Instructions to use gudo7208/CAD-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gudo7208/CAD-Coder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="gudo7208/CAD-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gudo7208/CAD-Coder")
model = AutoModelForCausalLM.from_pretrained("gudo7208/CAD-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gudo7208/CAD-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gudo7208/CAD-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gudo7208/CAD-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gudo7208/CAD-Coder

SGLang

How to use gudo7208/CAD-Coder with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "gudo7208/CAD-Coder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gudo7208/CAD-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "gudo7208/CAD-Coder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gudo7208/CAD-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gudo7208/CAD-Coder with Docker Model Runner:
```
docker model run hf.co/gudo7208/CAD-Coder
```

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:

Supervised Fine-Tuning (SFT): Learning CadQuery syntax and text-to-code mapping
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.

Installation

pip install transformers
pip install "numpy<2.0" cadquery==2.3.1  # Optional: for code execution

Quick Start

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

@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}
}