Instructions to use TheCluster/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TheCluster/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8 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/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8") config = load_config("TheCluster/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8") # 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/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8 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/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/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/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use TheCluster/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8 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/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8"
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/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8
Run Hermes
hermes

Qwen3.5-9B Ultra Heretic
Quality: quantized (mxfp8, group size: 32, 8.626 bpw)
This is a abliterated (uncensored) version of Qwen/Qwen3.5-9B, made using Heretic v1.2.0 with Magnitude-Preserving Orthogonal Ablation (MPOA) and Self-Organizing Map Abliteration (SOMA)
Performance
| Metric | This model | Original model (Qwen3.5-9B) |
|---|---|---|
| KL divergence | 0.1085 | 0 (by definition) |
| Refusals | 2/100 | 86/100 |
Lower refusals indicate fewer content restrictions, while lower KL divergence indicates better preservation of the original model's capabilities.
Alternative fine-tuned version: TheCluster/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED-MLX-mxfp8
Sampling Parameters:
- I suggest using the following sets of sampling parameters depending on the mode and task type:
- Thinking mode for general tasks:
temperature=1.0,top_p=0.95,top_k=20,min_p=0.0,presence_penalty=1.5,repetition_penalty=1.0 - Instruct (or non-thinking) mode for general tasks:
temperature=0.7,top_p=0.8,top_k=20,min_p=0.0,presence_penalty=1.5,repetition_penalty=1.0 - Instruct (or non-thinking) mode for reasoning tasks:
temperature=1.0,top_p=1.0,top_k=40,min_p=0.0,presence_penalty=2.0,repetition_penalty=1.0
- Thinking mode for general tasks:
- For supported frameworks, you can adjust the
presence_penaltyparameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
Source
This model was converted to MLX format from llmfan46/Qwen3.5-9B-ultra-heretic using mlx-vlm version 0.4.
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Model tree for TheCluster/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8
Base model
Qwen/Qwen3.5-9B-Base