Instructions to use llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1")
model = AutoModelForImageTextToText.from_pretrained("llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1")
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

vLLM

How to use llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1",
		"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/llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1

SGLang

How to use llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1 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 "llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1" \
    --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": "llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1",
		"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 "llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1" \
        --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": "llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1 with Docker Model Runner:
```
docker model run hf.co/llmfan46/Qwen3.5-27B-ultra-uncensored-heretic-v1
```

llmfan46 commited on Mar 31

Commit

0b134ea

verified ·

1 Parent(s): 9d6bc0e

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@@ -23,6 +23,22 @@ base_model:
 ---
 # This is a decensored version of [Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with Magnitude-Preserving Orthogonal Ablation (MPOA) and Self-Organizing Map Abliteration (SOMA)
 ## Abliteration parameters
@@ -72,7 +88,7 @@ base_model:
 | Metric | This model | Original model ([Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B)) |
 | :----- | :--------: | :---------------------------: |
 | **KL divergence** | <span style="color:darkgoldenrod">0.0301</span> | 0 *(by definition)* |
-| **Refusals** | ✅ <span style="color:darkgreen">3/100</span> | ❌ <span style="color:blue">94/100</span> |
 ## PIQA test results with batch size 128:

 ---
+### **97% fewer refusals** (3/100 Uncensored vs 95/100 Original) while preserving model quality (0.0301 KL divergence).
+## ❤️ Support My Work
+Creating these models takes significant time, work and compute. If you find them useful consider supporting me:
+![image/png](https://huggingface.co/llmfan46/Omega-Darker-Gaslight_The-Final-Forgotten-Fever-Dream-24B-ultra-uncensored-heretic-v1/resolve/main/waifu001.webp)
+| Platform | Link | What you get |
+|----------|------|--------------|
+| 🎉 Patreon | [Monthly support](https://patreon.com/LLMfan46) | Priority model requests |
+| ☕ Ko-fi | [One-time tip](https://ko-fi.com/llmfan46) | My eternal gratitude |
+Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.
+-----
 # This is a decensored version of [Qwen/Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with Magnitude-Preserving Orthogonal Ablation (MPOA) and Self-Organizing Map Abliteration (SOMA)
 ## Abliteration parameters
 | Metric | This model | Original model ([Qwen3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B)) |
 | :----- | :--------: | :---------------------------: |
 | **KL divergence** | <span style="color:darkgoldenrod">0.0301</span> | 0 *(by definition)* |
+| **Refusals** | ✅ <span style="color:darkgreen">3/100</span> | ❌ <span style="color:blue">95/100</span> |
 ## PIQA test results with batch size 128: