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--- |
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license: apache-2.0 |
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base_model: Qwen/Qwen2.5-7B-Instruct |
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tags: |
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- text-to-cad |
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- code-generation |
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- cadquery |
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- 3d-modeling |
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- reinforcement-learning |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# CAD-Coder |
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**CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward** |
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**Accepted at NeurIPS 2025 (Poster)** |
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This is the reinforcement learning (GRPO) fine-tuned model for generating CadQuery code from natural language descriptions. |
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## Model Description |
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CAD-Coder reformulates text-to-CAD as the generation of CadQuery scripts—a Python-based, parametric CAD language. The model is trained with a two-stage pipeline: |
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1. **Supervised Fine-Tuning (SFT)**: Learning CadQuery syntax and text-to-code mapping |
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2. **Reinforcement Learning (GRPO)**: Optimizing geometric accuracy with CAD-specific rewards (Chamfer Distance + Format Reward) |
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### Key Features |
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- Generates executable CadQuery Python code from natural language |
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- Chain-of-Thought (CoT) reasoning for complex CAD structures |
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- Geometric reward optimization for accurate 3D model generation |
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- Supports diverse CAD operations beyond simple sketch-extrusion |
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## Usage |
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For complete inference scripts, please visit our [GitHub repository](https://github.com/gudo7208/CAD-Coder). |
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### Installation |
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```bash |
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pip install transformers |
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pip install "numpy<2.0" cadquery==2.3.1 # Optional: for code execution |
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``` |
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### Quick Start |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "gudo7208/CAD-Coder" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto") |
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prompt = "Create a cylinder with radius 10mm and height 20mm, with a central hole of radius 5mm." |
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text = tokenizer.apply_chat_template( |
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[{"role": "user", "content": prompt}], |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=2048) |
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)) |
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``` |
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## Performance |
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| Method | Mean CD | Median CD | IR% | |
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|--------|---------|-----------|-----| |
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| Text2CAD | 29.29 | 0.37 | 3.75 | |
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| **CAD-Coder (Ours)** | **6.54** | **0.17** | **1.45** | |
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*CD metrics are ×10³. Lower is better.* |
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## Training Details |
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- **Base Model**: Qwen2.5-7B-Instruct |
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- **Training Data**: 110K text-CadQuery-3D model triplets + 1.5K CoT samples |
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- **Hardware**: 8× NVIDIA A800 80GB GPUs |
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- **Framework**: Hugging Face Transformers, DeepSpeed, Verl (GRPO) |
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## Citation |
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```bibtex |
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@article{guan2025cadcoder, |
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title={CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward}, |
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author={Guan, Yandong and Wang, Xilin and Xing, Ximing and Zhang, Jing and Xu, Dong and Yu, Qian}, |
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journal={arXiv preprint arXiv:2505.19713}, |
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year={2025} |
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} |
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``` |
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## License |
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This model is released under the Apache 2.0 License, following the base model (Qwen2.5-7B-Instruct) license terms. |
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## Acknowledgements |
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- Base model: [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
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- Training data derived from [Text2CAD](https://github.com/sadilkhan/Text2CAD) dataset |
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