Instructions to use campedersen/cad0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use campedersen/cad0 with Transformers:

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

pipe = pipeline("text-generation", model="campedersen/cad0")
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("campedersen/cad0")
model = AutoModelForCausalLM.from_pretrained("campedersen/cad0", 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 campedersen/cad0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "campedersen/cad0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "campedersen/cad0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/campedersen/cad0

SGLang

How to use campedersen/cad0 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 "campedersen/cad0" \
    --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": "campedersen/cad0",
		"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 "campedersen/cad0" \
        --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": "campedersen/cad0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use campedersen/cad0 with Docker Model Runner:
```
docker model run hf.co/campedersen/cad0
```

cad0

cad0 is a fine-tuned language model for text-to-CAD generation. Given a natural language description of a mechanical part, it generates Compact IR — a token-efficient domain-specific language for parametric 3D geometry.

Demo

Try it: cad0-demo on HuggingFace Spaces

For browser/offline use: cad0-mini (0.5B, ONNX quantized)

What It Does

Input:  "50x30mm mounting plate with 4 corner holes"
Output: C 50 30 5
        Y 2.5 10
        T 1 5 5 0
        D 0 2
        ...

The Compact IR output can be parsed and evaluated by the vcad kernel to produce editable parametric geometry.

Supported Part Types

Family	Examples
Bracket	L-brackets, Z-brackets, mounting plates
Standoff	Cylindrical spacers, threaded standoffs
Enclosure	Boxes, vented enclosures, rounded shells
Gear	Spur gears, hubs
Flange	Bolt circles, blind flanges
Clip	Snap clips, spring clips

Usage

With Transformers + PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "campedersen/cad0")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")

prompt = "50x30mm mounting plate with 4 corner holes"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False,
add_generation_prompt=True)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1,
do_sample=True)
ir = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:],
skip_special_tokens=True)
print(ir)

API Endpoint

curl -X POST https://ecto--cad0-training-inference-infer.modal.run \
  -H "Content-Type: application/json" \
  -d '{"prompt": "L-bracket with mounting holes", "temperature": 0.1}'

Compact IR Reference

┌──────────────┬──────────────┬──────────────────────────────────┐
│      Op      │    Syntax    │           Description            │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Cube         │ C x y z      │ Box with dimensions x, y, z (mm) │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Cylinder     │ Y r h        │ Cylinder with radius r, height h │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Sphere       │ S r          │ Sphere with radius r             │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Cone         │ K r1 r2 h    │ Cone/frustum                     │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Translate    │ T n x y z    │ Move node n by offset            │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Rotate       │ R n rx ry rz │ Rotate node n (degrees)          │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Scale        │ X n sx sy sz │ Scale node n                     │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Union        │ U a b        │ Boolean union                    │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Difference   │ D a b        │ Boolean subtraction              │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Intersection │ I a b        │ Boolean intersection             │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Shell        │ SH n t       │ Hollow out with wall thickness t │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Fillet       │ F n r        │ Round edges with radius r        │
├──────────────┼──────────────┼──────────────────────────────────┤
│ Chamfer      │ CH n d       │ Bevel edges with distance d      │
└──────────────┴──────────────┴──────────────────────────────────┘

Nodes are 0-indexed; each line creates a new node.

Training

┌───────────────────┬───────────────────────────┐
│     Parameter     │           Value           │
├───────────────────┼───────────────────────────┤
│ Base model        │ Qwen2.5-Coder-7B-Instruct │
├───────────────────┼───────────────────────────┤
│ Method            │ QLoRA (4-bit NF4)         │
├───────────────────┼───────────────────────────┤
│ LoRA rank         │ 64                        │
├───────────────────┼───────────────────────────┤
│ LoRA alpha        │ 128                       │
├───────────────────┼───────────────────────────┤
│ Training examples │ 530,531                   │
├───────────────────┼───────────────────────────┤
│ Epochs            │ 1                         │
├───────────────────┼───────────────────────────┤
│ Batch size        │ 64 (effective)            │
├───────────────────┼───────────────────────────┤
│ Learning rate     │ 2e-4 (cosine)             │
├───────────────────┼───────────────────────────┤
│ Hardware          │ 1x NVIDIA H100 80GB       │
├───────────────────┼───────────────────────────┤
│ Training time     │ 9h 15m                    │
└───────────────────┴───────────────────────────┘

Evaluation

┌──────────────────────────┬───────────────────────────┐
│          Metric          │           Value           │
├──────────────────────────┼───────────────────────────┤
│ Final eval loss          │ 0.324                     │
├──────────────────────────┼───────────────────────────┤
│ In-distribution accuracy │ 75%                       │
├──────────────────────────┼───────────────────────────┤
│ Out-of-distribution      │ Limited (see Limitations) │
└──────────────────────────┴───────────────────────────┘

Limitations

Training bias: May add features (holes, fillets) to simple primitives — training data weighted toward complex manufactured parts
Dimension ambiguity: Occasionally confuses radius vs diameter for cylinders
Limited primitives: No native hexagon support; hex shapes require boolean workarounds
OOD generalization: Struggles with part types not in training distribution

https://github.com/ecto/vcad — Open-source parametric CAD with custom BRep kernel
https://vcad.io — Web app with cad0 integration
https://huggingface.co/campedersen/cad0-mini — 0.5B distilled model for browser inference

Citation

@misc{cad0, author = {Cam Pedersen}, title = {cad0: Text-to-CAD Language Model}, year = {2026}, publisher = {HuggingFace}, url = {https://huggingface.co/campedersen/cad0} }

License

MIT

Downloads last month: 27

Safetensors

Model size

8B params

Tensor type

F32

Model tree for campedersen/cad0

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Adapter

(821)

this model

campedersen
/

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