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zwcf5200
/
Ornith-1.0-35B-Vision

Image-Text-to-Text
MLX
Safetensors
English
Chinese
qwen3_5_moe
mlx-vlm
qwen3.5
Mixture of Experts
vision
apple-silicon
4-bit precision
ornith
conversational
Model card Files Files and versions
xet
Community

Instructions to use zwcf5200/Ornith-1.0-35B-Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • MLX

    How to use zwcf5200/Ornith-1.0-35B-Vision 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("zwcf5200/Ornith-1.0-35B-Vision")
    config = load_config("zwcf5200/Ornith-1.0-35B-Vision")
    
    # 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 zwcf5200/Ornith-1.0-35B-Vision with Pi:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "zwcf5200/Ornith-1.0-35B-Vision"
    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": "zwcf5200/Ornith-1.0-35B-Vision"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Hermes Agent

    How to use zwcf5200/Ornith-1.0-35B-Vision 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 "zwcf5200/Ornith-1.0-35B-Vision"
    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 zwcf5200/Ornith-1.0-35B-Vision
    Run Hermes
    hermes
  • Atomic Chat
  • OpenClaw

    How to use zwcf5200/Ornith-1.0-35B-Vision with OpenClaw:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "zwcf5200/Ornith-1.0-35B-Vision"
    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 "zwcf5200/Ornith-1.0-35B-Vision" \
      --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"
Ornith-1.0-35B-Vision
20.4 GB
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  • 1 contributor
History: 3 commits
zwcf5200's picture
zwcf5200
Update README.md
d1155ef verified 14 days ago
  • .gitattributes
    1.57 kB
    Upload Ornith 1.0 35B Vision for MLX 16 days ago
  • README.md
    7.01 kB
    Update README.md 14 days ago
  • chat_template.jinja
    7.55 kB
    Upload Ornith 1.0 35B Vision for MLX 16 days ago
  • config.json
    19.9 kB
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  • generation_config.json
    213 Bytes
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  • model-00001-of-00004.safetensors
    5.34 GB
    xet
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  • model-00002-of-00004.safetensors
    5.37 GB
    xet
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  • model-00003-of-00004.safetensors
    5.37 GB
    xet
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  • model-00004-of-00004.safetensors
    3.43 GB
    xet
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  • model-vision-00001-of-00001.safetensors
    893 MB
    xet
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  • model.safetensors.index.json
    210 kB
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  • preprocessor_config.json
    390 Bytes
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  • processor_config.json
    1.19 kB
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  • tokenizer.json
    20 MB
    xet
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  • tokenizer_config.json
    9.01 kB
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  • video_preprocessor_config.json
    385 Bytes
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