Instructions to use chisato111/SPARED-Qwen3.5-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chisato111/SPARED-Qwen3.5-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="chisato111/SPARED-Qwen3.5-9B")
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("chisato111/SPARED-Qwen3.5-9B")
model = AutoModelForMultimodalLM.from_pretrained("chisato111/SPARED-Qwen3.5-9B", 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 chisato111/SPARED-Qwen3.5-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chisato111/SPARED-Qwen3.5-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chisato111/SPARED-Qwen3.5-9B",
		"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/chisato111/SPARED-Qwen3.5-9B

SGLang

How to use chisato111/SPARED-Qwen3.5-9B 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 "chisato111/SPARED-Qwen3.5-9B" \
    --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": "chisato111/SPARED-Qwen3.5-9B",
		"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 "chisato111/SPARED-Qwen3.5-9B" \
        --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": "chisato111/SPARED-Qwen3.5-9B",
		"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 chisato111/SPARED-Qwen3.5-9B with Docker Model Runner:
```
docker model run hf.co/chisato111/SPARED-Qwen3.5-9B
```

SPARED-Qwen3.5-9B

SPARED-Qwen3.5-9B is the final defender from SPARED: Reasoning-Based AI-Generated Image Detection via Adversarially Edited Data. This release contains the full merged model selected at defender Iteration 3, checkpoint 2600.

The model classifies an input image as real or fake and produces a short forensic explanation. Fully generated and digitally manipulated images are both treated as fake.

Response format

Use the detection prompt distributed with the SPARED code repository. The expected response format is:

<reasoning>Concise visual forensic analysis.</reasoning>
<answer>real</answer>

or:

<reasoning>Concise visual forensic analysis.</reasoning>
<answer>fake</answer>

Loading

from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "chisato111/SPARED-Qwen3.5-9B"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

The released checkpoint was validated with Transformers 5.9.0. Evaluation prompts, decoding parameters, benchmark adapters, and the complete training pipeline are provided in the SPARED code repository.

Training summary

Base model: Qwen/Qwen3.5-9B
Initialization: LoRA supervised fine-tuning
Defender optimization: three verdict-reward GRPO rounds
Adversarial co-evolution: two Qwen-Image-Edit attacker rounds
Released model: defender Iteration 3, checkpoint 2600

Training data and generated training pools are not distributed with this model.

Limitations

The detector can fail under unseen generators, transformations, compression, screenshots, or domain shifts. Generated explanations may sound plausible without identifying the true generation process. Do not use the model as the sole basis for legal, disciplinary, provenance, authorship, or moderation decisions.

License

This model is released under Apache-2.0 and is derived from Qwen/Qwen3.5-9B. Third-party data, benchmarks, and evaluation models are not included.

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Safetensors

Model size

9B params

Tensor type

BF16

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Paper for chisato111/SPARED-Qwen3.5-9B

SPARED: Reasoning-Based AI-Generated Image Detection via Adversarially Edited Data

Paper • 2608.12876 • Published 2 days ago