Instructions to use OsaurusAI/Ornith-1.5-9B-JANG_4D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/Ornith-1.5-9B-JANG_4D 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("OsaurusAI/Ornith-1.5-9B-JANG_4D")
config = load_config("OsaurusAI/Ornith-1.5-9B-JANG_4D")

# 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 OsaurusAI/Ornith-1.5-9B-JANG_4D with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Ornith-1.5-9B-JANG_4D"

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": "OsaurusAI/Ornith-1.5-9B-JANG_4D"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use OsaurusAI/Ornith-1.5-9B-JANG_4D 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 "OsaurusAI/Ornith-1.5-9B-JANG_4D"

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 OsaurusAI/Ornith-1.5-9B-JANG_4D

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OsaurusAI/Ornith-1.5-9B-JANG_4D with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Ornith-1.5-9B-JANG_4D"

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 "OsaurusAI/Ornith-1.5-9B-JANG_4D" \
  --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"

Osaurus-AI commited on 7 days ago

Commit

eaee88f

verified ·

1 Parent(s): c57a254

Remove throughput figures from the card

Browse files

Files changed (1) hide show

README.md +0 -4

README.md CHANGED Viewed

@@ -38,10 +38,6 @@ Ornith 1.5 is an agentic coding / reasoning VLM built on a hybrid **gated-delta
 | Context | 262,144 |
 | Shards | 2 |
 | Bit distribution | {4: 86, 5: 192, 6: 24, 8: 32} |
-| Decode | **73.0 tok/s** |
-| Prefill | 599 tok/s |
-> Throughput measured on an M5 Max: 512-token sustained decode, 3 runs after a discarded warmup, one bundle per process, on an otherwise idle machine (run-to-run spread <5%). Short-generation numbers overstate or understate this badly, so treat any figure not measured this way with suspicion.
 ## How it was quantized

 | Context | 262,144 |
 | Shards | 2 |
 | Bit distribution | {4: 86, 5: 192, 6: 24, 8: 32} |
 ## How it was quantized