Image-Text-to-Text
MLX
Safetensors
qwen3_5
heretic
uncensored
unrestricted
decensored
abliterated
bfloat16
conversational
Instructions to use TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16 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("TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16") config = load_config("TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16") # 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 TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16"
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": "TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16 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 "TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16"
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 TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16"
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 "TheCluster/Crow-9B-HERETIC-4.6-MLX-bf16" \ --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"
Update README.md
Browse files
README.md
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pipeline_tag: image-text-to-text
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---
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# CROW-9B Heretic
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**Quality**: original bfloat16
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-----
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### Source
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pipeline_tag: image-text-to-text
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language:
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- en
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---
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# CROW-9B Heretic
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**Quality**: original bfloat16
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**`Architecture:`** `Qwen 3.5` | **`Parameters:`** `9 Billion` | **`Teacher Model:`** `Claude Opus 4.6` | **`Type:`** `Distilled LLM`
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### 🌟 Model Highlights
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* **Distilled Excellence:** Captures the deep reasoning, nuanced formatting, and instruction-following capabilities of Claude Opus 4.6.
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* **Highly Agile:** At just 9B parameters, Crow runs efficiently on consumer-grade GPUs and edge devices without sacrificing contextual depth.
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* **Qwen 3.5 Backbone:** Inherits robust multilingual support, a massive context window, and structural stability.
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More information [`here`](https://huggingface.co/Crownelius/Crow-9B-HERETIC-4.6)
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-----
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### Source
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