初始化项目，由ModelHub XC社区提供模型
Model: luzimu/WebGen-LM-14B Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- Qwen/Qwen2.5-Coder-7B-Instruct
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datasets:
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- luzimu/WebGen-Bench
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language:
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- en
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library_name: transformers
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license: mit
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metrics:
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- accuracy
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pipeline_tag: text-generation
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tags:
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- code-generation
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---
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# WebGen-LM
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WebGen-LM is a code language model specifically trained for generating interactive and functional websites from scratch. It is trained using the Bolt.diy trajectories generated from a subset of the training set of WebGen-Bench (🤗 [luzimu/WebGen-Bench](https://huggingface.co/datasets/luzimu/WebGen-Bench)). It has been introduced in the paper [WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch](https://arxiv.org/abs/2505.03733).
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The training data and code can be found at [WebGen-Bench (Github)](https://github.com/mnluzimu/WebGen-Bench).
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The WebGen-LM family of models are as follows:
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|Models | HF Links |
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|---|---|
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|WebGen-LM-7B | 🤗 [luzimu/WebGen-LM-7B](https://huggingface.co/luzimu/WebGen-LM-7B) |
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|WebGen-LM-14B | 🤗 [luzimu/WebGen-LM-14B](https://huggingface.co/luzimu/WebGen-LM-14B) |
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|WebGen-LM-32B | 🤗 [luzimu/WebGen-LM-32B](https://huggingface.co/luzimu/WebGen-LM-32B) |
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## Performance on WebGen-Bench
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## Usage
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You can use `WebGen-LM` with the `transformers` library to generate website code.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "luzimu/WebGen-LM-32B" # You can also use WebGen-LM-7B or WebGen-LM-14B
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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# Example for website generation
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prompt = """Generate the complete HTML, CSS, and JavaScript code for a responsive website.
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The website should be a simple landing page for a coffee shop.
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It needs:
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1. A navigation bar at the top with "Home", "Menu", "About Us", and "Contact" links.
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2. A hero section with a background image, a title "Brewing Perfection", and a call-to-action button "View Our Menu".
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3. A menu section displaying at least 3 coffee items with their names and prices.
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4. An "About Us" section with a brief description of the coffee shop.
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5. A "Contact" section with an address, phone number, and a simple contact form (Name, Email, Message, Submit button).
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6. Basic responsive design for mobile views.
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"""
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=2048, # Adjust as needed for full website code
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.05,
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)
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# Decode the generated output, skipping special tokens
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response = tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True)[0]
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# The response will contain the full conversation history including the input prompt.
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# To get only the newly generated text, you might need to slice it or use the appropriate
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# tokenizer behavior based on how apply_chat_template adds prompt.
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# For simplicity, if the model just appends to the prompt, direct decode might suffice.
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# A more robust approach might be:
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# generated_text_only = tokenizer.decode(generated_ids[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True)
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print(response)
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# You might need to parse the output to separate HTML, CSS, and JS if the model outputs a combined file.
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# For example, look for specific markers like <html>, <style>, <script>
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```
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## Citation
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If you find our project useful, please cite:
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```
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@misc{lu2025webgenbenchevaluatingllmsgenerating,
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title={WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch},
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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},
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year={2025},
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eprint={2505.03733},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2505.03733},
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}
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@misc{lu2025webgenagentenhancinginteractivewebsite,
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title={WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning},
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author={Zimu Lu and Houxing Ren and Yunqiao Yang and Ke Wang and Zhuofan Zong and Junting Pan and Mingjie Zhan and Hongsheng Li},
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year={2025},
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eprint={2509.22644},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2509.22644},
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}
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```
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