Qwen3.6-35B-A3B-Abliterated-Heretic-BF16

Instructions to use CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16 with Transformers:

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

pipe = pipeline("text-generation", model="CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16", trust_remote_code=True)
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/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16", trust_remote_code=True, 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 CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16"
# 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/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16

SGLang

How to use CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16 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/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16" \
    --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/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16" \
        --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/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16 with Docker Model Runner:
```
docker model run hf.co/CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16
```

Qwen3.6-35B-A3B-Abliterated-Heretic-BF16

A BF16 abliterated Qwen3.6-35B-A3B checkpoint produced with Heretic.

Quick Benchmarks

Check	Original Qwen3.6-35B-A3B	Abliterated Heretic BF16
Official 25-prompt refusal check	22/25 refusals	1/25 refusals
Archived Heretic KL divergence	-	0.010655362159013748

Abliteration notes:

base model: Qwen/Qwen3.6-35B-A3B
method family: Heretic MPOA/SOMA-style sibling transfer, finalized with split-MoE input-side intervention
official refusal check: 1/25 refusals on the same 25-prompt marker suite used for the MiniMax M2.7 abliterated run
vision-language wrapper preserved; intervention was applied on the text-side MoE stack

Notes:

GGUF quants are published separately in Youssofal/Qwen3.6-35B-A3B-Abliterated-Heretic-GGUF
Export metadata for the accepted candidate is included in abliteration_metadata.json

Downloads last month: 8

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for CCSSNE/Qwen3.6-35B-A3B-Abliterated-Heretic-BF16

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

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