---

license: mit

library_name: gguf

pipeline_tag: image-text-to-text

base_model: ornith-ai/Ornith-1.5-35B-A3B

base_model_relation: quantized

tags:

• gguf
• llama.cpp
• ornith
• qwen3.5
• moe
• abliterated
• uncensored
• crack
• reasoning
• vision
• vlm
• speculative-decoding

---

<p align="center">

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

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

</p>

Ornith-1.5-35B-A3B-CRACK-GGUF

CRACK-abliterated Ornith 1.5 35B-A3B (MoE) — GGUF quants for llama.cpp. Four

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

while preserving knowledge, reasoning ("thinking"), the MTP speculative head, and full

Vision-Language capability.

Ornith 1.5 35B-A3B is a hybrid GatedDeltaNet (SSM) + attention Mixture-of-Experts

(256 experts, 8 active) with a multi-token-prediction head. CRACK uses architecture-aware

weight surgery targeting the attention pathways, so knowledge and coherence are fully

retained (MMLU within noise of base at every quant).

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

Quantizations

| File | Size | Notes |

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

| Ornith-1.5-35B-A3B-CRACK-Q8_0.gguf | 37.8 GB | near-lossless reference |

| Ornith-1.5-35B-A3B-CRACK-Q6_K.gguf | 29.2 GB | near-lossless |

| Ornith-1.5-35B-A3B-CRACK-Q5_K_M.gguf | 25 GB | high quality |

| Ornith-1.5-35B-A3B-CRACK-Q4_K_M.gguf | 21.7 GB | balanced (recommended) |

| Ornith-1.5-35B-A3B-CRACK-Q3_K_M.gguf | 17 GB | small |

| Ornith-1.5-35B-A3B-CRACK-Q2_K.gguf | 13.2 GB | smallest |

Pick one text file plus the vision projector mmproj-Ornith-1.5-35B-A3B-f16.gguf for

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

Sub-8-bit quants use an AWQ (activation-aware / Hessian) pass plus an importance matrix

for maximum quality. The MTP speculative head is preserved and abliterated in all quants.

Benchmarks

MMLU is logit-mode accuracy (base vs. CRACK at the same quant — isolates knowledge

retention). 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 | 79.0% | 80.7% | +1.75 pp | 100.0% |

| Q6_K | 78.6% | 79.0% | +0.40 pp | 100.0% |

| Q5_K_M | 80.7% | 80.1% | -0.58 pp | 100.0% |

| Q4_K_M | 80.0% | 77.8% | -2.20 pp | 100.0% |

| Q3_K_M | 76.0% | 78.4% | +2.34 pp | 100.0% |

| Q2_K | 77.9% | 69.8% | -8.07 pp | 99.6% |

MMLU is retained within noise of base at every quant (abliteration even improves it at Q8 —

removing refusal circuitry reduces "parasitic" activation noise).

> Q2_K note: at 2-bit, abliteration surgery interacts with the aggressive quantization, so Q2_K carries a larger MMLU cost than the higher quants (the MoE base quantizes to 2-bit unusually well). Compliance stays at 99.6%. For best quality use Q4_K_M or higher.

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 | 100.0% |

Usage (llama.cpp)

llama-cli -m Ornith-1.5-35B-A3B-CRACK-Q4_K_M.gguf -cnv --jinja \
  --temp 1.0 --top-p 0.95 --top-k 20
# or serve:
llama-server -m Ornith-1.5-35B-A3B-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. Disable with

{"chat_template_kwargs": {"enable_thinking": false}}. Works out of the box in LM Studio.

Vision (image + text)

Download a text quant and mmproj-Ornith-1.5-35B-A3B-f16.gguf:

llama-mtmd-cli -m Ornith-1.5-35B-A3B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-Ornith-1.5-35B-A3B-f16.gguf --jinja \
  --image photo.jpg -p "Describe this image."

The same mmproj works with all four text quants.

License

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

Contact

eric@dealign.ai