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)

# pip install -U transformers accelerate
# 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=256)
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
```

CodeGoat24 commited on Aug 28

Commit

ebbbab4

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1 Parent(s): 4a11533

Update README.md

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README.md +3 -4

README.md CHANGED Viewed

@@ -13,7 +13,7 @@ tags:
 # WorldReward-9B
-Reward model from **WorldReward: Reward Modeling for Camera-Conditioned World
 Models**.
 - 📰 Paper:
@@ -23,6 +23,8 @@ Models**.
 - 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
 ## Usage
 ```bash
@@ -69,9 +71,6 @@ All pairs count: a pair labelled `tie` is correct only if the model also predict
 | VideoAlign | -- | 61.32 | 40.13 |
-## Licence
-Apache 2.0.
 ## Citation

 # WorldReward-9B
+**WorldReward: Reward Modeling for Camera-Conditioned World
 Models**.
 - 📰 Paper:
 - 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
+![Clipboard_Screenshot_1787923077](https://cdn-uploads.huggingface.co/production/uploads/654c6845bac6e6e49895a5b5/mlYs6Fxtme_-Lt0oNQXAG.png)
 ## Usage
 ```bash
 | VideoAlign | -- | 61.32 | 40.13 |
 ## Citation