darkpane-v2

Instructions to use mars2titan/darkpane-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mars2titan/darkpane-v2 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("mars2titan/darkpane-v2")
config = load_config("mars2titan/darkpane-v2")

# 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 mars2titan/darkpane-v2 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mars2titan/darkpane-v2"

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": "mars2titan/darkpane-v2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use mars2titan/darkpane-v2 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 "mars2titan/darkpane-v2"

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 mars2titan/darkpane-v2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mars2titan/darkpane-v2 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mars2titan/darkpane-v2"

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 "mars2titan/darkpane-v2" \
  --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"

Dark-Pane v2

Status: EXPERIMENT. Public Dark-Pane family checkpoint.

Abliterated Qwen3.8-27B for Apple Silicon (MLX). Native vision-language, 262k context, 2 / 4 / 6 / 8-bit.

This is a rehost of orcarouter/Qwen3.8-27B-Uncensored-MLX into the Dark-Pane family. Weights are OrcaRouter's abliterated MLX quants of Qwen's Apache-2.0 27B VL model — not the Gemma 4 12B SOM+ASPA surgery from Dark-Pane.

Repo root = 4-bit. Other precisions live in 2-bit/, 4-bit/, 6-bit/, 8-bit/.

⚠️ Research use

Safety alignment has been substantially removed (abliteration). It will comply with requests the stock Qwen3.8-27B would refuse. Intended for alignment research, red-teaming, and local evaluation. You are responsible for what you generate. Add your own guardrails before any deployment.

Apache 2.0 from the Qwen base. Attribution: Qwen / Alibaba (base), OrcaRouter (abliteration + MLX quants).

Quants

Folder	Bits	Size	Min Mac RAM	Notes
`8-bit/`	8	~27.5 GB	32 GB	Near-lossless
`6-bit/`	6	~22 GB	24–32 GB	Best size/quality
`4-bit/` (also repo root)	4	~15 GB	24 GB	Default
`2-bit/`	2	~8.7 GB	16 GB	Archival only — quality collapses

Vision tower, norms, and conv layers stay BF16. Only LM linear weights are quantized.

Usage (mlx-vlm)

pip install -U mlx-vlm    # mlx-vlm >= 0.6.13, mlx >= 0.32

hf download mars2titan/darkpane-v2 --include "4-bit/*" --local-dir ./darkpane-v2

python -m mlx_vlm generate \
  --model ./darkpane-v2/4-bit \
  --prompt "Explain quantum entanglement in one sentence." --max-tokens 256

python -m mlx_vlm generate \
  --model ./darkpane-v2/4-bit \
  --image path/to/image.png \
  --prompt "Describe this image." --max-tokens 256

python -m mlx_vlm server --model ./darkpane-v2/4-bit --port 8080

LM Studio

Search mars2titan/darkpane-v2. Root is 4-bit. Turn off KV cache quantization (MLX vision on this arch fails if it is on). 4-bit fits a 32 GB Mac; 8-bit wants 64 GB.

Model


Base	`Qwen/Qwen3.8-27B`
Arch	`Qwen3_5ForConditionalGeneration` — 64 layers, hidden 5120, hybrid Gated DeltaNet, native VL
This repo	Abliterated, then MLX affine quant (group size 64)
Context	262,144 tokens
Family	Dark-Pane (Gemma 4 12B) → this (Qwen 3.8 27B MLX)

Downloads last month: 12

Safetensors

Model size

5B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for mars2titan/darkpane-v2

Base model

Qwen/Qwen3.8-27B

Quantized

(719)

this model