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MauroPello
/
llm-routing-attack-paraphrasers

Text Generation
Transformers
Safetensors
paraphrasing
t5
reinforcement-learning
router-evasion
adversarial-robustness
llm-routing
Model card Files Files and versions
xet
Community
1

Instructions to use MauroPello/llm-routing-attack-paraphrasers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use MauroPello/llm-routing-attack-paraphrasers with Transformers:

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

    How to use MauroPello/llm-routing-attack-paraphrasers with vLLM:

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

    How to use MauroPello/llm-routing-attack-paraphrasers 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 "MauroPello/llm-routing-attack-paraphrasers" \
        --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": "MauroPello/llm-routing-attack-paraphrasers",
    		"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 "MauroPello/llm-routing-attack-paraphrasers" \
            --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": "MauroPello/llm-routing-attack-paraphrasers",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use MauroPello/llm-routing-attack-paraphrasers with Docker Model Runner:

    docker model run hf.co/MauroPello/llm-routing-attack-paraphrasers
llm-routing-attack-paraphrasers
6.74 GB
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  • 2 contributors
History: 39 commits
MauroPello's picture
MauroPello
matcav's picture
matcav
Delete checkpoints/causal_only_aggressive. (#1)
b7c73c0 about 2 months ago
  • checkpoints
    Delete checkpoints/causal_only_aggressive. (#1) about 2 months ago
  • .gitattributes
    1.52 kB
    initial commit about 2 months ago
  • README.md
    7.46 kB
    Update README.md about 2 months ago