Instructions to use breezexian/UniPhysGen-1.7B-Init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use breezexian/UniPhysGen-1.7B-Init with Transformers:

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

pipe = pipeline("text-generation", model="breezexian/UniPhysGen-1.7B-Init")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("breezexian/UniPhysGen-1.7B-Init", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use breezexian/UniPhysGen-1.7B-Init with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "breezexian/UniPhysGen-1.7B-Init"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "breezexian/UniPhysGen-1.7B-Init",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/breezexian/UniPhysGen-1.7B-Init

SGLang

How to use breezexian/UniPhysGen-1.7B-Init 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 "breezexian/UniPhysGen-1.7B-Init" \
    --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": "breezexian/UniPhysGen-1.7B-Init",
		"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 "breezexian/UniPhysGen-1.7B-Init" \
        --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": "breezexian/UniPhysGen-1.7B-Init",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use breezexian/UniPhysGen-1.7B-Init with Docker Model Runner:
```
docker model run hf.co/breezexian/UniPhysGen-1.7B-Init
```

UniPhysGen-1.7B-Init

UniPhysGen-1.7B-Init is the training initialization checkpoint for UniPhysGen, a unified 3D model for grounding articulation semantics and intrinsic physical properties. It combines the Qwen3-1.7B language backbone with the Sonata point-cloud encoder and the UniPhysGen multimodal projection layers.

This checkpoint contains no UniPhys task-specific grounding training. It is intended as the starting point for physical semantic alignment and subsequent task-specific fine-tuning. It is not a recommended inference checkpoint.

Model details

Item	Value
Model architecture	Qwen3-1.7B + Sonata + UniPhysGen multimodal projector
Intended stage	Training initialization
Language backbone	`Qwen/Qwen3-1.7B`
Point-cloud backbone	`facebook/sonata`
Input modality	3D point clouds, with task-dependent text prompts
Tested Transformers version	`4.51.0`
Source code	breezexian/UniPhysGen
Paper	arXiv:2607.13586

Intended use

Use this checkpoint to reproduce the physical semantic alignment stage or to train UniPhysGen on compatible custom data. For released predictions, use one of the task-specific checkpoints instead:

Installation

The model has been tested on Linux with Python 3.11, PyTorch 2.4.1, CUDA 12.4, and transformers==4.51.0.

Use transformers==4.51.0. This is the tested version and is pinned by the UniPhysGen project metadata.

git clone https://github.com/breezexian/UniPhysGen.git
cd UniPhysGen

conda create -n uniphysgen python=3.11 -y
conda activate uniphysgen
conda install -y -c nvidia/label/cuda-12.4.0 cuda-toolkit

python -m pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
python -m pip install -e ".[train]"
bash scripts/install_cuda_extensions.sh

For inference only, replace python -m pip install -e ".[train]" with python -m pip install -e ..

The model uses a custom Transformers architecture. Install the UniPhysGen package before loading the checkpoint so that the custom configuration and model classes are registered.

Download and training

hf download breezexian/UniPhysGen-1.7B-Init \
  --local-dir checkpoints/UniPhysGen-1.7B-Init

Set the initialization path in the semantic-alignment release config:

model_name_or_path: checkpoints/UniPhysGen-1.7B-Init

Then launch the training entry point documented in the main project README. The corresponding training dataset is spatialverse/UniPhys-40K.

Limitations

This checkpoint is an initialization artifact and has not been fine-tuned for any of the four released grounding tasks.
It should not be used as a drop-in text-generation model or with a generic Hugging Face text-generation pipeline.
Inputs must follow the UniPhysGen point-cloud and prompt conventions.
Model outputs may be inaccurate and require task-specific fine-tuning and validation before use in simulation or robotics.

License

The model weights are released under the Creative Commons Attribution-NonCommercial 4.0 International license. Commercial use is not permitted under this license. The UniPhysGen source code is licensed separately under Apache-2.0.

This checkpoint is derived from Qwen3-1.7B, released under Apache-2.0, and the official Sonata pretrained weights, released under CC BY-NC 4.0. See the included license for attribution details.

Citation

@article{li2026uniphysgen,
  title   = {UniPhysGen: Unified Physical Grounding for Simulation-Ready 3D Assets},
  author  = {Li, Xian and Wei, Rong and Yang, Lujie and Huang, Haolin and Fang, Junyuan and Tang, Siliang and Xiao, Jun and Tang, Rui and Li, Juncheng},
  journal = {arXiv preprint arXiv:2607.13586},
  year    = {2026}
}