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)

bash

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:

bash

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

dealignai/Ornith-1.5-35B-A3B-UNCENSORED-GGUF

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