Instructions to use InterpreticAI/Qwen3.8-27B-Uncensored-v2-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InterpreticAI/Qwen3.8-27B-Uncensored-v2-FP8 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-FP8")
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-FP8")
model = AutoModelForMultimodalLM.from_pretrained("InterpreticAI/Qwen3.8-27B-Uncensored-v2-FP8", 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-FP8 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-FP8"
# 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-FP8",
		"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-FP8

SGLang

How to use InterpreticAI/Qwen3.8-27B-Uncensored-v2-FP8 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-FP8" \
    --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-FP8",
		"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-FP8" \
        --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-FP8",
		"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-FP8 with Docker Model Runner:
```
docker model run hf.co/InterpreticAI/Qwen3.8-27B-Uncensored-v2-FP8
```

Qwen3.8-27B-Uncensored-v2-FP8

This is the FP8 release of Qwen3.8-27B-Uncensored-v2, a variant of Qwen3.8-27B with political censorship largely removed. It uses the same layout and quantization scheme as the official Qwen/Qwen3.8-27B-FP8 (block-wise FP8 E4M3 with 128 × 128 blocks). The checkpoint is 29 GB.

Other formats: BF16 · GGUF (Q4_K_M)

Evaluation

The settings are identical to those described on the BF16 model card.

1. Political censorship (Pan & Xu, 2026)

113 of the 115 politically sensitive questions (No. 6 and No. 109 excluded as knowledge gaps of the base model; see the BF16 card); non-thinking mode, greedy decoding, 4,096 maximum output tokens; each response labeled as censored or uncensored by an LLM judge.

Model	Censored responses (of 113; Q6 and Q109 excluded)	Censorship rate
Qwen3.8-27B	98	86.7%
Qwen3.8-27B-Uncensored-v2-FP8	3	2.7%

Uncensored-v2-FP8 removes 96.9% of the censored responses of Qwen3.8-27B (95 of 98).

Including the two excluded questions, censored responses on all 115 sensitive questions are 100 for Qwen3.8-27B and 4 for Qwen3.8-27B-Uncensored-v2-FP8.

2. Refusal of harmful requests

First 100 prompts of the mlabonne/harmful_behaviors test split (AdvBench-derived).

Model	Harmful requests refused (of 100)
Qwen3.8-27B	100
Qwen3.8-27B-Uncensored-v2-FP8	97

3. General capability

Capability benchmarks were run on the BF16 release:

Benchmark	Reference	Reference score	Uncensored-v2 (BF16)	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

vllm serve InterpreticAI/Qwen3.8-27B-Uncensored-v2-FP8 --reasoning-parser qwen3

We recommend the sampling parameters published for Qwen3.8-27B: 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.

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 and Qwen/Qwen3.8-27B-FP8. 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_fp8,
  title        = {Qwen3.8-27B-Uncensored-v2-FP8},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/InterpreticAI/Qwen3.8-27B-Uncensored-v2-FP8}}
}