Instructions to use adrlau/qwen2.5-3B-vl-openscad-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adrlau/qwen2.5-3B-vl-openscad-v1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="adrlau/qwen2.5-3B-vl-openscad-v1.0") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("adrlau/qwen2.5-3B-vl-openscad-v1.0") model = AutoModelForMultimodalLM.from_pretrained("adrlau/qwen2.5-3B-vl-openscad-v1.0") 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 Settings
- vLLM
How to use adrlau/qwen2.5-3B-vl-openscad-v1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "adrlau/qwen2.5-3B-vl-openscad-v1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adrlau/qwen2.5-3B-vl-openscad-v1.0", "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/adrlau/qwen2.5-3B-vl-openscad-v1.0
- SGLang
How to use adrlau/qwen2.5-3B-vl-openscad-v1.0 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 "adrlau/qwen2.5-3B-vl-openscad-v1.0" \ --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": "adrlau/qwen2.5-3B-vl-openscad-v1.0", "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 "adrlau/qwen2.5-3B-vl-openscad-v1.0" \ --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": "adrlau/qwen2.5-3B-vl-openscad-v1.0", "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
How to use adrlau/qwen2.5-3B-vl-openscad-v1.0 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 adrlau/qwen2.5-3B-vl-openscad-v1.0 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 adrlau/qwen2.5-3B-vl-openscad-v1.0 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for adrlau/qwen2.5-3B-vl-openscad-v1.0 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="adrlau/qwen2.5-3B-vl-openscad-v1.0", max_seq_length=2048, ) - Docker Model Runner
How to use adrlau/qwen2.5-3B-vl-openscad-v1.0 with Docker Model Runner:
docker model run hf.co/adrlau/qwen2.5-3B-vl-openscad-v1.0
Readme
Hi there,
Can you tell us a bit about what this model does? Does it generate openscad code given images and video?
TLDR: Dont use it. Current sota models are way stronger than this even in the niche.
It was a failed research attempt at SFT finetuning qwen to generating openscad code from, images and or text.
It mostly learned the openscad syntax, but actual 3d modeling was way off.
Will maybe try this again, with added reinforcement learning on qwen3vl, but scrapped for now.
Thanks for replying back, you are right the SOTA models are getting quite good at this, and don't need any SFT right now.