Image-Text-to-Text
MLX
Safetensors
qwen3_5
ornith
ornith-1.5
multimodal
abliterated
mlx-vlm
conversational
8-bit precision
Instructions to use PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit 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("PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit") config = load_config("PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit") # 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 PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit"
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": "PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit 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 "PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit"
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 PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit"
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 "PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit" \ --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"
| { | |
| "schema_version": 1, | |
| "model": "PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit", | |
| "status": "release-candidate", | |
| "base_model": "ornith-ai/Ornith-1.5-9B", | |
| "base_revision": "c927ad73b7eb20f00aafcaa0a11a9d58ed5487bc", | |
| "format": "MLX-VLM", | |
| "precision": "affine 8-bit/group 64 RTN", | |
| "abliteration_scale": 1.0, | |
| "behavioral_scorer": "transparent phrase-based explicit-refusal detector", | |
| "manual_review_caveat": "Zero phrase flags does not mean universal literal compliance; manual review found semantic redirects that the phrase detector did not flag.", | |
| "evaluation": { | |
| "refusal_screen": { | |
| "capability": { | |
| "count": 12, | |
| "pass_count": 12, | |
| "pass_rate": 1.0 | |
| }, | |
| "jbb_benign": { | |
| "count": 100, | |
| "explicit_refusal_count": 0, | |
| "explicit_refusal_rate": 0.0, | |
| "final_answer_count": 100, | |
| "completed_answer_count": 3, | |
| "evasive_nonanswer_count": 0, | |
| "evasive_nonanswer_rate": 0.0, | |
| "truncated_generation_count": 97, | |
| "unfinished_reasoning_count": 0 | |
| }, | |
| "jbb_harmful": { | |
| "count": 100, | |
| "explicit_refusal_count": 0, | |
| "explicit_refusal_rate": 0.0, | |
| "final_answer_count": 100, | |
| "completed_answer_count": 6, | |
| "evasive_nonanswer_count": 0, | |
| "evasive_nonanswer_rate": 0.0, | |
| "truncated_generation_count": 94, | |
| "unfinished_reasoning_count": 0 | |
| } | |
| }, | |
| "capability": { | |
| "results": { | |
| "count": 80, | |
| "pass_count": 72, | |
| "pass_rate": 0.9 | |
| }, | |
| "failed_case_ids": [ | |
| "math-04-fractions", | |
| "math-08-arithmetic-mean", | |
| "math-13-probability", | |
| "math-15-order-operations", | |
| "instruction-13-repeat", | |
| "instruction-14-remove-vowels", | |
| "code-10-rust-option", | |
| "language-03-german" | |
| ], | |
| "categories": { | |
| "coding": { | |
| "count": 10, | |
| "passCount": 9, | |
| "passRate": 0.9, | |
| "failedCaseIds": [ | |
| "code-10-rust-option" | |
| ] | |
| }, | |
| "context_comprehension": { | |
| "count": 5, | |
| "passCount": 5, | |
| "passRate": 1.0, | |
| "failedCaseIds": [] | |
| }, | |
| "factuality_false_premise": { | |
| "count": 15, | |
| "passCount": 15, | |
| "passRate": 1.0, | |
| "failedCaseIds": [] | |
| }, | |
| "general_coherence": { | |
| "count": 5, | |
| "passCount": 5, | |
| "passRate": 1.0, | |
| "failedCaseIds": [] | |
| }, | |
| "instruction_following": { | |
| "count": 15, | |
| "passCount": 13, | |
| "passRate": 0.8666666666666667, | |
| "failedCaseIds": [ | |
| "instruction-13-repeat", | |
| "instruction-14-remove-vowels" | |
| ] | |
| }, | |
| "math_reasoning": { | |
| "count": 15, | |
| "passCount": 11, | |
| "passRate": 0.7333333333333333, | |
| "failedCaseIds": [ | |
| "math-04-fractions", | |
| "math-08-arithmetic-mean", | |
| "math-13-probability", | |
| "math-15-order-operations" | |
| ] | |
| }, | |
| "multilingual": { | |
| "count": 5, | |
| "passCount": 4, | |
| "passRate": 0.8, | |
| "failedCaseIds": [ | |
| "language-03-german" | |
| ] | |
| }, | |
| "structured_output": { | |
| "count": 10, | |
| "passCount": 10, | |
| "passRate": 1.0, | |
| "failedCaseIds": [] | |
| } | |
| } | |
| }, | |
| "runtime_smoke": { | |
| "passed": true, | |
| "peak_memory_gb": 12.008178454, | |
| "text_passed": true, | |
| "vision_passed": true | |
| } | |
| } | |
| } | |