Qwen3.5-9B Heretic MLX
Collection
8 items • Updated • 7
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 size
9B params
Tensor type
U8
·
U32 ·
BF16 ·
Hardware compatibility
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8-bit
Model tree for TheCluster/Qwen3.5-9B-Ultra-Heretic-MLX-mxfp8
Base model
Qwen/Qwen3.5-9B-Base Finetuned
Qwen/Qwen3.5-9B
# 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)