Instructions to use Qwen/WebWorld-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/WebWorld-8B with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/WebWorld-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/WebWorld-8B")
model = AutoModelForCausalLM.from_pretrained("Qwen/WebWorld-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Qwen/WebWorld-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/WebWorld-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qwen/WebWorld-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/WebWorld-8B

SGLang

How to use Qwen/WebWorld-8B 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 "Qwen/WebWorld-8B" \
    --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": "Qwen/WebWorld-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Qwen/WebWorld-8B" \
        --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": "Qwen/WebWorld-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/WebWorld-8B with Docker Model Runner:
```
docker model run hf.co/Qwen/WebWorld-8B
```

zikaixiao1 commited on Feb 18

Commit

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@@ -273,12 +273,12 @@ WebWorld-Bench evaluates models using **Factuality Score** (functional correctne
 ## 📝 Citation
 ```bibtex
-@article{WebWorld,
-  title         = {WebWorld: A Large-Scale World Model for Web Agent Training},
-  author        = {Xiao, Zikai and Tu, Jianhong and Zou, Chuhang and Zuo, Yuxin and Li, Zhi and Wang, Peng and Yu, Bowen and Huang, Fei and Lin, Junyang and Liu, Zuozhu},
-  journal       = {arXiv preprint arXiv:YYMM.NNNNN},
-  year          = {20YY},
-  eprint        = {YYMM.NNNNN},
-  archivePrefix = {arXiv},
-  primaryClass  = {cs.AI},
-  url           = {https://arxiv.org/abs/YYMM.NNNNN

 ## 📝 Citation
 ```bibtex
+@misc{xiao2026webworldlargescaleworldmodel,
+      title={WebWorld: A Large-Scale World Model for Web Agent Training},
+      author={Zikai Xiao and Jianhong Tu and Chuhang Zou and Yuxin Zuo and Zhi Li and Peng Wang and Bowen Yu and Fei Huang and Junyang Lin and Zuozhu Liu},
+      year={2026},
+      eprint={2602.14721},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2602.14721},
+}