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"

Request for Q4 GGUF version for x86 users

by cltyq777 - opened May 4

Discussion

cltyq777

May 4

Hi Dealignal

Thank you so much for your incredible work on abliterating this hybrid SSM/CoT model! I saw your comments on Reddit mentioning the massive effort it took to get this 397B model working coherently.

I am extremely interested in running this, but my setup is an x86 server (Dual CPU with 512GB RAM) running llama.cpp, so I cannot use the MLX/JANG format.

I remember you mentioned on Reddit that you could make GGUF versions (Q4 or above) if there was enough demand. Could you please consider releasing a Q4_K_M or Q4_0 GGUF version of this CRACK abliterated model? Or alternatively, uploading the unquantized Safetensors so the community can run the conversion scripts?

Thank you again for your dedication to the open-source community!

revster1337

Jun 1

waiting for GGUF versions too :)

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