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antibyte
/
AuraGo-Ling

Text Generation
GGUF
German
English
aurago
tool-calling
mixture-of-experts
imatrix
conversational
Model card Files Files and versions
xet
Community

Instructions to use antibyte/AuraGo-Ling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use antibyte/AuraGo-Ling 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 antibyte/AuraGo-Ling
    # Run inference directly in the terminal:
    llama cli -hf antibyte/AuraGo-Ling
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf antibyte/AuraGo-Ling
    # Run inference directly in the terminal:
    llama cli -hf antibyte/AuraGo-Ling
    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 antibyte/AuraGo-Ling
    # Run inference directly in the terminal:
    ./llama-cli -hf antibyte/AuraGo-Ling
    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 antibyte/AuraGo-Ling
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf antibyte/AuraGo-Ling
    Use Docker
    docker model run hf.co/antibyte/AuraGo-Ling
  • LM Studio
  • Jan
  • vLLM

    How to use antibyte/AuraGo-Ling with vLLM:

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

    How to use antibyte/AuraGo-Ling with Ollama:

    ollama run hf.co/antibyte/AuraGo-Ling
  • Unsloth Desktop
  • Pi

    How to use antibyte/AuraGo-Ling with Pi:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf antibyte/AuraGo-Ling
    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": "antibyte/AuraGo-Ling"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Docker Model Runner

    How to use antibyte/AuraGo-Ling with Docker Model Runner:

    docker model run hf.co/antibyte/AuraGo-Ling
  • Lemonade

    How to use antibyte/AuraGo-Ling with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull antibyte/AuraGo-Ling
    Run and chat with the model
    lemonade run user.AuraGo-Ling-{{QUANT_TAG}}
    List all available models
    lemonade list
  • Hermes Agent

    How to use antibyte/AuraGo-Ling with Hermes Agent:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf antibyte/AuraGo-Ling
    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 antibyte/AuraGo-Ling
    Run Hermes
    hermes
  • Atomic Chat
  • OpenClaw

    How to use antibyte/AuraGo-Ling with OpenClaw:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf antibyte/AuraGo-Ling
    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 "antibyte/AuraGo-Ling" \
      --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"
AuraGo-Ling
5.1 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
antibyte's picture
antibyte
Publish verified AuraGo-Ling Q4_K_L export and evaluation
c9d1e3f verified 8 days ago
  • runtime
    Publish verified AuraGo-Ling Q4_K_L export and evaluation 8 days ago
  • .gitattributes
    1.59 kB
    Publish verified AuraGo-Ling Q4_K_L export and evaluation 8 days ago
  • AuraGo-Ling-3.0-tiny-Q4_K_L.gguf
    5.1 GB
    xet
    Publish verified AuraGo-Ling Q4_K_L export and evaluation 8 days ago
  • README.md
    8.35 kB
    Publish verified AuraGo-Ling Q4_K_L export and evaluation 8 days ago
  • SHA256SUMS.txt
    99 Bytes
    Publish verified AuraGo-Ling Q4_K_L export and evaluation 8 days ago