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