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adityabhaskara
/
groot_n15_bimanul_cleanuo

Text Generation
PEFT
Safetensors
Transformers
lora
Model card Files Files and versions
xet
Community

Instructions to use adityabhaskara/groot_n15_bimanul_cleanuo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use adityabhaskara/groot_n15_bimanul_cleanuo with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("/home/jovyan/.cache/huggingface/hub/models--nvidia--GR00T-N1.5-3B/snapshots/869830fc749c35f34771aa5209f923ac57e4564e")
    model = PeftModel.from_pretrained(base_model, "adityabhaskara/groot_n15_bimanul_cleanuo")
  • Transformers

    How to use adityabhaskara/groot_n15_bimanul_cleanuo with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="adityabhaskara/groot_n15_bimanul_cleanuo")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("adityabhaskara/groot_n15_bimanul_cleanuo", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use adityabhaskara/groot_n15_bimanul_cleanuo with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "adityabhaskara/groot_n15_bimanul_cleanuo"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "adityabhaskara/groot_n15_bimanul_cleanuo",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/adityabhaskara/groot_n15_bimanul_cleanuo
  • SGLang

    How to use adityabhaskara/groot_n15_bimanul_cleanuo 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 "adityabhaskara/groot_n15_bimanul_cleanuo" \
        --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": "adityabhaskara/groot_n15_bimanul_cleanuo",
    		"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 "adityabhaskara/groot_n15_bimanul_cleanuo" \
            --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": "adityabhaskara/groot_n15_bimanul_cleanuo",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use adityabhaskara/groot_n15_bimanul_cleanuo with Docker Model Runner:

    docker model run hf.co/adityabhaskara/groot_n15_bimanul_cleanuo
groot_n15_bimanul_cleanuo
158 MB
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  • 1 contributor
History: 2 commits
adityabhaskara's picture
adityabhaskara
Upload folder using huggingface_hub
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  • experiment_cfg
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  • .gitattributes
    1.52 kB
    initial commit 6 months ago
  • README.md
    5.29 kB
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  • adapter_config.json
    958 Bytes
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  • adapter_model.safetensors
    52.4 MB
    xet
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  • optimizer.pt
    105 MB
    xet
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  • rng_state.pth

    Detected Pickle imports (7)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "_codecs.encode",
    • "numpy.dtype",
    • "numpy.core.multiarray._reconstruct",
    • "torch.ByteStorage",
    • "numpy.ndarray"

    How to fix it?

    14.2 kB
    xet
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  • scheduler.pt

    Pickle imports

    • No problematic imports detected

    What is a pickle import?

    1.06 kB
    xet
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  • trainer_state.json
    263 kB
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