README.md

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license: apache-2.0
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![img1](./img/overview_paper.jpg)  
# GeoCAD-LLM: CAD Sequence Generation via Multimodal LLMs with Equivariant Geometric Features🛠️
- [Github](https://github.com/kshsh0405/GeoCADLLM_Inference)    
- [Paper](comming_soon)  

## GeoCAD-LLM_4B_pc🛠️
- Base_model: [Qwen3-4B-Instruct](Qwen/Qwen3-4B-Instruct-2507)
- Max sequence length: 8,192
- Epoch: 2
- Learning rate: 1e-4
- Batch size: 128
- This model specialized for pc-text-to-CAD. However, it also supports multi-modality.

## GeoCAD-LLM Contributions🔥
![img2](./img/model_structure.jpg)  

- **State-of-the-art Performace🏆** in [Text2CAD](https://huggingface.co/datasets/SadilKhan/Text2CAD) datasets. (as shown in below Tables)
- **Multimodal CAD Generation🌐**: Both text-to-CAD and pc-text-to-CAD.
- GeoCAD-LLM directly generate CAD vector sequence as **natural language**.
- **Novel Two Stage Training Pipeline🧭**: In stage1, training semantic geometry alignment. In stage2, training fine-grained geometry. Especially, we **direct levearge E(3)-equivariant features** for geomtry-consistent supervision, inherently ensuring geometric feature consistency regardless of input orientation.
- **Apply Point Cloud Dropout (PCD) technique🧶**: PCD mitigates over-reliance on geometric inputs and improves multimodal generalization. Also, it is a critical training technique for multimodal CAD generation.

## Performace (text-to-CAD & pc-text-to-CAD)🔥
![img3](./img/performance.jpg)  

## Qualitative Results
Please check our paper and supplementary materials.🤗

## Bibtex🤗
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