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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gudo7208/CAD-Coder")
model = AutoModelForCausalLM.from_pretrained("gudo7208/CAD-Coder", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

gudo7208 commited on Jan 6

Commit

a2e5cc8

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@@ -19,8 +19,6 @@ library_name: transformers
 [![arXiv](https://img.shields.io/badge/arXiv-2505.19713-b31b1b.svg)](https://arxiv.org/abs/2505.19713)
-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:
@@ -33,7 +31,6 @@ CAD-Coder reformulates text-to-CAD as the generation of CadQuery scripts—a Pyt
 - 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

 [![arXiv](https://img.shields.io/badge/arXiv-2505.19713-b31b1b.svg)](https://arxiv.org/abs/2505.19713)
 ## 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:
 - 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
 ## Usage