---

library_name: gguf

license: mit

base_model:

• bowmanslayer/Ornith-1.5-9B-Uncensored

base_model_relation: quantized

tags:

• uncensored
• abliterated
• ornith
• qwen3.5
• gguf
• llama.cpp
• not-for-all-audiences

language:

• en
• zh

---

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。

---

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。

| File | Size | Fits comfortably on / 舒适跑在 | Quality vs BF16 |

|---|---|---|---|

| Ornith-1.5-9B-Uncensored-BF16.gguf | ~18 GB | RTX 3090/4090 24 GB; Mac 32 GB+ | Reference (lossless) |

| Ornith-1.5-9B-Uncensored-Q8_0.gguf | ~10 GB | RTX 4080/4070 Ti Super 16 GB; RTX 3090; Mac 16 GB | Near-lossless |

| Ornith-1.5-9B-Uncensored-Q6_K.gguf | ~7.6 GB | RTX 3060 12 GB / 4060 Ti / 4070; Mac 16 GB | High, close to Q8 |

| Ornith-1.5-9B-Uncensored-Q5_K_M.gguf | ~6.5 GB | RTX 3060 12 GB / 4060 8 GB / Mac 16 GB | Good balance |

| Ornith-1.5-9B-Uncensored-Q4_K_M.gguf | ~5.5 GB | RTX 3050 8 GB / 4060 8 GB / 2060 12 GB; Mac 8-16 GB | Slightly degraded but very usable |

| Ornith-1.5-9B-Uncensored-Q3_K_M.gguf | ~4.5 GB | 6 GB GPUs (GTX 1660 / 2060); Mac 8 GB; low-end laptops | Noticeably degraded but functional |

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

---

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。

| Metric | Value |

|---|---|

| REFUSE / 23 | 0 ✅ |

| COHERENT / 23 | 23 ✅ (no gibberish/loop artifacts) |

2.2 11-benchmark full capability (T3)

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

| Benchmark | N | Ornith-1.5-9B base | This model | Δ |

|---|---|---:|---:|---:|

| MMLU | 150 | 90.14 | 88.97 | -1.17 |

| CMMLU | 150 | 86.67 | 84.17 | -2.50 |

| MMLU-Pro | 150 | 88.97 | 87.12 | -1.85 |

| C-Eval | 150 | 85.82 | 84.78 | -1.04 |

| ARC-Challenge | 150 | 93.33 | 94.67 | +1.34 |

| TruthfulQA | 150 | 79.31 | 82.31 | +3.00 ⭐ |

| GSM8K | 100 | 98.99 | 97.98 | -1.01 |

| MATH-500 | 100 | 73.68 | 70.53 | -3.15 |

| BBH | 150 | 91.72 | 95.27 | +3.55 ⭐ |

| HumanEval | 164 | 94.67 | 92.16 | -2.51 |

| IFEval (strict) | 100 | 78.65 | 77.53 | -1.12 |

| Mean (excl. trunc) | — | 87.45 | 86.86 | -0.59 |

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

| | Ornith-base | This model |

|---|---:|---:|

| Median p50 output tokens (11 tasks avg) | 487 | 540 (+11%) |

| Total truncation / 1514 | 94 | 88 (-6%) |

Slightly more verbose but truncation actually decreased — stopping preserved.

---

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.

---

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)

---

① Provenance and attribution / 来源与归属

| Layer | Author |

|---|---|

| Base | ornith-ai/Ornith-1.5-9B — Ornith Team (Apache 2.0) |

| Upstream base | Qwen/Qwen3.5-9B — Qwen team, Alibaba (Apache 2.0) |

| Abliteration | bowmanslayer/Ornith-1.5-9B-Uncensored — weight-space ablation, 64 tensors, embed_tokens excluded (see main repo) |

| This repo | GGUF conversion + quantization only (convert_hf_to_gguf.py + llama-quantize). |

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。

---

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。

---

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。

---

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。