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latent-action-pretraining
/
LAPA-7B-openx

Image-Text-to-Text
Transformers
JAX
English
robotics
Model card Files Files and versions
xet
Community

Instructions to use latent-action-pretraining/LAPA-7B-openx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use latent-action-pretraining/LAPA-7B-openx with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="latent-action-pretraining/LAPA-7B-openx")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("latent-action-pretraining/LAPA-7B-openx", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use latent-action-pretraining/LAPA-7B-openx with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "latent-action-pretraining/LAPA-7B-openx"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "latent-action-pretraining/LAPA-7B-openx",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/latent-action-pretraining/LAPA-7B-openx
  • SGLang

    How to use latent-action-pretraining/LAPA-7B-openx 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 "latent-action-pretraining/LAPA-7B-openx" \
        --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": "latent-action-pretraining/LAPA-7B-openx",
    		"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 "latent-action-pretraining/LAPA-7B-openx" \
            --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": "latent-action-pretraining/LAPA-7B-openx",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use latent-action-pretraining/LAPA-7B-openx with Docker Model Runner:

    docker model run hf.co/latent-action-pretraining/LAPA-7B-openx
LAPA-7B-openx
61.3 GB
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  • 1 contributor
History: 10 commits
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latent-action-pretraining
Upload laq_openx.pt
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  • .gitattributes
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    Upload latent_action_pretraining_openx.jsonl with huggingface_hub almost 2 years ago
  • README.md
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  • laq_openx.pt

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage"

    What is a pickle import?

    1.38 GB
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  • latent_action_pretraining_openx.jsonl
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    Upload latent_action_pretraining_openx.jsonl with huggingface_hub almost 2 years ago
  • params
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  • tokenizer.model
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  • vqgan
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