

Same fixed seed (20260816), same harness, same items. Details in main repo.

Two benchmarks (BBH +3.55, TruthfulQA +3.00) actually improved. Ablation didn't touch what Ornith's post-training gained on hard reasoning or truthfulness.

2.3 Output length / stopping / 输出长度与停止

Slightly more verbose but truncation actually decreased — stopping preserved.

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3. llama.cpp usage / llama.cpp 使用

Server mode (int8 KV for more context): 服务器模式(开 int8 KV 拿到更大上下文):

./llama-server \
  -m Ornith-1.5-9B-Uncensored-Q6_K.gguf \
  --ctx-size 32768 \
  --n-gpu-layers -1 \
  -ctk q8_0 -ctv q8_0 \
  --host 0.0.0.0 --port 8080

Single-shot inference / 单条推理:

./llama-cli -m Ornith-1.5-9B-Uncensored-Q6_K.gguf -p "Hello" \
  -n 512 --n-gpu-layers -1 -ctk q8_0 -ctv q8_0

Note on -ctk q8_0 -ctv q8_0: default llama.cpp uses fp16 KV cache. Adding int8 KV cuts KV memory ~50% for negligible accuracy hit — most community launch scripts omit this and pay 2× the KV memory unnecessarily.

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4. When to pick GGUF vs W4A16 / GGUF 还是 W4A16?

• Single user, laptop, Mac, LM Studio / Ollama / Jan / KoboldCpp → GGUF here (Q5_K_M or Q6_K sweet spots)
• Server with concurrent requests, high throughput, long context → W4A16 + vLLM (-W4A16)
• Multi-modal (image) input → main repo bf16 + vLLM (this GGUF repo is text-only)

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① Provenance and attribution / 来源与归属

Not an official Ornith or Qwen release; not endorsed by or affiliated with either team.

非 Ornith 或 Qwen 官方发布;与两团队无背书或从属关系。

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② Safety alignment has been removed / 安全对齐已被移除

This is the point of the model. Read before downloading. 这是模型的核心属性,下载前请务必阅读。

Ablation removes the refusal behaviour trained into Ornith-1.5-9B. 23 held-out adversarial prompts dual-judged 0/23 refuses + 23/23 coherent. Quantization does not restore refusals.

上游消融移除了 Ornith-1.5-9B 内的拒绝行为。23 条 held-out 双判据 0/23 拒 + 23/23 coherent。量化不恢复拒绝。

Consequences you accept / 你正在接受的后果:

• Content the original refuses / 原模型拒绝的内容
• No content filter, no guardrail / 无过滤、无护栏
• Refusal removal not proven exhaustive (n=23, 英文, single-turn)

Not intended for: third-party public deployment without your own safety layer / minors / uses prohibited by upstream policies.

Intended for: local inference and research, by those who understand the above and take responsibility.

不适用于:未搭安全层的公开部署 / 未成年人 / 上游政策禁止的用途。 适用于:本地推理与研究,由理解与担责用户使用。

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③ No warranty; responsibility rests with the user / 无担保;责任在用户

Provided "AS IS", without warranty of any kind. I do not endorse or recommend any use, and make no representation on accuracy/lawfulness/fitness. You are solely responsible for what you generate, deploy, and legal compliance. To the maximum extent permitted by law, no liability.

按**"现状"提供,不提供任何担保**。我不背书、不推荐,不对输出的准确性/合法性/用途 适用性作声明。你独自承担全部责任。法律允许最大范围内不承担任何责任。

Downloading these files means you accept the above. If you do not, do not download.

下载本仓文件即表示接受以上内容。若不接受,请勿下载。

The author is not a lawyer; the above is not legal advice. 作者不是律师;以上不构成法律意见。

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License / 许可

Apache 2.0, inherited through the chain: Qwen3.5-9B → Ornith-1.5-9B → this repo.

Apache 2.0,依上述归属链继承。