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AbstractPerspective
/
bling_hidden

Text Generation
Transformers
PyTorch
phi-msft
custom_code
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use AbstractPerspective/bling_hidden with Transformers:

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

    How to use AbstractPerspective/bling_hidden with vLLM:

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

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

    How to use AbstractPerspective/bling_hidden with Docker Model Runner:

    docker model run hf.co/AbstractPerspective/bling_hidden
bling_hidden
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  • 1 contributor
History: 2 commits
AbstractPerspective's picture
AbstractPerspective
Upload 11 files
0cbb29e verified about 2 years ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • added_tokens.json
    1.08 kB
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  • config.json
    755 Bytes
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  • configuration_phi.py
    2.03 kB
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  • generation_config.json
    69 Bytes
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  • merges.txt
    456 kB
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  • modeling_phi.py
    35 kB
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  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    5.56 GB
    xet
    Upload 11 files about 2 years ago
  • special_tokens_map.json
    99 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    7.34 kB
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  • vocab.json
    798 kB
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