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

Libraries

How to use vodkasn/Gemma-4-31B-JANG_4M-CRACK with MLX:

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

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("vodkasn/Gemma-4-31B-JANG_4M-CRACK")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use vodkasn/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 "vodkasn/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": "vodkasn/Gemma-4-31B-JANG_4M-CRACK"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

OpenClaw new

How to use vodkasn/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 "vodkasn/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 "vodkasn/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"

MLX LM

How to use vodkasn/Gemma-4-31B-JANG_4M-CRACK with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "vodkasn/Gemma-4-31B-JANG_4M-CRACK"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "vodkasn/Gemma-4-31B-JANG_4M-CRACK"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "vodkasn/Gemma-4-31B-JANG_4M-CRACK",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Gemma-4-31B-JANG_4M-CRACK

File size: 1,359 Bytes

85f2950

{
  "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
  },
  "runtime": {
    "total_weight_bytes": 19586875392,
    "total_weight_gb": 18.24
  },
  "format": "jang",
  "format_version": "2.0",
  "crack_surgery": {
    "method": "per-layer",
    "mode": "mpoa",
    "vector": "gemma4_31b_refusal_vectors.safetensors",
    "target_layers": [
      20,
      21,
      22,
      23,
      24,
      25,
      26,
      27,
      28,
      29,
      30,
      31,
      32,
      33,
      34,
      35,
      36,
      37,
      38,
      39,
      40,
      41,
      42,
      43,
      44,
      45,
      46,
      47,
      48,
      49
    ],
    "target_projs": [
      "o_proj",
      "down_proj"
    ],
    "strength": 1.2,
    "modified_tensors": 60
  }
}