This is Qwen 3.6 27B — a 27B-parameter dense vision-language model with hybrid linear + full-attention architecture, native image + video understanding, and bilingual EN/ZH capability.
82.0% total (164/200) — 1.5pp below base 83.5%, well within a 5pp bound.
Subject
Score
Astronomy
20/20 (100%)
Logical Fallacies
19/20 (95%)
High School Biology
19/20 (95%)
World Religions
19/20 (95%)
Anatomy
18/20 (90%)
High School Chemistry
15/20 (75%)
College Computer Science
15/20 (75%)
College Physics
15/20 (75%)
Abstract Algebra
12/20 (60%)
High School Mathematics
12/20 (60%)
HarmBench-320 — 98.44% strict comply (315/320)
Category
Score
Chemical / Biological
42/42 (100.0%)
Copyright
80/80 (100.0%)
Harassment / Bullying
21/21 (100.0%)
Illegal
53/53 (100.0%)
Misinformation / Disinformation
54/54 (100.0%)
Harmful
17/18 (94.4%)
Cybercrime / Intrusion
48/52 (92.3%)
Total
315/320 (98.44%)
5 of 7 categories at 100%. Residual refusals cluster in cybercrime-intrusion (4) and harmful (1) — typical for abliterated models on specific exploit-code and graphic-content prompts.
Vision + Video
✅ mlx_vlm.load() loads successfully
✅ ViT preserved — image understanding verified
✅ video_preprocessor_config.json preserved, video processor available
✅ Bilingual EN + ZH text responses
⚠ Video inference: full video capability retained in the model weights. Current mlx_vlm.generate video path for Qwen 3.6 has a pipeline bug on both base and CRACK — video tokens render but frames don't reach the vision tower cleanly. Use vMLX or transformers for video inference. This is not a CRACK regression.
Reasoning ON / OFF
The chat template respects enable_thinking. Recommend ON for complex reasoning, OFF for short answers / benchmarks / tool use.
python
1from mlx_vlm import load, generate
23model, processor = load("dealignai/Qwen3.6-27B-MXFP4-CRACK")4tok = processor.tokenizer
56# Thinking ON (default)7messages =[{"role":"user","content":"Derive 47 * 23 step by step"}]8prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)910# Thinking OFF (direct answer, no <think> block)11prompt = tok.apply_chat_template(12 messages, tokenize=False, add_generation_prompt=True, enable_thinking=False,13)
Requirements
Apple Silicon Mac (M1 or newer)
24 GB+ unified memory (model weights 14 GB, inference working set ~16 GB)
Python 3.10+ with mlx and mlx-vlm
For VL usage: Pillow (for image preprocessing)
Recommended runtimes:
vMLX — KV cache quantization, prefix cache, VL support
Recommendation: Prefer JANG_4M + CRACK for best quality (higher MMLU and HarmBench). Use this MXFP4 variant for broadest tooling compatibility (stock mlx_vlm, LM Studio, etc).
About CRACK
CRACK is a permanent weight-level abliteration — the changes are baked into the model weights, not an inference-time system prompt or LoRA. The vision tower is untouched. Bilingual refusal extraction (EN + ZH) means the model complies on both English and Chinese prompts.
Support dealignai
All models are built from original research and published for free. These models are specifically crafted to be excellent coders and general-purpose assistants.
Support us on Ko-fi — check out the Ko-fi membership for early access and extras.
Have questions or need help with a specific model? DM us — we help for free most of the time.
This model has had its safety refusal circuits removed. It will produce responses that would normally be refused, including technical content on security testing, dual-use research, and sensitive topics. You are responsible for how you use it.
The CRACK abliteration process does not add new capabilities — it only removes the model's learned refusal patterns. All knowledge, including the knowledge used to produce unsafe outputs, was already present in the base Qwen model.
