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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hkust-nlp/WebExplorer-8B")
model = AutoModelForCausalLM.from_pretrained("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?"}
        ]
    },
]
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]:]))

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
```

Improve model card: Update license, pipeline tag, add agent tag, and full abstract

by nielsr HF Staff - opened Sep 10, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-11

nielsr

Sep 10, 2025

This PR significantly improves the model card for WebExplorer-8B by:

Updating the license metadata from mit to apache-2.0 to accurately reflect the license indicated in the project's GitHub repository.
Changing the pipeline_tag from text-generation to image-text-to-text to better categorize this web agent model, which interacts with and processes information from web pages, potentially involving visual elements, as per Hugging Face guidelines for relevant metadata.
Adding agent to the tags for improved discoverability, given the model's explicit description as a "long-horizon web agent."
Adding a paper metadata tag (2509.06501) to link the model directly to its corresponding paper on the Hugging Face Hub.
Replacing the brief "Overview" section with the full paper abstract to provide more comprehensive and detailed information about the model's approach and findings directly in the model card.

These changes enhance the model card's accuracy, discoverability, and informativeness for the community.

Improve model card: Update license, pipeline tag, add agent tag, and full abstractfd23290c

Junteng changed pull request status to merged Sep 11, 2025

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment