Instructions to use openvla/openvla-7b-finetuned-libero-10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openvla/openvla-7b-finetuned-libero-10 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-10", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForVision2Seq
model = AutoModelForVision2Seq.from_pretrained("openvla/openvla-7b-finetuned-libero-10", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openvla/openvla-7b-finetuned-libero-10 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-10"
# 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-10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/openvla/openvla-7b-finetuned-libero-10

SGLang

How to use openvla/openvla-7b-finetuned-libero-10 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-10" \
    --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-10",
		"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-10" \
        --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-10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use openvla/openvla-7b-finetuned-libero-10 with Docker Model Runner:
```
docker model run hf.co/openvla/openvla-7b-finetuned-libero-10
```

Improve model card: Add robotics pipeline tag, paper, project page, and code links

by nielsr HF Staff - opened Oct 13, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+27

-14

nielsr

Oct 13, 2025

This PR improves the model card for this repository by:

Updating the pipeline_tag to robotics, making it easier for users to discover the model for robotics-related tasks.
Adding a link to the associated paper, "Goal-oriented Backdoor Attack against Vision-Language-Action Models via Physical Objects".
Including links to the project page (https://goba-attack.github.io/) and the GitHub repository for the GoBA attack (https://github.com/trustmlyoungscientist/GoBA_attack).
Adding relevant tags (backdoor-attack, goal-oriented-backdoor-attack).
Adding the BibTeX citation for the GoBA paper.

Please review and merge this PR if everything looks good.

Improve model card: Add robotics pipeline tag, paper, project page, and code links09954c27

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