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
```

openvla-7b-finetuned-libero-spatial / model-00003-of-00004.safetensors

moojink

Add LIBERO-Spatial checkpoint

28319c2 almost 2 years ago

Download

History Blame Contribute Delete

4.95 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 41a8abcebb22aa498430f93c9601c06f5962cd36674e3ef91b32d6552669cedd
Size of remote file:: 4.95 GB
SHA256:: d696e042cf49abd0b5846e73cbd4931d75a5467ae44857bf8e54a90ed6588308

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.