Instructions to use CodeGoat24/WorldReward-qwen38-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeGoat24/WorldReward-qwen38-27b with Transformers:

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

pipe = pipeline("image-text-to-text", model="CodeGoat24/WorldReward-qwen38-27b")
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-qwen38-27b")
model = AutoModelForMultimodalLM.from_pretrained("CodeGoat24/WorldReward-qwen38-27b", 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-qwen38-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeGoat24/WorldReward-qwen38-27b"
# 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-qwen38-27b",
		"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-qwen38-27b

SGLang

How to use CodeGoat24/WorldReward-qwen38-27b 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-qwen38-27b" \
    --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-qwen38-27b",
		"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-qwen38-27b" \
        --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-qwen38-27b",
		"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-qwen38-27b with Docker Model Runner:
```
docker model run hf.co/CodeGoat24/WorldReward-qwen38-27b
```

WorldReward-qwen38-27b

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())"

Citation

@article{wang2026worldreward,
  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 others},
  journal={arXiv preprint arXiv:2609.03952},
  year={2026}
}