Instructions to use IFMedTechdemo/qwen3-vl-cadquery with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IFMedTechdemo/qwen3-vl-cadquery with Transformers:

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

pipe = pipeline("image-text-to-text", model="IFMedTechdemo/qwen3-vl-cadquery")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("IFMedTechdemo/qwen3-vl-cadquery")
model = AutoModelForImageTextToText.from_pretrained("IFMedTechdemo/qwen3-vl-cadquery")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use IFMedTechdemo/qwen3-vl-cadquery with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IFMedTechdemo/qwen3-vl-cadquery"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IFMedTechdemo/qwen3-vl-cadquery",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/IFMedTechdemo/qwen3-vl-cadquery

SGLang

How to use IFMedTechdemo/qwen3-vl-cadquery 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 "IFMedTechdemo/qwen3-vl-cadquery" \
    --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": "IFMedTechdemo/qwen3-vl-cadquery",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "IFMedTechdemo/qwen3-vl-cadquery" \
        --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": "IFMedTechdemo/qwen3-vl-cadquery",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Studio new

How to use IFMedTechdemo/qwen3-vl-cadquery with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for IFMedTechdemo/qwen3-vl-cadquery to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for IFMedTechdemo/qwen3-vl-cadquery to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for IFMedTechdemo/qwen3-vl-cadquery to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="IFMedTechdemo/qwen3-vl-cadquery",
    max_seq_length=2048,
)

Docker Model Runner
How to use IFMedTechdemo/qwen3-vl-cadquery with Docker Model Runner:
```
docker model run hf.co/IFMedTechdemo/qwen3-vl-cadquery
```

qwen3-vl-cadquery (fine-tuned)

This repository contains a fine-tuned Qwen3-VL model adapted for CadQuery-style CAD/code understanding and generation, trained on the ThomasTheMaker/cadquery dataset.

Model details

Developed by: IFMedTechdemo
Base model: unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit
License: Apache-2.0
Dataset: ThomasTheMaker/cadquery (~2000 examples)
Training: 30 epochs
Training stack: Unsloth + Hugging Face TRL

Intended use

This model is designed to help with CAD / parametric modeling workflows that resemble the CadQuery dataset distribution, including:

Generating CadQuery-style Python code from natural-language CAD instructions.
Explaining or modifying existing CadQuery snippets (e.g., “add 4 holes”, “increase wall thickness”, “fillet edges”, “change dimensions”).
Basic reasoning about geometric intent described in text (dimensions, features, operations).

How to use (example)

Typical usage is as an instruction-following model where you provide:

A natural-language task description, and optionally
A partial CadQuery snippet to modify

Example prompt:

“Create a parametric bracket with two mounting holes (M4), 3mm thickness, and a 20mm standoff. Output CadQuery code.”

Notes on training

Fine-tuned for 30 epochs on ~2000 items from ThomasTheMaker/cadquery.
Unsloth was used to speed up training and improve memory efficiency; TRL was used for the fine-tuning loop.

Deployment

This repo is tagged for text-generation-inference and transformers. You can run it with standard Transformers inference or deploy via TGI depending on your environment and quantization support.

Limitations

The model can produce code that is not directly executable (syntax/import issues, missing variables, or invalid CadQuery API usage).
Dimensions/constraints may be inconsistent; validate outputs before using them in production CAD pipelines.
Complex assemblies, tight tolerance constraints, and manufacturing-ready outputs may require human review and iterative refinement.

License

Apache-2.0 (see metadata above).

Downloads last month: 61

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for IFMedTechdemo/qwen3-vl-cadquery

Base model

Qwen/Qwen3-VL-8B-Instruct

Quantized

unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit

Finetuned

(143)

this model

IFMedTechdemo
/

qwen3-vl-cadquery