Instructions to use mlx-community/Ornith-1.0-35B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Ornith-1.0-35B-bf16 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("mlx-community/Ornith-1.0-35B-bf16")
config = load_config("mlx-community/Ornith-1.0-35B-bf16")

# 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 mlx-community/Ornith-1.0-35B-bf16 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Ornith-1.0-35B-bf16"

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": "mlx-community/Ornith-1.0-35B-bf16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Ornith-1.0-35B-bf16 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 "mlx-community/Ornith-1.0-35B-bf16"

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 mlx-community/Ornith-1.0-35B-bf16

Run Hermes

hermes

OpenClaw new

How to use mlx-community/Ornith-1.0-35B-bf16 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Ornith-1.0-35B-bf16"

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 "mlx-community/Ornith-1.0-35B-bf16" \
  --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-bf16

Full-precision (bfloat16) MLX build of deepreinforce-ai/Ornith-1.0-35B, produced with mlx-vlm 0.6.3. Full multimodal: vision encoder + language model, no precision loss. For Apple Silicon. Runs in mlx-vlm or any MLX app.

≈70 GB on disk; fits in 128 GB unified memory. Use a quantized sibling (3-, 4-, 5-, 6- or 8-bit) on smaller machines.

Conversion note (MoE expert fusion)

Ornith stores its 256 MoE experts unfused (per-expert), but mlx-vlm's qwen3_5_moe loader expects them fused/batched. A sanitize monkeypatch was required to stack the experts before conversion; without it the conversion failed.

Usage

uvx --from mlx-vlm mlx_vlm.generate \
  --model mlx-community/Ornith-1.0-35B-bf16 --image image.png \
  --prompt "Describe this image." --max-tokens 512

from mlx_vlm import load, generate
model, processor = load("mlx-community/Ornith-1.0-35B-bf16")

Conversion check

Smoke-tested after conversion: coherent on both an image prompt (correctly read an evaluation bar chart) and a text reasoning prompt (17 * 24 solved as 408), no repetition loop. 69 tok/s generation, peak 72 GB on a Macbook Pro M5 Max 128GB 40 GPU.

Refer to the original model card for architecture, benchmarks, license, and intended use.