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Model: luzimu/WebGen-LM-14B
Source: Original Platform

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README.md

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+---
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+datasets:
+- luzimu/WebGen-Bench
+language:
+- en
+library_name: transformers
+license: mit
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- code-generation
+---
+
+# WebGen-LM
+
+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).
+
+The training data and code can be found at [WebGen-Bench (Github)](https://github.com/mnluzimu/WebGen-Bench).
+
+The WebGen-LM family of models are as follows:
+
+|Models | HF Links |
+|---|---|
+|WebGen-LM-7B | 🤗 [luzimu/WebGen-LM-7B](https://huggingface.co/luzimu/WebGen-LM-7B) |
+|WebGen-LM-14B | 🤗 [luzimu/WebGen-LM-14B](https://huggingface.co/luzimu/WebGen-LM-14B) |
+|WebGen-LM-32B | 🤗 [luzimu/WebGen-LM-32B](https://huggingface.co/luzimu/WebGen-LM-32B) |
+
+## Performance on WebGen-Bench
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64b0bfef2f2f9c345b87e673/ADt1JdvKw-IZ_xnS17adL.png)
+
+## Usage
+
+You can use `WebGen-LM` with the `transformers` library to generate website code.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "luzimu/WebGen-LM-32B" # You can also use WebGen-LM-7B or WebGen-LM-14B
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Example for website generation
+prompt = """Generate the complete HTML, CSS, and JavaScript code for a responsive website.
+The website should be a simple landing page for a coffee shop.
+It needs:
+1. A navigation bar at the top with "Home", "Menu", "About Us", and "Contact" links.
+2. A hero section with a background image, a title "Brewing Perfection", and a call-to-action button "View Our Menu".
+3. A menu section displaying at least 3 coffee items with their names and prices.
+4. An "About Us" section with a brief description of the coffee shop.
+5. A "Contact" section with an address, phone number, and a simple contact form (Name, Email, Message, Submit button).
+6. Basic responsive design for mobile views.
+"""
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=2048, # Adjust as needed for full website code
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    repetition_penalty=1.05,
+)
+
+# Decode the generated output, skipping special tokens
+response = tokenizer.batch_decode(generated_ids[0], skip_special_tokens=True)[0]
+# The response will contain the full conversation history including the input prompt.
+# To get only the newly generated text, you might need to slice it or use the appropriate
+# tokenizer behavior based on how apply_chat_template adds prompt.
+# For simplicity, if the model just appends to the prompt, direct decode might suffice.
+# A more robust approach might be:
+# generated_text_only = tokenizer.decode(generated_ids[0][len(model_inputs.input_ids[0]):], skip_special_tokens=True)
+print(response)
+
+# You might need to parse the output to separate HTML, CSS, and JS if the model outputs a combined file.
+# For example, look for specific markers like <html>, <style>, <script>
+```
+
+## 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}, 
+}
+```

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config.json

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+{
+  "_name_or_path": "/mnt/cache/sharemath/models/Qwen/Qwen2.5-Coder-14B-Instruct",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 48,
+  "model_type": "qwen2",
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+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "top_p": 0.8,
+  "transformers_version": "4.44.0"
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+}

special_tokens_map.json

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tokenizer_config.json

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