Instructions to use luzimu/WebGenAgent-LM-7B-Step-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luzimu/WebGenAgent-LM-7B-Step-GRPO with Transformers:

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

pipe = pipeline("image-text-to-text", model="luzimu/WebGenAgent-LM-7B-Step-GRPO")
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("luzimu/WebGenAgent-LM-7B-Step-GRPO")
model = AutoModelForCausalLM.from_pretrained("luzimu/WebGenAgent-LM-7B-Step-GRPO")
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

vLLM

How to use luzimu/WebGenAgent-LM-7B-Step-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luzimu/WebGenAgent-LM-7B-Step-GRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luzimu/WebGenAgent-LM-7B-Step-GRPO",
		"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/luzimu/WebGenAgent-LM-7B-Step-GRPO

SGLang

How to use luzimu/WebGenAgent-LM-7B-Step-GRPO 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 "luzimu/WebGenAgent-LM-7B-Step-GRPO" \
    --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": "luzimu/WebGenAgent-LM-7B-Step-GRPO",
		"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 "luzimu/WebGenAgent-LM-7B-Step-GRPO" \
        --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": "luzimu/WebGenAgent-LM-7B-Step-GRPO",
		"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 luzimu/WebGenAgent-LM-7B-Step-GRPO with Docker Model Runner:
```
docker model run hf.co/luzimu/WebGenAgent-LM-7B-Step-GRPO
```

Improve model card: Add pipeline tag, library, code link, and usage

by nielsr HF Staff - opened Sep 30, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+25

-3

Files changed (1) hide show

README.md +25 -3

README.md CHANGED Viewed

@@ -1,16 +1,20 @@
 ---
-license: mit
 datasets:
 - luzimu/webgen-agent_train_step-grpo
 - luzimu/webgen-agent_train_sft
-base_model:
-- Qwen/Qwen2.5-Coder-7B-Instruct
 ---
 # WebGen-Agent
 WebGen-Agent is an advanced website generation agent designed to autonomously create websites from natural language instructions. It was introduced in the paper [WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning](https://arxiv.org/pdf/2509.22644v1).
 ## Project Overview
 WebGen-Agent combines state-of-the-art language models with specialized training techniques to create a powerful website generation tool. The agent can understand natural language instructions specifying appearance and functional requirements, iteratively generate website codebases, and refine them using visual and functional feedback.
@@ -55,6 +59,24 @@ These dual rewards provide dense, reliable process supervision that significantl
 ![Step-GRPO with Screenshot and GUI-agent Feedback](fig/step-grpo.png)
 ## Citation
 If you find our project useful, please cite:

 ---
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
 datasets:
 - luzimu/webgen-agent_train_step-grpo
 - luzimu/webgen-agent_train_sft
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 # WebGen-Agent
 WebGen-Agent is an advanced website generation agent designed to autonomously create websites from natural language instructions. It was introduced in the paper [WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning](https://arxiv.org/pdf/2509.22644v1).
+Code: https://github.com/mnluzimu/WebGen-Agent
 ## Project Overview
 WebGen-Agent combines state-of-the-art language models with specialized training techniques to create a powerful website generation tool. The agent can understand natural language instructions specifying appearance and functional requirements, iteratively generate website codebases, and refine them using visual and functional feedback.
 ![Step-GRPO with Screenshot and GUI-agent Feedback](fig/step-grpo.png)
+## Sample Usage
+Before running inference, you should rename `.env.template` to `.env` and set the base urls and api keys for the agent-engine LLM and feedback VLM. They can be obtained from any openai-compatible providers such as [openrouter](https://openrouter.ai/), [modelscope](https://www.modelscope.cn/my/overview), [bailian](https://bailian.console.aliyun.com/#/home), and [llmprovider](https://llmprovider.ai/).
+You can also deploy open-source VLMs and LLMs by running `src/scripts/deploy_qwenvl_32b.sh` and `src/scripts/deploy.sh`. Scripts for single inference and batch inference can be found at `src/scripts/infer_single.sh` and `src/scripts/infer_batch.sh`.
+```bash
+python src/infer_single.py \
+    --model deepseek-chat \
+    --vlm_model Qwen/Qwen2.5-VL-32B-Instruct \
+    --instruction "Please implement a wheel of fortune website." \
+    --workspace-dir workspaces_root/test \
+    --log-dir service_logs/test \
+    --max-iter 20 \
+    --overwrite \
+    --error-limit 5
+```
 ## Citation
 If you find our project useful, please cite: