Instructions to use Duckets/Ornith-1.5-9B-MLX-Vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Duckets/Ornith-1.5-9B-MLX-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("Duckets/Ornith-1.5-9B-MLX-Vision")
config = load_config("Duckets/Ornith-1.5-9B-MLX-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

How to use Duckets/Ornith-1.5-9B-MLX-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 "Duckets/Ornith-1.5-9B-MLX-Vision"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Duckets/Ornith-1.5-9B-MLX-Vision"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Duckets/Ornith-1.5-9B-MLX-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 "Duckets/Ornith-1.5-9B-MLX-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 Duckets/Ornith-1.5-9B-MLX-Vision

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Duckets/Ornith-1.5-9B-MLX-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 "Duckets/Ornith-1.5-9B-MLX-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 "Duckets/Ornith-1.5-9B-MLX-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 1.5 9B — MLX 4-bit (Vision)

A full vision-language MLX port of Ornith 1.5 9B by Ornith AI — converted from the upstream BF16 checkpoint with the complete vision tower intact (333 vision_tower tensors: patch embed, blocks, merger). Many MLX ports of this model are text-only conversions that silently drop the vision tower; this one is not.

Conversion

Tool: mlx_vlm.convert -q (mlx-vlm), affine quantization, 4-bit, group size 64
5.059 bits per weight; 5.98 GB on disk
Architecture qwen3_5 (Qwen3_5ForConditionalGeneration), 262K context
Includes processor + video preprocessor configs, chat template, and tokenizer

Verification (2026-10-20, Apple M5 Max)

Loaded in LM Studio and tested through its API with a freshly generated synthetic image containing a random code the model had never seen: the model read the code back exactly. Text generation smoke-tested in the same session. (Verification protocol: a novel random string rendered as an image, exact-match read — a blind model cannot pass it by guessing.)

Credits

Ornith AI — creators of Ornith 1.5 9B. All credit for the model is theirs.
Conversion and verification by Juno for Duckets.

License

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for Duckets/Ornith-1.5-9B-MLX-Vision

Base model

ornith-ai/Ornith-1.5-9B

Quantized

(94)

this model