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BreakFeelingAether
/
aetherargus

Text Generation
Transformers
Safetensors
GGUF
PEFT
Chinese
English
qlora
cybersecurity
security
intrusion-detection
structured-output
json
ollama
aetherguard
aetherargus
conversational
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use BreakFeelingAether/aetherargus with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="BreakFeelingAether/aetherargus")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("BreakFeelingAether/aetherargus", device_map="auto")
  • PEFT

    How to use BreakFeelingAether/aetherargus with PEFT:

    Task type is invalid.
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use BreakFeelingAether/aetherargus with llama.cpp:

    Install (macOS, Linux)
    curl -LsSf https://llama.app/install.sh | sh
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf BreakFeelingAether/aetherargus:F16
    # Run inference directly in the terminal:
    llama cli -hf BreakFeelingAether/aetherargus:F16
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf BreakFeelingAether/aetherargus:F16
    # Run inference directly in the terminal:
    llama cli -hf BreakFeelingAether/aetherargus:F16
    Use pre-built binary
    # Download pre-built binary from:
    # https://github.com/ggerganov/llama.cpp/releases
    # Start a local OpenAI-compatible server with a web UI:
    ./llama-server -hf BreakFeelingAether/aetherargus:F16
    # Run inference directly in the terminal:
    ./llama-cli -hf BreakFeelingAether/aetherargus:F16
    Build from source code
    git clone https://github.com/ggerganov/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-server llama-cli
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama-server -hf BreakFeelingAether/aetherargus:F16
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf BreakFeelingAether/aetherargus:F16
    Use Docker
    docker model run hf.co/BreakFeelingAether/aetherargus:F16
  • LM Studio
  • Jan
  • vLLM

    How to use BreakFeelingAether/aetherargus with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "BreakFeelingAether/aetherargus"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "BreakFeelingAether/aetherargus",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/BreakFeelingAether/aetherargus:F16
  • SGLang

    How to use BreakFeelingAether/aetherargus 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 "BreakFeelingAether/aetherargus" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "BreakFeelingAether/aetherargus",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    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 "BreakFeelingAether/aetherargus" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "BreakFeelingAether/aetherargus",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Ollama

    How to use BreakFeelingAether/aetherargus with Ollama:

    ollama run hf.co/BreakFeelingAether/aetherargus:F16
  • Unsloth Desktop
  • Pi

    How to use BreakFeelingAether/aetherargus with Pi:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf BreakFeelingAether/aetherargus:F16
    Configure the model in Pi
    # Install Pi:
    npm install -g @earendil-works/pi-coding-agent
    # Add to ~/.pi/agent/models.json:
    {
      "providers": {
        "llama-cpp": {
          "baseUrl": "http://localhost:8080/v1",
          "api": "openai-completions",
          "apiKey": "none",
          "models": [
            {
              "id": "BreakFeelingAether/aetherargus:F16"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Docker Model Runner

    How to use BreakFeelingAether/aetherargus with Docker Model Runner:

    docker model run hf.co/BreakFeelingAether/aetherargus:F16
  • Lemonade

    How to use BreakFeelingAether/aetherargus with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull BreakFeelingAether/aetherargus:F16
    Run and chat with the model
    lemonade run user.aetherargus-F16
    List all available models
    lemonade list
  • Hermes Agent

    How to use BreakFeelingAether/aetherargus with Hermes Agent:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf BreakFeelingAether/aetherargus:F16
    Configure Hermes
    # Install Hermes:
    curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
    hermes setup
    # Point Hermes at the local server:
    hermes config set model.provider custom
    hermes config set model.base_url http://127.0.0.1:8080/v1
    hermes config set model.default BreakFeelingAether/aetherargus:F16
    Run Hermes
    hermes
  • Atomic Chat
  • OpenClaw

    How to use BreakFeelingAether/aetherargus with OpenClaw:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf BreakFeelingAether/aetherargus:F16
    Configure OpenClaw
    # Install OpenClaw:
    npm install -g openclaw@latest
    # Register the local server and set it as the default model:
    openclaw onboard --non-interactive --mode local \
      --auth-choice custom-api-key \
      --custom-base-url http://127.0.0.1:8080/v1 \
      --custom-model-id "BreakFeelingAether/aetherargus:F16" \
      --custom-provider-id llama-cpp \
      --custom-compatibility openai \
      --custom-text-input \
      --accept-risk \
      --skip-health
    Run OpenClaw
    openclaw agent --local --agent main --message "Hello from Hugging Face"
aetherargus / aetherargus-lora
77.6 MB
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  • 1 contributor
History: 11 commits
BreakFeelingAether's picture
BreakFeelingAether
Upload aetherargus-lora/training_args.bin with huggingface_hub
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  • README.md
    5.24 kB
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  • adapter_config.json
    1.12 kB
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  • adapter_model.safetensors
    66.1 MB
    xet
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  • chat_template.jinja
    2.63 kB
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  • source-training-config.yaml
    1.35 kB
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  • tokenizer.json
    11.4 MB
    xet
    Upload aetherargus-lora/tokenizer.json with huggingface_hub 3 months ago
  • tokenizer_config.json
    694 Bytes
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  • trainer_state.json
    3.98 kB
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  • training-config.yaml
    1.07 kB
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  • training-summary.json
    1.31 kB
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  • training_args.bin
    5.27 kB
    xet
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