Instructions to use ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT with Transformers:

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

pipe = pipeline("text-generation", model="ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT

SGLang

How to use ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT 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 "ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT" \
    --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": "ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT",
		"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 "ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT" \
        --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": "ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT with Docker Model Runner:
```
docker model run hf.co/ysmao/SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT
```

SpatialLM1.1-Qwen-0.5B-ARKitScenes-SFT

This is a finetuned version of SpatialLM1.1-Qwen-0.5B on ARKitScenes, using a new set of object categories and enable random, gravity-aligned scene orientations. During inference, only the z-axis needs to be kept as the up axis; the x and y axes do not need to be aligned.

Results

Here are some example results comparing ground truth (GT) oriented object bounding boxes with predictions (Pred) from the finetuned SpatialLM1.1-Qwen-0.5B model on the ARKitScenes dataset.