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jhlee11
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Model Details
Model Provider
jhlee11
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Model | Revision | Training stage |
|---|---|---|
| FutureCAD-7B | main | Reinforcement learning with GSPO |
| FutureCAD-7B-SFT | sft | Supervised fine-tuning |
Use revision="sft" to load the SFT model. Omitting revision loads main.
Install the futurecad environment from the code repository.
Run from that repository's root:
bash
python scripts/download_models.pybash scripts/serve_grounding.shIn another terminal using the same environment:
bash
export VLLM_USE_FLASHINFER_SAMPLER=0python -m futurecad.text_to_cad \ --text "Create a cylinder with radius 10 mm and height 20 mm." \ --model weights/futurecad-llm \ --output-dir outputs/cylinderThe command saves the generated Python program and exports STEP/STL when CAD execution succeeds. The repository supplies the prompt template and CadQuery extensions used by the model.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "jhlee11/FutureCAD-7B"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")For prompt formatting, generation, execution and evaluation, use the code repository.
bibtex
@misc{li2026highfidelitycadgenerationllmdriven, title={Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding}, author={Jiahao Li and Qingwang Zhang and Qiuyu Chen and Guozhan Qiu and Yunzhong Lou and Xiangdong Zhou}, year={2026}, eprint={2603.11831}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2603.11831},}python scripts/download_models.pybash scripts/serve_grounding.shexport VLLM_USE_FLASHINFER_SAMPLER=0python -m futurecad.text_to_cad \ --text "Create a cylinder with radius 10 mm and height 20 mm." \ --model weights/futurecad-llm \ --output-dir outputs/cylinderfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "jhlee11/FutureCAD-7B"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")@misc{li2026highfidelitycadgenerationllmdriven, title={Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Grounding}, author={Jiahao Li and Qingwang Zhang and Qiuyu Chen and Guozhan Qiu and Yunzhong Lou and Xiangdong Zhou}, year={2026}, eprint={2603.11831}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2603.11831},}