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

Libraries

How to use luzimu/WebGen-LM-7B with Transformers:

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

pipe = pipeline("text-generation", model="luzimu/WebGen-LM-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luzimu/WebGen-LM-7B")
model = AutoModelForCausalLM.from_pretrained("luzimu/WebGen-LM-7B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use luzimu/WebGen-LM-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luzimu/WebGen-LM-7B"
# 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/WebGen-LM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luzimu/WebGen-LM-7B

SGLang

How to use luzimu/WebGen-LM-7B 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/WebGen-LM-7B" \
    --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/WebGen-LM-7B",
		"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 "luzimu/WebGen-LM-7B" \
        --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/WebGen-LM-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use luzimu/WebGen-LM-7B with Docker Model Runner:
```
docker model run hf.co/luzimu/WebGen-LM-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

WebGen-LM

WebGen-LM is trained using the Bolt.diy trajectories generated from a subset of the training set of WebGen-Bench (🤗 luzimu/WebGen-Bench). It has been introduced in the paper WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch.

The training data and code can be found at WebGen-Bench (Github).

The WebGen-LM family of models are as follows:

Models	HF Links
WebGen-LM-7B	🤗 luzimu/WebGen-LM-7B
WebGen-LM-14B	🤗 luzimu/WebGen-LM-14B
WebGen-LM-32B	🤗 luzimu/WebGen-LM-32B

Performance on WebGen-Bench

Sample Usage

You can use this model with the Hugging Face transformers library.

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "luzimu/WebGen-LM-7B" # This model card refers to WebGen-LM-7B

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

# Example for website generation
user_prompt = "Generate a simple HTML page with a heading 'Hello, World!' and a paragraph of lorem ipsum text."
messages = [
    {"role": "user", "content": user_prompt}
]

# Apply chat template for instruction-following format
text_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Generate output
model_inputs = tokenizer(text_input, return_tensors="pt").to(model.device)
generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=500, do_sample=True, temperature=0.01, top_k=50, top_p=0.95)

# Decode and print the generated code
generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(generated_text)

# Example using Hugging Face pipeline for simpler inference
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
result = generator(user_prompt, max_new_tokens=500, do_sample=True, temperature=0.01, top_k=50, top_p=0.95)
print(result[0]['generated_text'])

Citation

If you find our project useful, please cite:

@misc{lu2025webgenbenchevaluatingllmsgenerating,
      title={WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch}, 
      author={Zimu Lu and Yunqiao Yang and Houxing Ren and Haotian Hou and Han Xiao and Ke Wang and Weikang Shi and Aojun Zhou and Mingjie Zhan and Hongsheng Li},
      year={2025},
      eprint={2505.03733},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.03733}, 
}

@misc{lu2025webgenagentenhancinginteractivewebsite,
      title={WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning}, 
      author={Zimu Lu and Houxing Ren and Yunqiao Yang and Ke Wang and Zhuofan Zong and Junting Pan and Mingjie Zhan and Hongsheng Li},
      year={2025},
      eprint={2509.22644},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.22644}, 
}