Instructions to use IPEC-COMMUNITY/spatialvla-4b-224-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IPEC-COMMUNITY/spatialvla-4b-224-pt with Transformers:

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

pipe = pipeline("image-text-to-text", model="IPEC-COMMUNITY/spatialvla-4b-224-pt", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("IPEC-COMMUNITY/spatialvla-4b-224-pt", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IPEC-COMMUNITY/spatialvla-4b-224-pt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IPEC-COMMUNITY/spatialvla-4b-224-pt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IPEC-COMMUNITY/spatialvla-4b-224-pt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IPEC-COMMUNITY/spatialvla-4b-224-pt

SGLang

How to use IPEC-COMMUNITY/spatialvla-4b-224-pt 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 "IPEC-COMMUNITY/spatialvla-4b-224-pt" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IPEC-COMMUNITY/spatialvla-4b-224-pt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "IPEC-COMMUNITY/spatialvla-4b-224-pt" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IPEC-COMMUNITY/spatialvla-4b-224-pt",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IPEC-COMMUNITY/spatialvla-4b-224-pt with Docker Model Runner:
```
docker model run hf.co/IPEC-COMMUNITY/spatialvla-4b-224-pt
```

Tavish9 commited on Jan 30, 2025

Commit

cf960b5

verified ·

1 Parent(s): 01f5ea2

Update README.md

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README.md +15 -15

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@@ -1,23 +1,23 @@
----
-license: mit
-base_model:
-- google/paligemma2-3b-pt-224
-tags:
-- VLA
-- Foundation Vision-language-action Model
-- Generalist Robot Policy
-- robotics
-language:
-- en
-pipeline_tag: image-text-to-text
-library_name: transformers
----
 # SpatialVLA
 SpatialVLA is a spatial-enhanced vision-language-action model trained on 1.1 Million real robot episodes. The code is purely huggingFace-based and concise, with efficient performance.
-All SpatialVLA checkpoints, as well as our [training codebase](https://github.com/openvla/openvla) are released under an MIT License.
 For full details, please read [our paper](https://arxiv.org/abs/2501.15830) and see [our project page](https://spatialvla.github.io/).

+---
+license: mit
+base_model:
+- google/paligemma2-3b-pt-224
+tags:
+- VLA
+- Foundation Vision-language-action Model
+- Generalist Robot Policy
+- robotics
+language:
+- en
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
 # SpatialVLA
 SpatialVLA is a spatial-enhanced vision-language-action model trained on 1.1 Million real robot episodes. The code is purely huggingFace-based and concise, with efficient performance.
+All SpatialVLA checkpoints, as well as our [training codebase](https://github.com/SpatialVLA/SpatialVLA) are released under an MIT License.
 For full details, please read [our paper](https://arxiv.org/abs/2501.15830) and see [our project page](https://spatialvla.github.io/).