Instructions to use jhlee11/FutureCAD-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jhlee11/FutureCAD-7B with Transformers:

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

pipe = pipeline("text-generation", model="jhlee11/FutureCAD-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jhlee11/FutureCAD-7B")
model = AutoModelForCausalLM.from_pretrained("jhlee11/FutureCAD-7B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jhlee11/FutureCAD-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jhlee11/FutureCAD-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jhlee11/FutureCAD-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jhlee11/FutureCAD-7B

SGLang

How to use jhlee11/FutureCAD-7B 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 "jhlee11/FutureCAD-7B" \
    --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": "jhlee11/FutureCAD-7B",
		"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 "jhlee11/FutureCAD-7B" \
        --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": "jhlee11/FutureCAD-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jhlee11/FutureCAD-7B with Docker Model Runner:
```
docker model run hf.co/jhlee11/FutureCAD-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FutureCAD-7B

FutureCAD-7B generates CadQuery programs from text descriptions. It is based on Qwen2.5-7B-Instruct.

Paper · Code · Dataset · BRepGround

Text-to-CAD

Install the futurecad environment from the code repository. Run from that repository's root:

python scripts/download_models.py
bash scripts/serve_grounding.sh

In another terminal using the same environment:

export VLLM_USE_FLASHINFER_SAMPLER=0
python -m futurecad.text_to_cad \
  --text "Create a cylinder with radius 10 mm and height 20 mm." \
  --model weights/futurecad-llm \
  --output-dir outputs/cylinder

The 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.

Load with Transformers

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.

Citation

@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},
}