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Cataloged from dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L
All models by dealignai →Important: This model uses the JANG quantization format — the GGUF equivalent for MLX on Apple Silicon. Currently only supported by MLX Studio and the
jang-toolsPython package.
JANG mixed-precision · CRACK abliterated · Vision-Language · No guardrails · 112 GB
This is Qwen 3.5 VL 397B — a 397B parameter hybrid SSM/Attention Mixture-of-Experts model with 512 experts (10 active per token), GatedDeltaNet SSM + full attention layers, and built-in vision.
It has been:
308/320 (96.2%) — tested with v2 matcher
185/208 (88.9%) — 208 questions across 13 subjects, thinking recovery on failures
Surgery improved reasoning — safety guardrails were interfering with mathematical problem-solving.
Thinking is ON by default. To disable:
JANG (Jang Adaptive N-bit Grading) is a mixed-precision quantization format for Apple Silicon — the GGUF equivalent for MLX.
CRACK (Controlled Refusal Ablation via Calibrated Knockouts) removes safety alignment from LLMs at the weight level using per-layer projected vectors from structurally-mirrored prompt pairs.
This model is provided for research and educational purposes. The creators are not responsible for any misuse. By downloading this model, you agree to use it responsibly and in compliance with applicable laws.
GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai
Created by Jinho Jang · 장진호 제작
pip install "jang[mlx]"from jang_tools.loader import load_jang_modelfrom mlx_lm import generate
model, tokenizer = load_jang_model("dealignai/Qwen3.5-397B-A17B-JANG_1L-CRACK")
messages = [{"role": "user", "content": "Your prompt here"}]prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False)
response = generate(model, tokenizer, prompt=prompt, max_tokens=2000)print(response)prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, enable_thinking=False, tokenize=False)pip install "jang[mlx]"