ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking

Instructions to use CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking")
model = AutoModelForMultimodalLM.from_pretrained("CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking

SGLang

How to use CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking",
    max_seq_length=2048,
)

Docker Model Runner
How to use CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking with Docker Model Runner:
```
docker model run hf.co/CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
```

Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking

Yes... fully uncensored AND fine tuned lightly.

Freedom and brainpower.

Trained on different Heretic base, with different KLD/Refusals.

Model fine tune was used to finalize and "firm up" Heretic / uncensored changes.

The goal here was light, minor fixes rather than full / heavy fine tune.

That being said, the tuning still raised critical metrics.

This is Version 2, using "trohrbaugh" Heretic, which has a lower refusal rate, and tuning bumped up the metrics a bit more too.

This has also positively impacted "NEO-Coder Di-Matrix" (dual imatrix) GGUF quants as well (vs heretic/non heretic too).

https://huggingface.co/DavidAU/Qwen3.6-27B-Heretic-Uncensored-FINETUNE-NEO-CODE-Di-IMatrix-MAX-GGUF

IN HOUSE BENCHMARKS [by Nightmedia]:

         arc-c arc/e boolq hswag obkqa piqa  wino

Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
mxfp8    0.673,0.846,0.905... [instruct mode]

Qwen3.6-27B-Heretic-Uncensored-Finetune-Thinking
mxfp8    0.669,0.835,0.906,...   [instruct mode]

---

BASE UNTUNED MODEL:

Qwen3.6-27B HERETIC (by llmfan46) [instruct mode]
mxfp8    0.644,0.788,0.902,...

Qwen3.6-27B (by Qwen) [instruct mode]
mxfp8    0.647,0.803,0.910,0.773,0.450,0.806,0.742

NOTE: Instruct mode will often test higher than "thinking" mode due to token usage in thinking and context limits.

Heretic Stats:

Metric 			This model 		Original model (Qwen/Qwen3.6-27B)
KL divergence 	0.0469 			0 (by definition)
Refusals 		4/100 			99/100

A KLD of .3 or lower is great, lower than this excellent.

KLD measures HERETIC vs Original model performance:

IE: How closely the Heretic version matchs in generation / token selection vs org model.

EXAMPLE GENERATION:

Q4KS, non imatrix, temp:1, rep pen: 1 [off]

There may be some loss of formatting from copy/paste.

Downloads last month: 69

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for CCSSNE/ansulev-Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking

Base model

trohrbaugh/Qwen3.6-27B-heretic-ara

Finetuned

(14)

this model

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