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/Qwen3.5-27B-Writer-V2-Uncensored-Heretic-MLX-mixed-9.4bit")
config = load_config("TheCluster/Qwen3.5-27B-Writer-V2-Uncensored-Heretic-MLX-mixed-9.4bit")

# 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)

Qwen3.5-27B Writer V2 Uncensored Heretic

Quality: quantized (mixed quants per tensor, group size: 32, 9.450 bpw)

Most layers use 8-bit affine quantization with a group size 32; some layers are saved in bf16.

A writing & roleplay finetune of uncensored Qwen3.5 27B. The primary emphasis is on writing quality as it strongly generalizes across both domains.

Recommended Settings:

  • Chatml template with <think>\n\n</think> or {{char}}: prefill. Only non-thinking was trained, but thinking probably still works.
  • temperature = 0.7
  • top_p = 0.95
  • I do not recommend using high rep pen values like Qwen suggests for the base model. rep_pen = 1.05 or a moderate dry setting should suffice.

Source

This model was converted to MLX format from llmfan46/Qwen3.5-27B-Writer-V2-uncensored-heretic using mlx-vlm version 0.4.4.

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