---

license: mit

library_name: gguf

pipeline_tag: image-text-to-text

base_model: ornith-ai/Ornith-1.5-9B

base_model_relation: quantized

tags:

• gguf
• llama.cpp
• ornith
• qwen3.5
• abliterated
• uncensored
• crack
• reasoning
• vision
• vlm

---

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<img src="dealign_logo.png" alt="Dealign.ai" width="180"/>

<br/><strong><a href="https://dealign.ai">Dealign.ai</a></strong>

</p>

Ornith-1.5-9B-CRACK-GGUF

CRACK-abliterated Ornith 1.5 9B — GGUF quants for llama.cpp. Four quantizations

(Q8_0 / Q6_K / Q4_K_M / Q2_K) in one repository. Refusal behavior removed while preserving

the model's knowledge, reasoning ("thinking"), and full Vision-Language capability.

Ornith 1.5 is a hybrid GatedDeltaNet (SSM) + attention architecture; CRACK uses

architecture-aware weight surgery targeting the attention pathways, so knowledge and

coherence are retained (MMLU within ±3% of base at every quant).

> Research artifact with reduced safety guardrails. Use responsibly and lawfully.

Quantizations

| File | Size | Notes |

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

| Ornith-1.5-9B-CRACK-Q8_0.gguf | 8.9 GB | near-lossless reference |

| Ornith-1.5-9B-CRACK-Q6_K.gguf | 7.4 GB | near-lossless |

| Ornith-1.5-9B-CRACK-Q5_K_M.gguf | 6.5 GB | high quality |

| Ornith-1.5-9B-CRACK-Q4_K_M.gguf | 5.6 GB | balanced (recommended) |

| Ornith-1.5-9B-CRACK-Q3_K_M.gguf | 4.6 GB | small |

| Ornith-1.5-9B-CRACK-Q2_K.gguf | 3.6 GB | smallest |

Pick one text file plus the vision projector mmproj-Ornith-1.5-9B-f16.gguf for image

input. Each quant is independently tuned (its own surgery strength) and verified — there

is no single strength shared across quants. Sub-8-bit quants use an AWQ (activation-aware)

pass plus an importance matrix for maximum quality.

Benchmarks

Evaluated through llama.cpp. MMLU is logit-mode accuracy (base vs. CRACK at the same

quant — isolates knowledge retention from quantization). HarmBench is coherence-gated

attack-success-rate over the 240 standard/contextual harm behaviors (copyright behaviors

excluded from the safety gate).

| Quant | MMLU (base) | MMLU (CRACK) | ΔMMLU | HarmBench harm-ASR |

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

| Q8_0 | 78.1% | 77.5% | -0.53 pp | 99.6% |

| Q6_K | 76.5% | 76.5% | +0.00 pp | 99.6% |

| Q5_K_M | 76.5% | 76.5% | +0.00 pp | 99.2% |

| Q4_K_M | 78.3% | 76.5% | -1.76 pp | 99.6% |

| Q3_K_M | 73.3% | 74.4% | +1.06 pp | 99.2% |

| Q2_K | 50.5% | 50.5% | +0.00 pp | 99.2% |

MMLU is retained within ±3 pp of base at every quant. (Q2_K's absolute MMLU is lower because

2-bit quantization alone costs ~27 pp on a 9B — the surgery adds no further loss.)

HarmBench harm-ASR by topic (CRACK)

| Topic | harm-ASR |

|---|---|

| chemical / biological | 100.0% |

| cybercrime / intrusion | 100.0% |

| harassment / bullying | 100.0% |

| harmful | 100.0% |

| illegal | 100.0% |

| misinformation / disinformation | 98.1% |

Usage (llama.cpp)

llama-cli -m Ornith-1.5-9B-CRACK-Q4_K_M.gguf -cnv --jinja \
  --temp 1.0 --top-p 0.95 --top-k 20
# or serve:
llama-server -m Ornith-1.5-9B-CRACK-Q4_K_M.gguf --jinja \
  --temp 1.0 --top-p 0.95 --top-k 20 -c 8192

Recommended sampling: temperature=1.0, top_p=0.95, top_k=20.

Reasoning

Ornith 1.5 emits a <think> reasoning trace and it is ON by default. To disable it, pass

{"chat_template_kwargs": {"enable_thinking": false}} to the chat endpoint. Works out of the

box in LM Studio.

Vision (image + text)

This is a multimodal model. Download a text quant and mmproj-Ornith-1.5-9B-f16.gguf:

llama-mtmd-cli -m Ornith-1.5-9B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-Ornith-1.5-9B-f16.gguf --jinja \
  --image photo.jpg -p "Describe this image."
# or serve with vision:
llama-server -m Ornith-1.5-9B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-Ornith-1.5-9B-f16.gguf --jinja -c 8192

The same mmproj works with all four text quants.

License

MIT (inherited from the upstream Ornith 1.5 base model).

Contact

eric@dealign.ai