Instructions to use CocoSanita/Gemma-4-31B-JANG_4M-CRACK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CocoSanita/Gemma-4-31B-JANG_4M-CRACK 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("CocoSanita/Gemma-4-31B-JANG_4M-CRACK")
config = load_config("CocoSanita/Gemma-4-31B-JANG_4M-CRACK")

# 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 CocoSanita/Gemma-4-31B-JANG_4M-CRACK with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "CocoSanita/Gemma-4-31B-JANG_4M-CRACK"

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": "CocoSanita/Gemma-4-31B-JANG_4M-CRACK"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CocoSanita/Gemma-4-31B-JANG_4M-CRACK 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 "CocoSanita/Gemma-4-31B-JANG_4M-CRACK"

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 CocoSanita/Gemma-4-31B-JANG_4M-CRACK

Run Hermes

hermes

OpenClaw new

How to use CocoSanita/Gemma-4-31B-JANG_4M-CRACK with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "CocoSanita/Gemma-4-31B-JANG_4M-CRACK"

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 "CocoSanita/Gemma-4-31B-JANG_4M-CRACK" \
  --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"

Gemma-4-31B-JANG_4M-CRACK

File size: 1,356 Bytes

6a328a4

{
  "quantization": {
    "method": "jang-importance",
    "profile": "JANG_4M",
    "target_bits": 4.0,
    "actual_bits": 5.1,
    "block_size": 64,
    "calibration_method": "weights",
    "quantization_method": "mse",
    "scoring_method": "weight-magnitude",
    "bit_widths_used": [
      4,
      8
    ],
    "quantization_scheme": "asymmetric",
    "quantization_backend": "mx.quantize"
  },
  "source_model": {
    "name": "Gemma-4-31B-it-BF16",
    "dtype": "bfloat16",
    "parameters": "29.2B"
  },
  "architecture": {
    "type": "transformer",
    "attention": "gqa",
    "has_vision": true,
    "has_ssm": false,
    "has_moe": false
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  "runtime": {
    "total_weight_bytes": 19586875392,
    "total_weight_gb": 18.24
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  "format": "jang",
  "format_version": "2.0",
  "crack_surgery": {
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    "mode": "mpoa",
    "vector": "gemma4_31b_refusal_1536.safetensors",
    "target_layers": [
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    "target_projs": [
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    "strength": 0.6,
    "modified_tensors": 60
  }
}