Instructions to use mlasli/Qwen3.6-27B-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlasli/Qwen3.6-27B-abliterated with Transformers:

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

pipe = pipeline("image-text-to-text", model="mlasli/Qwen3.6-27B-abliterated")
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("mlasli/Qwen3.6-27B-abliterated")
model = AutoModelForMultimodalLM.from_pretrained("mlasli/Qwen3.6-27B-abliterated", device_map="auto")
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 mlasli/Qwen3.6-27B-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlasli/Qwen3.6-27B-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlasli/Qwen3.6-27B-abliterated",
		"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/mlasli/Qwen3.6-27B-abliterated

SGLang

How to use mlasli/Qwen3.6-27B-abliterated 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 "mlasli/Qwen3.6-27B-abliterated" \
    --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": "mlasli/Qwen3.6-27B-abliterated",
		"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 "mlasli/Qwen3.6-27B-abliterated" \
        --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": "mlasli/Qwen3.6-27B-abliterated",
		"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 mlasli/Qwen3.6-27B-abliterated with Docker Model Runner:
```
docker model run hf.co/mlasli/Qwen3.6-27B-abliterated
```

Qwen3.6-27B Abliterated

Uncensored, agent-friendly variant of Qwen3.6-27B — Alibaba's flagship 27B dense coding model.

This is an abliterated (refusal-vector-removed) version of Qwen3.6-27B, designed for use cases where the base model's safety alignment gets in the way: security research, red-teaming, creative writing, and unrestricted coding agents.

Why this model?

Qwen3.6-27B is a beast — it matches Claude 4.5 Opus on Terminal-Bench 2.0 and outperforms the 397B Qwen3.5 MoE on agentic coding. But the base model refuses a lot of legitimate security research prompts.

This variant was abliterated using Heretic (v1.4.0), which surgically removes the refusal direction from the model's residual stream — no fine-tuning, no prompt injection hacks, no degradation to general capabilities.

Abliteration Results

Metric	Value
Base Model	Qwen/Qwen3.6-27B (BF16)
Abliteration Tool	Heretic v1.4.0
Best Trial	#153
Refusal Rate	88% compliance (only 12/100 refused)
KL Divergence	0.0118
Capability Preservation	Near-identical to base model

The extremely low KL divergence (0.0118) means the model's output distribution is virtually unchanged from the original — it just doesn't refuse anymore.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "mlasli/Qwen3.6-27B-abliterated",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("mlasli/Qwen3.6-27B-abliterated")

prompt = "Explain how stack canaries work and how they can be bypassed."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Hardware Requirements

Precision	VRAM
BF16 (full)	~55 GB
4-bit (BitsAndBytes)	~17 GB
8-bit	~28 GB

Fits comfortably on an A100 (80GB), H100, or dual 4090 setup.

Intended Use

🔐 Security research & red-teaming — no more "I can't help with that"
🤖 Unrestricted coding agents — pair with OpenCode, Cline, Aider, etc.
✍️ Creative writing — no content filters
🧪 Experimentation — test prompt injection, jailbreak techniques, alignment research

Limitations

Same architecture as Qwen3.6-27B — same tokenizer, same 262K context window (extensible to 1M)
Abliteration removes refusals but does not add new knowledge or capabilities
Still a 27B model — not suitable for tasks requiring >100B scale reasoning

License

Apache 2.0 — same as the base Qwen3.6-27B model. Commercial use, modification, and redistribution are all permitted.

Citation

@misc{qwen3.6-27b,
    title  = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model},
    author = {{Qwen Team}},
    year   = {2026},
    month  = {April},
    url    = {https://qwen.ai/blog?id=qwen3.6-27b}
}

@misc{heretic,
    title  = {Heretic: Abliteration for Language Models},
    author = {p-e-w},
    url    = {https://github.com/p-e-w/heretic}
}

@misc{mlasli2026abliterated,
    title  = {Qwen3.6-27B Abliterated},
    author = {mlasli},
    year   = {2026},
    url    = {https://huggingface.co/mlasli/Qwen3.6-27B-abliterated}
}

This model was abliterated using the Heretic framework on a single A100 80GB GPU. The original Qwen3.6-27B weights are from Alibaba Cloud and licensed under Apache 2.0.

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Safetensors

Model size

27B params

Tensor type

BF16

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