Image-Text-to-Text
Transformers
Safetensors
English
spatialvla
feature-extraction
VLA
Foundation Vision-language-action Model
Generalist Robot Policy
robotics
custom_code
Instructions to use IPEC-COMMUNITY/spatialvla-4b-224-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,279 Bytes
8cbc8de 365b6d8 8cbc8de 886c455 8cbc8de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | import os
import argparse
from pathlib import Path
import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor
parser = argparse.ArgumentParser("Huggingface AutoModel Tesing")
parser.add_argument("--model_name_or_path", default=".", help="pretrained model name or path.")
parser.add_argument("--num_images", type=int, default=1, help="num_images for testing.")
args = parser.parse_args()
if __name__ == "__main__":
model_name_or_path = Path(args.model_name_or_path)
processor = AutoProcessor.from_pretrained(args.model_name_or_path, trust_remote_code=True)
print(processor.statistics)
model = AutoModel.from_pretrained(args.model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16).eval().cuda()
image = Image.open("example.png").convert("RGB")
images = [image] * args.num_images
prompt = "What action should the robot take to pick the cup?"
inputs = processor(images=images, text=prompt, unnorm_key="bridge_orig/1.0.0", return_tensors="pt")
print(inputs)
generation_outputs = model.predict_action(inputs)
print(generation_outputs, processor.batch_decode(generation_outputs))
actions = processor.decode_actions(generation_outputs, unnorm_key="bridge_orig/1.0.0")
print(actions)
|