Instructions to use CodeGoat24/WorldReward-qwen35-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeGoat24/WorldReward-qwen35-9b with Transformers:

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

pipe = pipeline("image-text-to-text", model="CodeGoat24/WorldReward-qwen35-9b")
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("CodeGoat24/WorldReward-qwen35-9b")
model = AutoModelForMultimodalLM.from_pretrained("CodeGoat24/WorldReward-qwen35-9b", 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 CodeGoat24/WorldReward-qwen35-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeGoat24/WorldReward-qwen35-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeGoat24/WorldReward-qwen35-9b",
		"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/CodeGoat24/WorldReward-qwen35-9b

SGLang

How to use CodeGoat24/WorldReward-qwen35-9b 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 "CodeGoat24/WorldReward-qwen35-9b" \
    --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": "CodeGoat24/WorldReward-qwen35-9b",
		"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 "CodeGoat24/WorldReward-qwen35-9b" \
        --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": "CodeGoat24/WorldReward-qwen35-9b",
		"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 CodeGoat24/WorldReward-qwen35-9b with Docker Model Runner:
```
docker model run hf.co/CodeGoat24/WorldReward-qwen35-9b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

WorldReward-qwen35-9b

WorldReward: Reward Modeling for Camera-Conditioned World Models.

📰 Paper: https://arxiv.org/abs/2609.03952
🪐 Project Page: https://codegoat24.github.io/WorldReward
🤗 Model Collections: https://huggingface.co/CodeGoat24/WorldReward-qwen35-9b
🚀 Github: https://github.com/CodeGoat24/WorldReward
👋 Point of Contact: Yibin Wang

Usage

git clone https://github.com/CodeGoat24/WorldReward
cd WorldReward && pip install -e .

python examples/run_single_pair.py \
    --input-image  my_data/scene.jpg \
    --left-video   my_data/system_x.mp4 \
    --right-video  my_data/system_y.mp4 \
    --caption      "A sunlit street lined with colorful European-style buildings." \
    --actions      forward,forward,left+camera_down \
    --frames-per-action 8 \
    --show-reasoning

Inference needs vLLM new enough to register Qwen3_5ForConditionalGeneration:

python -c "from vllm.model_executor.models.registry import ModelRegistry as R; \
           print('Qwen3_5ForConditionalGeneration' in R.get_supported_archs())"

Results

Three-way agreement with human labels on WorldReward-Bench (760 pairs, %). All pairs count: a pair labelled tie is correct only if the model also predicts tie.

Reward model	Action	Appearance	Motion
WorldReward-9B	77.63	81.32	73.03
GPT-5.5	74.21	79.87	69.47
Gemini-3.1-Pro	65.79	80.13	60.79
DAv3	70.53	--	--
WorldMirror	68.55	--	--
Qwen3.5-VL-27B (zero-shot)	63.68	44.34	62.76
Qwen3.5-VL-9B (zero-shot)	48.42	48.29	43.82
HPSv3	--	73.68	--
Aesthetic	--	69.87	--
UnifiedReward-Think	--	66.09	38.79
UnifiedReward-Flex	--	64.62	49.86
VideoAlign	--	61.32	40.13

Citation

@article{WorldReward,
  title={WorldReward: Reward Modeling for Camera-Conditioned World Models},
  author={Wang, Yibin and Wang, Zehan and Tang, Junshu and Li, Zhimin and Zhou, Yujie and Bu, Jiazi and Ling, Pengyang and Han, Feng and Zhang, Zhixiong and Xing, Long and Ding, Shengyuan and Li, Ziang and Jin, Cheng and Zang, Yuhang and Wang, Jiaqi and Pang, Tianyu},
  journal={arXiv preprint arXiv:2609.03952},
  year={2026}
}