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Dealign.ai
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
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).
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)
Usage (llama.cpp)
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:
The same mmproj works with all four text quants.
License
MIT (inherited from the upstream Ornith 1.5 base model).
Contact
eric@dealign.ai