Instructions to use Rootkit7/Qwen3.6-27B-abliterated-b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rootkit7/Qwen3.6-27B-abliterated-b with Transformers:

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

pipe = pipeline("text-generation", model="Rootkit7/Qwen3.6-27B-abliterated-b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rootkit7/Qwen3.6-27B-abliterated-b")
model = AutoModelForCausalLM.from_pretrained("Rootkit7/Qwen3.6-27B-abliterated-b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rootkit7/Qwen3.6-27B-abliterated-b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rootkit7/Qwen3.6-27B-abliterated-b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rootkit7/Qwen3.6-27B-abliterated-b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rootkit7/Qwen3.6-27B-abliterated-b

SGLang

How to use Rootkit7/Qwen3.6-27B-abliterated-b 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 "Rootkit7/Qwen3.6-27B-abliterated-b" \
    --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": "Rootkit7/Qwen3.6-27B-abliterated-b",
		"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 "Rootkit7/Qwen3.6-27B-abliterated-b" \
        --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": "Rootkit7/Qwen3.6-27B-abliterated-b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rootkit7/Qwen3.6-27B-abliterated-b with Docker Model Runner:
```
docker model run hf.co/Rootkit7/Qwen3.6-27B-abliterated-b
```

Qwen3.6-27B-abliterated-b

Refusal-ablated Qwen/Qwen3.6-27B (dense, 27B, hybrid Gated-Delta-Net + softmax, thinking model), produced with Solutus using the band_directional technique.

Research artifact — private. Intended for safety/robustness research on refusal mechanisms. Ablating refusal removes safety guardrails; use responsibly and under the base model's licence.

Technique

band_directional estimates the refusal direction and projects it out of the residual-writing weights (attention o_proj / linear-attn out_proj, MLP down_proj) across a KL-guarded band of layers — any layer whose edit pushes KL divergence past the guard is automatically reverted, so the edit stays as shallow as it can while still removing refusal.

Recipe

knob	value
technique	`band_directional`
n_directions	8
keep_frac	0.15 (band = decoder layers 18–56)
kl_guard	1.3 (KL-reverted layers: 30, 42, 44)
selection	cosmic
norm_preserve	false
layers edited	36 (302 weight tensors modified)

Results (held-out)

metric	value
refusal rate	0.0% (n=30, 95% CI [0.00, 0.11])
coherent compliance	100%
degenerate fraction	0%
KL divergence (vs base)	1.035
MMLU	0.75
GSM8K	0.825
capability gate	pass

Base Qwen3.6-27B under the same thinking-aware harness scores MMLU ≈ 0.84 — abliteration retains the bulk of general capability at zero measured refusal. (GSM8K is reported at small n; treat as indicative.)

Notes

The base is a hybrid (Gated-Delta-Net linear-attention + softmax) thinking model; both attention residual-write paths were ablated, and evaluation is thinking-aware.
solutus_metadata.json carries full provenance. Its ppl_delta field is a known-broken corpus-perplexity diagnostic for hybrid/thinking models — ignore it; MMLU/GSM8K are the capability signals used by the gate.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Rootkit7/Qwen3.6-27B-abliterated-b")
model = AutoModelForCausalLM.from_pretrained("Rootkit7/Qwen3.6-27B-abliterated-b", torch_dtype="auto", device_map="auto")

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Safetensors

Model size

27B params

Tensor type

BF16

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Base model

Qwen/Qwen3.6-27B

Finetuned

(372)

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