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

Libraries

How to use hkust-nlp/WebExplorer-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="hkust-nlp/WebExplorer-8B")
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 AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hkust-nlp/WebExplorer-8B")
model = AutoModelForCausalLM.from_pretrained("hkust-nlp/WebExplorer-8B", 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 = 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hkust-nlp/WebExplorer-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hkust-nlp/WebExplorer-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": "hkust-nlp/WebExplorer-8B",
		"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/hkust-nlp/WebExplorer-8B

SGLang

How to use hkust-nlp/WebExplorer-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 "hkust-nlp/WebExplorer-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": "hkust-nlp/WebExplorer-8B",
		"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 "hkust-nlp/WebExplorer-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": "hkust-nlp/WebExplorer-8B",
		"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 hkust-nlp/WebExplorer-8B with Docker Model Runner:
```
docker model run hf.co/hkust-nlp/WebExplorer-8B
```

Junteng commited on Sep 9, 2025

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@@ -11,8 +11,9 @@ pipeline_tag: text-generation
 ---
 # 🔍 WebExplorer-8B
-<!-- [![Model](https://img.shields.io/badge/Model-8B_Parameters-blue)](https://huggingface.co/WebExplorer-8B)
-[![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/xxxx.xxxxx) -->
 A state-of-the-art 8B parameter web agent model designed for complex information-seeking tasks and long-horizon reasoning.
@@ -116,3 +117,18 @@ WebExplorer-8B supports two tools for web interaction:

 ---
 # 🔍 WebExplorer-8B
+[![Paper](https://img.shields.io/badge/Paper-arXiv-red.svg)](https://arxiv.org/abs/2509.06501)
+[![License](https://img.shields.io/badge/License-Apache--2.0-green.svg)](LICENSE)
+[![GitHub](https://img.shields.io/badge/GitHub-WebExplorer-blue?logo=github)](https://github.com/hkust-nlp/WebExplorer)
 A state-of-the-art 8B parameter web agent model designed for complex information-seeking tasks and long-horizon reasoning.
+## 📝 Citation
+If you find our work useful, please consider citing:
+```bibtex
+@misc{liu2025webexplorer,
+      title={WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents},
+      author={Junteng Liu and Yunji Li and Chi Zhang and Jingyang Li and Aili Chen and Ke Ji and Weiyu Cheng and Zijia Wu and Chengyu Du and Qidi Xu and Jiayuan Song and Zhengmao Zhu and Wenhu Chen and Pengyu Zhao and Junxian He},
+      year={2025},
+      eprint={2509.06501},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2509.06501},
+}
+```