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Stanford-ILIAD
/
minivla-vq-bridge-prismatic

Image-Text-to-Text
Transformers
English
robotics
vla
multimodal
pretraining
Model card Files Files and versions
xet
Community
1

Instructions to use Stanford-ILIAD/minivla-vq-bridge-prismatic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Stanford-ILIAD/minivla-vq-bridge-prismatic with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="Stanford-ILIAD/minivla-vq-bridge-prismatic")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Stanford-ILIAD/minivla-vq-bridge-prismatic", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Stanford-ILIAD/minivla-vq-bridge-prismatic with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Stanford-ILIAD/minivla-vq-bridge-prismatic"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Stanford-ILIAD/minivla-vq-bridge-prismatic",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Stanford-ILIAD/minivla-vq-bridge-prismatic
  • SGLang

    How to use Stanford-ILIAD/minivla-vq-bridge-prismatic 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 "Stanford-ILIAD/minivla-vq-bridge-prismatic" \
        --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": "Stanford-ILIAD/minivla-vq-bridge-prismatic",
    		"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 "Stanford-ILIAD/minivla-vq-bridge-prismatic" \
            --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": "Stanford-ILIAD/minivla-vq-bridge-prismatic",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Stanford-ILIAD/minivla-vq-bridge-prismatic with Docker Model Runner:

    docker model run hf.co/Stanford-ILIAD/minivla-vq-bridge-prismatic
minivla-vq-bridge-prismatic
5.63 GB
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  • 1 contributor
History: 3 commits
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belkhale
Create README.md
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  • prism-qwen25-dinosiglip-224px+0_5b+mx-bridge+n1+b16+x7--bridge_vq_extra_tokenizer_evenlowerlr.jsonl
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