language:
- en
- zh
license: apache-2.0
tags: - unsloth
- heretic
- uncensored
- abliterated
- fine tune
- creative
- creative writing
- fiction writing
- plot generation
- sub-plot generation
- fiction writing
- story generation
- scene continue
- storytelling
- fiction story
- science fiction
- romance
- all genres
- story
- writing
- vivid prosing
- vivid writing
- fiction
- roleplaying
- bfloat16
- all use cases
- transformers
- mlx
library_name: transformers
pipeline_tag: image-text-to-text
datasets: - TeichAI/polaris-alpha-1000x
base_model: - DavidAU/Qwen3.5-27B-HERETIC-Polaris-Advanced-Thinking-Alpha-uncensored
Qwen3.5-27B-HERETIC-Polaris-Advanced-Thinking-Alpha-uncensored-mxfp4-mlx
Brainwaves from the non-Heretic model shown
arc arc/e boolq hswag obkqa piqa wino
mxfp4 0.473,0.548,0.709,0.728,0.396,0.777,0.753
Quant Perplexity Peak Memory
qx64-hi 3.931 ± 0.025 26.62 GB
Similar thinking models
TeichAI/Qwen3.5-27b-Opus-4.6-Distill
qx64-hi 0.459,0.542,0.724,0.764,0.402,0.790,0.783
DavidAU/Qwen3.5-27B-Claude-4.6-OS-Auto-Variable-Thinking
mxfp8 0.485,0.566,0.875,0.746,0.408,0.789,0.730
Instruct models
DavidAU/Qwen3.5-27B-Claude-4.6-OS-INSTRUCT
mxfp8 0.675,0.827,0.900,0.750,0.496,0.800,0.721
qx86-hi 0.667,0.822,0.900
qx64-hi 0.664,0.820,0.902
mxfp4 0.653,0.815,0.899
Older VL models
Huihui-Qwen3-VL-32B-Thinking-abliterated
qx86-hi 0.376,0.449,0.823,0.637,0.378,0.772,0.681
unsloth-Qwen3-VL-32B-Instruct
qx86x-hi 0.447,0.593,0.904,0.610,0.432,0.738,0.594
Specialized older VL models
nightmedia/Qwen3-32B-Element5-Heretic
qx86-hi 0.483,0.596,0.738,0.754,0.394,0.802,0.710
UIGEN-FX-Agentic-32B
qx86-hi 0.460,0.527,0.681,0.721,0.404,0.790,0.728
-G
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3.5-27B-HERETIC-Polaris-Advanced-Thinking-Alpha-uncensored-mxfp4-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)