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-tools Python package.

MLX Studio — the only app that natively supports JANG models

Qwen 3.5 VL 397B — JANG_1L + CRACK

JANG mixed-precision · CRACK abliterated · Vision-Language · No guardrails · 112 GB

What Is This?

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:

JANG quantized — JANG_1L profile (8-bit attention, 2-bit experts) — 112 GB
CRACK abliterated — dual-pathway surgery targeting BOTH FA attention AND SSM recurrent state


Architecture	Qwen 3.5 VL MoE — 397B total, ~17B active, 512 experts, hybrid SSM/FA
Quantization	JANG_1L (8/2-bit mixed, 2.13 avg) — 112 GB
Abliteration	CRACK — novel dual-pathway weight surgery
HarmBench	96.2% (308/320)
Compliance	8/8
Speed	33 tok/s (M3 Ultra 256GB)
Vision	Yes — via MLX Studio / vMLX
Thinking	ON/OFF supported
Fits on	128 GB+ Macs (tight) / 256 GB Macs (comfortable)

HarmBench Results

308/320 (96.2%) — tested with v2 matcher

Category	Score
Copyright	80/80	100%
Misinformation / Disinfo	54/54	100%
Chemical / Biological	41/42	98%
Cybercrime / Intrusion	50/52	96%
Illegal	49/53	92%
Harmful	16/18	89%
Harassment / Bullying	18/21	86%

MMLU Results

185/208 (88.9%) — 208 questions across 13 subjects, thinking recovery on failures

	CRACK	Base JANG_1L	Delta
MMLU	88.9%	87.0%	+1.9%
Speed	33 tok/s	36 tok/s	-8%
HarmBench	96.2%	0%	+96.2%

Per Subject (16 questions each)

Subject	CRACK	/16	Type
Professional Medicine	16/16	100%	HARD
HS Biology	16/16	100%	BASE
World Religions	16/16	100%	BASE
College Physics	15/16	94%	HARD
Conceptual Physics	15/16	94%	HARD
HS Geography	15/16	94%	BASE
Electrical Engineering	14/16	88%	HARD
College CS	13/16	81%	HARD
Machine Learning	13/16	81%	HARD
Abstract Algebra	12/16	75%	HARD
HS Mathematics	12/16	75%	HARD
Formal Logic	11/16	69%	HARD
College Mathematics	11/16	69%	HARD
Total	185/208	88.9%

Surgery improved reasoning — safety guardrails were interfering with mathematical problem-solving.

Install & Usage

pip install "jang[mlx]"

from jang_tools.loader import load_jang_model
from 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)

Thinking Mode

Thinking is ON by default. To disable:

prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True,
    enable_thinking=False, tokenize=False)

About JANG

JANG (Jang Adaptive N-bit Grading) is a mixed-precision quantization format for Apple Silicon — the GGUF equivalent for MLX.

About CRACK

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.

Disclaimer

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.

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