How to use from the
Use from the
MLX library
# 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-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED-MLX-mixed-10bit")
config = load_config("TheCluster/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED-MLX-mixed-10bit")

# 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)
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Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED

Quality: quantized (mixed quants per tensor, group size: 32, 10.101 bpw). Most layers use 8-bit affine quantization with a group size 32; some layers are saved in bf16.

Fully uncensored and fine-tuned (by DavidAU) using Claude 4.6 large distill dataset.

This version is INSTRUCT, with modified jinja template which put this model into "instruct only" mode.

The model weights were updated on April 14.

Abliteration metrics

Metric This model Original model (Qwen/Qwen3.5-9B)
KL divergence 0.0793 0 (by definition)
Refusals 6/100 100/100

Benchmarks:

         arc   arc/e boolq hswag obkqa piqa  wino

HERETIC verison (this model):
mxfp8    0.574,0.755,0.869,0.714,0.410,0.780,0.691

Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT
mxfp8    0.574,0.729,0.882,0.711,0.422,0.775,0.691

Qwen3.5-9B
mxfp8    0.417,0.458,0.623,0.634,0.338,0.737,0.639

Source

This model was converted to MLX format from DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED using mlx-vlm version 0.4.4.

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