Instructions to use dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L")
config = load_config("dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L

Run Hermes

hermes

OpenClaw new

How to use dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "dealignai/Qwen3.5-VL-397B-A17B-UNCENSORED-JANG_1L" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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 — refusal behavior removed via weight-level surgery


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 — weight-level 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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