Instructions to use lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX 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("lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX")
config = load_config("lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX")

# 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 lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX"

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": "lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX 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 "lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX"

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 lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX"

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 "lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX" \
  --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"

lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX

This quantization preserves the model's BF16 vision tower. For a text-only version check lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX-TextOnly.

You can download MTP heads separately from this collection Ornith 1.5 35B-A3B MLX-Quants (Vision, Text-Only & MTP).

Hugging Face might render incorrect parameter counts for this model, it is a common display bug for MLX quants.

Credits

Converted to MLX format from ornith-ai/Ornith-1.5-35B-A3B using mlx-vlm version 0.6.17.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

U32

MLX

Hardware compatibility

2-bit

Model tree for lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX

Base model

ornith-ai/Ornith-1.5-35B-A3B

Quantized

(132)

this model

Collection including lukaskremla/Ornith-1.5-35B-A3B-2bit-MLX

Ornith 1.5 35B A3B MLX-Quants (Vision, Text-Only & MTP)

Collection

Complete quantized MLX conversion of Ornith 1.5 35B A3B. This collection contains vision-capable, text-only and all MTP quants. • 19 items • Updated about 12 hours ago