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"
File size: 3,591 Bytes
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"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
}
}
}
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