Instructions to use openvla/openvla-7b-finetuned-libero-spatial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openvla/openvla-7b-finetuned-libero-spatial with Transformers:

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

pipe = pipeline("image-text-to-text", model="openvla/openvla-7b-finetuned-libero-spatial", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForVision2Seq
model = AutoModelForVision2Seq.from_pretrained("openvla/openvla-7b-finetuned-libero-spatial", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openvla/openvla-7b-finetuned-libero-spatial with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openvla/openvla-7b-finetuned-libero-spatial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openvla/openvla-7b-finetuned-libero-spatial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openvla/openvla-7b-finetuned-libero-spatial

SGLang

How to use openvla/openvla-7b-finetuned-libero-spatial 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 "openvla/openvla-7b-finetuned-libero-spatial" \
    --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": "openvla/openvla-7b-finetuned-libero-spatial",
		"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 "openvla/openvla-7b-finetuned-libero-spatial" \
        --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": "openvla/openvla-7b-finetuned-libero-spatial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openvla/openvla-7b-finetuned-libero-spatial with Docker Model Runner:
```
docker model run hf.co/openvla/openvla-7b-finetuned-libero-spatial
```

Update model card for GoBA: add paper, links, improve usage, update pipeline tag

by nielsr HF Staff - opened Oct 13, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+24

-18

nielsr

Oct 13, 2025

This PR updates the model card for openvla/openvla-7b-finetuned-libero-spatial to accurately reflect its association with the Goal-oriented Backdoor Attack against Vision-Language-Action Models via Physical Objects paper.

Key changes include:

Updating the primary paper link, project page, and GitHub repository.
Changing the pipeline_tag from image-text-to-text to robotics for better discoverability within the robotics community.
Updating the usage instructions to point to the dedicated GoBA GitHub repository for comprehensive guidance on environment setup, data collection, fine-tuning, and evaluation.
Updating the citation to the GoBA paper.

Please review and merge if these changes are appropriate.

Update model card for GoBA: add paper, links, improve usage, update pipeline tag01464b55

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