Instructions to use mlx-community/Ornith-1.0-35B-OptiQ-4bit 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-OptiQ-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-OptiQ-4bit") config = load_config("mlx-community/Ornith-1.0-35B-OptiQ-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
- Pi
How to use mlx-community/Ornith-1.0-35B-OptiQ-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-OptiQ-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-OptiQ-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use mlx-community/Ornith-1.0-35B-OptiQ-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-OptiQ-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-OptiQ-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mlx-community/Ornith-1.0-35B-OptiQ-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-OptiQ-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-OptiQ-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"
Suggesting re-quantization of this model from source to OptiQ-4bit
Hello Folks,
As always thanks for all the Great work and the helpful model files,
Anecdotally, I feel this version could benefit from reprocessing or re-quantization into the exact same 4bit Optiq, perhaps with a quality first setting.
Anecdotally, I observe it is more prone to skip instructions or to go into endless loops while thinking as compared to other four-bit models that are also based on the Quen architecture, the 6 bit OptiQ version however Runs perfectly with immense observed difference in instruction following and general quality of outputs.
I know this is an anecdotal observation but wanted to share it,if you have time to reprocess this file with perhaps the latest Python versions from OpitQ and the latest quality focused settings.
Thank you.
Thanks for the detailed feedback and for sharing your observations. The comparison with the 6-bit OptiQ version is particularly useful. We’ll take a closer look at the quantization process and instruction-following behavior, and consider reprocessing it with the latest OptiQ tooling and quality-focused settings if we can reproduce the issue.
We'll investigate further
Thank you Siddh and jorgemunozl, many people will benefit from running models locally who can't afford to pay the monthly premium.
That’s the mission — putting capable AI back in the hands of the people, locally and without the paywall.