Image-Text-to-Text
MLX
Safetensors
qwen3_5_moe
mlx-vlm
ornith
qwen3.5-moe
multimodal
abliterated
4-bit precision
conversational
Instructions to use npario/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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("npario/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit") config = load_config("npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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 npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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 "npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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": "npario/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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 "npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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 npario/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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 "npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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 "npario/Ornith-1.5-35B-A3B-Abliterated-MLX-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"
Download release-manifest.json from npario/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit: direct link, hf CLI and curl.
- Browser
- Download file 676 Bytes
-
https://huggingface.co/npario/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit/resolve/main/release-manifest.json
- Command line
-
hf download hf://npario/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit/release-manifest.json
-
curl -L -o release-manifest.json https://huggingface.co/npario/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit/resolve/main/release-manifest.json
676 Bytes
| { | |
| "schema_version": 1, | |
| "generated_at": "2026-08-21T04:03:46.400084+00:00", | |
| "status": "experimental-release", | |
| "repository": "PocketAiHub/Ornith-1.5-35B-A3B-Abliterated-MLX-4bit", | |
| "visibility": "public", | |
| "variant": "4bit", | |
| "model_payload_bytes": 20429166953, | |
| "artifact_manifest_sha256": "976292a0eeb6555c97893370c0c4a070b156ef43fa339edc8c850fa4e9125c5a", | |
| "evidence": { | |
| "README.md": "fc3cc1cab7c09e6d3918400f071db6f85665722961f499fda64d52a490bda896", | |
| "abliteration-manifest.json": "82d49e30e1d0eca38c23e3b748f6e923562ee3ee7803ecfa5b07558c88caf224", | |
| "validation-summary.json": "4a4d9b642926f21685d47607cba54a717787992cab2f93f34c517d23c0fdf692" | |
| } | |
| } | |