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
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).