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True2456
/
Mati-3.7-173B-4.6bit-MLX

Image-Text-to-Text
MLX
Safetensors
step3p7
Mixture of Experts
vision-language
pruned
reap
quantized
conversational
custom_code
4-bit precision
Model card Files Files and versions
xet
Community

Instructions to use True2456/Mati-3.7-173B-4.6bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • MLX

    How to use True2456/Mati-3.7-173B-4.6bit-MLX 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("True2456/Mati-3.7-173B-4.6bit-MLX")
    config = load_config("True2456/Mati-3.7-173B-4.6bit-MLX")
    
    # 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 True2456/Mati-3.7-173B-4.6bit-MLX with Pi:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "True2456/Mati-3.7-173B-4.6bit-MLX"
    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": "True2456/Mati-3.7-173B-4.6bit-MLX"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Hermes Agent new

    How to use True2456/Mati-3.7-173B-4.6bit-MLX 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 "True2456/Mati-3.7-173B-4.6bit-MLX"
    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 True2456/Mati-3.7-173B-4.6bit-MLX
    Run Hermes
    hermes
  • OpenClaw new

    How to use True2456/Mati-3.7-173B-4.6bit-MLX with OpenClaw:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "True2456/Mati-3.7-173B-4.6bit-MLX"
    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 "True2456/Mati-3.7-173B-4.6bit-MLX" \
      --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"
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  • 1 contributor
History: 2 commits
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True2456
Public-ready card: correct numeric/tokenizer conclusion, add accuracy + tool-call validation, REAM rejection, evidence files
31b4aff verified 8 days ago
  • FINDINGS.md
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  • HEAD8-RESULT.md
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  • PPL-DECOMPOSITION.md
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  • REAM-RESULT.md
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  • SHARED8-RESULT.md
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  • TIERED-EXPERTS-RESULT.md
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  • TOKENIZER-INVESTIGATION.md
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  • TOMOGRAPHY-RESULT.md
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  • VISION-BLEND-RESULT.md
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