Instructions to use Jiunsong/SuperQwen3.8-27b-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jiunsong/SuperQwen3.8-27b-abliterated with Transformers:

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

pipe = pipeline("image-text-to-text", model="Jiunsong/SuperQwen3.8-27b-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("Jiunsong/SuperQwen3.8-27b-abliterated")
model = AutoModelForMultimodalLM.from_pretrained("Jiunsong/SuperQwen3.8-27b-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 Jiunsong/SuperQwen3.8-27b-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jiunsong/SuperQwen3.8-27b-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": "Jiunsong/SuperQwen3.8-27b-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/Jiunsong/SuperQwen3.8-27b-abliterated

SGLang

How to use Jiunsong/SuperQwen3.8-27b-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 "Jiunsong/SuperQwen3.8-27b-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": "Jiunsong/SuperQwen3.8-27b-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 "Jiunsong/SuperQwen3.8-27b-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": "Jiunsong/SuperQwen3.8-27b-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 Jiunsong/SuperQwen3.8-27b-abliterated with Docker Model Runner:
```
docker model run hf.co/Jiunsong/SuperQwen3.8-27b-abliterated
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SuperQwen3.8-27b-abliterated

The full-BF16 SuperQwen3.8 release: refusal-reduced, overthinking-corrected, multimodal, tool-capable, and verified across the native 262K window.

SuperQwen3.8-27b-abliterated is a directly loadable, full-BF16 weight release built from Qwen/Qwen3.8-27B@1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0. It applies a measured rank-4 OBLITERATUS refusal-subspace edit while preserving the official vision tower and MTP weights exactly. No LoRA or inference-time adapter is required.

Release highlights


Format	Full BF16, 18 safetensors shards, about 52 GB
Targeted edit	100 tensors: output projections in layers 15-63 plus embeddings and `lm_head`
Protected exactly	333 vision tensors and 15 MTP tensors
Refusal shift	30/32 (93.75%) → 0/32, with 0 empty outputs
Overthinking correction	Default `xhigh` → bounded `medium`; repeat/restart guard for explicit `xhigh`; 36/36 pass
Measured decode	4.3411 tok/s C1 at p256
Verified context	262,043 prompt tokens, needle retrieved in the native window
OBLITERATUS corpus	842 canonical harmful/harmless pairs, pinned commit `a5a1ffa5849b`

Why this release

Original-weight quality: BF16 transformer, vision, output head, embeddings, and MTP storage.
Less refusal without wrecking capabilities: capability floor 7/8, tool call PASS, vision PASS.
Reasoning that stops: all nine deterministic tasks pass at default, low, medium, and xhigh.
Multimodal preserved: this remains an image-text-to-text checkpoint, not a text-only conversion.
Reproducible: the exact parent revision, modified tensor list, and evidence hashes ship with the model.

Bounded reasoning

The upstream template defaulted unspecified reasoning to xhigh. This release defaults to medium and adds a stop condition to xhigh: once an answer is established, the model must stop instead of repeating or restarting its deliberation. The checkpoint was tested across 36 deterministic effort/task combinations; all 36 terminated correctly.

Explicit controls remain available through chat_template_kwargs:

extra_body={"chat_template_kwargs": {"enable_thinking": True, "reasoning_effort": "xhigh"}}

Behavior and capability

Gate	Result
Parent refusal	30 / 32 (93.75%)
SuperQwen refusal	0 / 32
Empty output	0 / 32
Capability	7 / 8 (paired-parent floor)
Tool use	PASS
Vision	PASS
Overthinking	36 / 36 PASS

Precision and integrity

Full BF16 checkpoint; this repository is not quantized.
100 declared tensors changed and zero unexpected tensors changed.
Vision (333 tensors) and MTP (15 tensors) remain byte/value exact.
Parent revision is pinned to 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0.

Decode performance

Measured on one DGX Spark with fixed-length generation and the sparkDash-style post-first-token contract:

Prompt / concurrency	Aggregate decode
p256 / C1	4.3411 tok/s

Verified long context

The official native limit is 262,144 tokens. A 262,043-token prompt completed end to end and retrieved its hidden needle. Long-context acceptance is not a claim of perfect recall on every task.

Serving

QWEN38_SPECULATIVE_TOKENS=0   bash repro/scripts/serve_superqwen38_replica.sh   /model SuperQwen3.8-27b-abliterated 8888

Uncensored behavior

“Abliterated” means that the measured refusal direction was reduced. It does not mean that every response is correct, harmless, or suitable for every deployment. Operators remain responsible for access controls and downstream safeguards.

Limitations

Abliteration changes refusal behavior and may surface content the parent declined.
The capability, tool, vision, and overthinking suites are finite regression gates.
Speed is hardware- and runtime-specific.
The 1M expansion run was explicitly stopped before completion and is not claimed as a pass.

Evidence identities

Evidence	SHA-256
abliteration verification	`6d7b8b9abc431ed3845b18438fc48a6cc169e2ba93f83ada54174ae93765a61e`
abliteration recipe	`3c057cf9364e72857d9a48b6c8888c6b438cbfb519459dcdfbf23b22c2bc6796`
842-pair OBLITERATUS corpus manifest	`be770a8469fafe7a9a76db5d82a7893c706c4414e1de6089f1d329b906480aa8`
bounded-thinking template	`352baa520d23c77129d1cb9b007c5480118c174e12e271457ffe915fb466f3fb`
parent refusal baseline	`7a4177613c86ae0db589cb4fc167ff8a0d45b0d49f44e40b90c9405f4b2b9bb6`
BF16 release gate	`38a7093ce40a9aca2fc5e4389c041660ff56c51a4caec8411998a6d02ed48bc5`
BF16 refusal gate	`97c2f745d032c3ed3147007f31e4ee844bb05181072a7365a160d5fc8c316511`
BF16 native 262K retrieval	`01fdbc4bf458f457b9e403ff9fd43b72124b11b448f02bda4e9701eefccfe814`
User-authorized 1M skip (not a pass)	`a2c94a799d483e313debc1e9fb5e0fa234913c9e1c9fa503b8c466d70ad53dee`

License

Apache-2.0, following the upstream Qwen3.8 release.

Downloads last month: 8

Safetensors

Model size

28B params

Tensor type

BF16

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Quantizations

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SuperQwen3.8-27b-abliterated