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
- MLX
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
- Pi
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"} ] }'
File size: 1,689 Bytes
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"audio_ms_per_token": 40,
"audio_seq_length": 750,
"feature_extractor": {
"dither": 0.0,
"feature_extractor_type": "Gemma4AudioFeatureExtractor",
"feature_size": 128,
"fft_length": 512,
"fft_overdrive": false,
"frame_length": 320,
"hop_length": 160,
"input_scale_factor": 1.0,
"max_frequency": 8000.0,
"mel_floor": 0.001,
"min_frequency": 0.0,
"padding_side": "right",
"padding_value": 0.0,
"per_bin_mean": null,
"per_bin_stddev": null,
"preemphasis": 0.0,
"preemphasis_htk_flavor": true,
"return_attention_mask": true,
"sampling_rate": 16000
},
"image_processor": {
"do_convert_rgb": true,
"do_normalize": false,
"do_rescale": true,
"do_resize": true,
"image_mean": [
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],
"image_processor_type": "Gemma4ImageProcessor",
"image_seq_length": 280,
"image_std": [
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"max_soft_tokens": 280,
"patch_size": 16,
"pooling_kernel_size": 3,
"resample": 3,
"rescale_factor": 0.00392156862745098
},
"image_seq_length": 280,
"processor_class": "Gemma4Processor",
"video_processor": {
"do_convert_rgb": true,
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"do_sample_frames": true,
"image_mean": [
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"image_std": [
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"max_soft_tokens": 70,
"num_frames": 32,
"patch_size": 16,
"pooling_kernel_size": 3,
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"rescale_factor": 0.00392156862745098,
"return_metadata": false,
"video_processor_type": "Gemma4VideoProcessor"
}
}
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