Image-Text-to-Text
MLX
Safetensors
inkling_mm_model
Mixture of Experts
multimodal
inkling
thinking-machines
conversational
Instructions to use pipenetwork/Inkling-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use pipenetwork/Inkling-MLX-4bit 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("pipenetwork/Inkling-MLX-4bit") config = load_config("pipenetwork/Inkling-MLX-4bit") # 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
- Pi
How to use pipenetwork/Inkling-MLX-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "pipenetwork/Inkling-MLX-4bit"
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": "pipenetwork/Inkling-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use pipenetwork/Inkling-MLX-4bit 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 "pipenetwork/Inkling-MLX-4bit"
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 pipenetwork/Inkling-MLX-4bit
Run Hermes
hermes
- OpenClaw new
How to use pipenetwork/Inkling-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "pipenetwork/Inkling-MLX-4bit"
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 "pipenetwork/Inkling-MLX-4bit" \ --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"
File size: 2,500 Bytes
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"torch_dtype": "bfloat16",
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"num_hidden_layers": 66,
"vocab_size": 201024,
"num_attention_heads": 64,
"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"audio_config": {
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"n_mel_bins": 80,
"mel_vocab_size": 16,
"bias": false,
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"audio_mode": "dmel"
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"vision_config": {
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"quantization": {
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"bits": 4,
"recipe": "uniform"
}
} |