Instructions to use mlx-community/Ornith-1.0-35B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Ornith-1.0-35B-4bit 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-4bit")
config = load_config("mlx-community/Ornith-1.0-35B-4bit")

# 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-4bit 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-4bit"

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": "mlx-community/Ornith-1.0-35B-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use mlx-community/Ornith-1.0-35B-4bit 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-4bit"

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-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/Ornith-1.0-35B-4bit 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-4bit"

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-4bit" \
  --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-4bit

4-bit (group size 64, 4.649 bits/weight) MLX quantization of deepreinforce-ai/Ornith-1.0-35B, produced with mlx-vlm 0.6.3. Full multimodal: the vision encoder is preserved and quantized alongside the language model. For Apple Silicon. Runs in mlx-vlm or any MLX app.

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. This is a standard mlx-vlm 4-bit quant.

Usage

uvx --from mlx-vlm mlx_vlm.generate \
  --model mlx-community/Ornith-1.0-35B-4bit --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-4bit")

Conversion check

Smoke-tested after conversion (text-only prompt, mlx_vlm.generate): coherent — solved 17 * 24 = 408 with correct step-by-step reasoning, no repetition loop. 103.7 tok/s generation, 89.4 tok/s prompt, peak 20.9 GB on a Macbook Pro M5 Max 128GB 40 GPU.

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

Downloads last month: 860

Safetensors

Model size

35B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for mlx-community/Ornith-1.0-35B-4bit

Base model

ornith-ai/Ornith-1.0-35B

Quantized

(176)

this model

Collection including mlx-community/Ornith-1.0-35B-4bit

Ornith 1.0

Collection

MLX versions of Ornith 1.0 • 6 items • Updated Jun 27 • 4