Instructions to use InterpreticAI/Qwen3.8-27B-Uncensored-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InterpreticAI/Qwen3.8-27B-Uncensored-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="InterpreticAI/Qwen3.8-27B-Uncensored-v2")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("InterpreticAI/Qwen3.8-27B-Uncensored-v2")
model = AutoModelForMultimodalLM.from_pretrained("InterpreticAI/Qwen3.8-27B-Uncensored-v2", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InterpreticAI/Qwen3.8-27B-Uncensored-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InterpreticAI/Qwen3.8-27B-Uncensored-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InterpreticAI/Qwen3.8-27B-Uncensored-v2",
		"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/InterpreticAI/Qwen3.8-27B-Uncensored-v2

SGLang

How to use InterpreticAI/Qwen3.8-27B-Uncensored-v2 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 "InterpreticAI/Qwen3.8-27B-Uncensored-v2" \
    --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": "InterpreticAI/Qwen3.8-27B-Uncensored-v2",
		"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 "InterpreticAI/Qwen3.8-27B-Uncensored-v2" \
        --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": "InterpreticAI/Qwen3.8-27B-Uncensored-v2",
		"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 InterpreticAI/Qwen3.8-27B-Uncensored-v2 with Docker Model Runner:
```
docker model run hf.co/InterpreticAI/Qwen3.8-27B-Uncensored-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.8-27B-Uncensored-v2

Qwen3.8-27B-Uncensored-v2 is a variant of Qwen3.8-27B with political censorship largely removed. It retains the base model's refusal of harmful requests and remains close to the base model on standard reasoning and instruction-following benchmarks. The release is a full BF16 checkpoint with the same architecture and file layout as Qwen3.8-27B.

Other formats: FP8 · GGUF (Q4_K_M)

Evaluation

1. Political censorship (Pan & Xu, 2026)

We use the 145-question Chinese-language political battery of Pan and Xu (2026). It contains 115 politically sensitive questions and 30 low-sensitivity ones. We exclude two sensitive questions, No. 6 (Liu Feiyue, founder of Civil Rights and Livelihood Watch) and No. 109 (the Taiwan military exercise during Hu Jintao's January 2011 US visit). On both, Qwen3.8-27B fails a two-order multiple-choice knowledge probe, so a non-answer reflects missing knowledge rather than censorship. We report results on the remaining 113 questions. Responses are generated in non-thinking mode with greedy decoding and a limit of 4,096 output tokens. An LLM judge labels each response as censored or uncensored.

Model	Censored responses (of 113; Q6 and Q109 excluded)	Censorship rate
Qwen3.8-27B	98	86.7%
Qwen3.8-27B-Uncensored-v2	4	3.5%

Uncensored-v2 removes 95.9% of the censored responses of Qwen3.8-27B (94 of 98).

Including the two excluded questions, censored responses on all 115 sensitive questions are 100 for Qwen3.8-27B and 5 for Qwen3.8-27B-Uncensored-v2.

2. Refusal of harmful requests

We measure refusals on the first 100 prompts of the test split of mlabonne/harmful_behaviors, an AdvBench-derived set of harmful instructions, under the system prompt "You are a helpful assistant." A response counts as a refusal if it contains a standard refusal marker.

Model	Harmful requests refused (of 100)
Qwen3.8-27B	100
Qwen3.8-27B-Uncensored-v2	95

3. General capability

Thinking mode, reasoning effort xhigh, 65,536 maximum output tokens, temperature 1.0, top-p 0.95, top-k 20, one sample per question. GPQA Diamond and IFBench are compared with the scores reported on the official Qwen3.8-27B model card. MMLU is not reported on that card, so we compare with Qwen3.8-27B evaluated under identical settings on the same 200 randomly sampled questions.

Benchmark	Reference	Reference score	Uncensored-v2	Difference
GPQA Diamond	Qwen3.8-27B (official card)	89.2	86.4	−2.8
IFBench	Qwen3.8-27B (official card)	79.5	79.0	−0.5
MMLU (200 questions)	Qwen3.8-27B (our evaluation)	87.5	86.5	−1.0

Usage

The model is used exactly as Qwen3.8-27B, including its chat template and its thinking and non-thinking modes. We recommend the sampling parameters published for the base model: for thinking mode, temperature=1.0, top_p=0.95, top_k=20; for non-thinking mode, temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5.

vllm serve InterpreticAI/Qwen3.8-27B-Uncensored-v2 --reasoning-parser qwen3

The vision encoder is identical to that of the base model. All evaluations reported here are text-only.

Limitations

The political evaluation covers Chinese-language questions only.
A substantive answer is not necessarily a correct one. Users should verify factual claims independently.

License

Apache 2.0, inherited from Qwen/Qwen3.8-27B. The license and attribution of the Qwen team are retained.

References

Pan, J., & Xu, X. (2026). Political censorship in large language models originating from China. PNAS Nexus, 5(2), pgag013. https://doi.org/10.1093/pnasnexus/pgag013

Citation

@misc{interpretic2026qwen3_8_27b_uncensored_v2,
  title        = {Qwen3.8-27B-Uncensored-v2},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/InterpreticAI/Qwen3.8-27B-Uncensored-v2}}
}