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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 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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Project page: https://webgen-bench.github.io/ |
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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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## Sample Usage |
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You can use this model with the `transformers` library for text generation tasks, specifically for code generation based on instructions. |
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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" |
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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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messages = [ |
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{"role": "user", "content": "Write HTML, CSS, and JavaScript for a simple to-do list web application. The list should allow users to add and remove items."}, |
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] |
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chat_input = 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([chat_input], 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, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.95 |
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) |
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# Decode only the newly generated tokens |
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output_text = tokenizer.decode(generated_ids[0][model_inputs.input_ids.shape[1]:], skip_special_tokens=False) |
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print(output_text) |
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``` |
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## Performance on WebGen-Bench |
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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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``` |
