Instructions to use mlx-community/Ornith-1.0-35B-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Ornith-1.0-35B-6bit 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-6bit") config = load_config("mlx-community/Ornith-1.0-35B-6bit") # 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
- Pi
How to use mlx-community/Ornith-1.0-35B-6bit 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-6bit"
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-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/Ornith-1.0-35B-6bit 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-6bit"
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-6bit
Run Hermes
hermes
Ornith-1.0-35B-6bit
6-bit (group size 64, 6.622 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 6-bit quant.
Usage
uvx --from mlx-vlm mlx_vlm.generate \
--model mlx-community/Ornith-1.0-35B-6bit --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-6bit")
Conversion check
Smoke-tested after conversion (mlx_vlm.generate on an image): coherent — correctly read an
evaluation bar chart, no repetition loop. 102.4 tok/s generation, 964.8 tok/s prompt,
peak 31.1 GB on a Macbook Pro M5 Max 128GB 40 GPU.
Refer to the original model card for architecture, benchmarks, license, and intended use.
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6-bit
Model tree for mlx-community/Ornith-1.0-35B-6bit
Base model
deepreinforce-ai/Ornith-1.0-35B