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mradermacher
/
scout-4b-GGUF

Text Generation
Transformers
GGUF
English
reasoning
tactical-analysis
cognitive-architectures
problem-solving
reconnaissance
devops
chatbot
gemma
vanta-research
large-language-model
persona-ai
personality
tactical
LLM
language-model
chat
scout
conversational-ai
conversational
roleplay
chat-llm
ai-research
ai-alignment-research
ai-alignment
ai-behavior-research
human-ai-collaboration
Model card Files Files and versions
xet
Community

Instructions to use mradermacher/scout-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mradermacher/scout-4b-GGUF with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="mradermacher/scout-4b-GGUF")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("mradermacher/scout-4b-GGUF", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use mradermacher/scout-4b-GGUF 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 mradermacher/scout-4b-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf mradermacher/scout-4b-GGUF:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf mradermacher/scout-4b-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf mradermacher/scout-4b-GGUF:Q4_K_M
    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 mradermacher/scout-4b-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf mradermacher/scout-4b-GGUF:Q4_K_M
    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 mradermacher/scout-4b-GGUF:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf mradermacher/scout-4b-GGUF:Q4_K_M
    Use Docker
    docker model run hf.co/mradermacher/scout-4b-GGUF:Q4_K_M
  • LM Studio
  • Jan
  • vLLM

    How to use mradermacher/scout-4b-GGUF with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "mradermacher/scout-4b-GGUF"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mradermacher/scout-4b-GGUF",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/mradermacher/scout-4b-GGUF:Q4_K_M
  • SGLang

    How to use mradermacher/scout-4b-GGUF 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 "mradermacher/scout-4b-GGUF" \
        --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": "mradermacher/scout-4b-GGUF",
    		"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 "mradermacher/scout-4b-GGUF" \
            --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": "mradermacher/scout-4b-GGUF",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Ollama

    How to use mradermacher/scout-4b-GGUF with Ollama:

    ollama run hf.co/mradermacher/scout-4b-GGUF:Q4_K_M
  • Unsloth Desktop
  • Docker Model Runner

    How to use mradermacher/scout-4b-GGUF with Docker Model Runner:

    docker model run hf.co/mradermacher/scout-4b-GGUF:Q4_K_M
  • Lemonade

    How to use mradermacher/scout-4b-GGUF with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull mradermacher/scout-4b-GGUF:Q4_K_M
    Run and chat with the model
    lemonade run user.scout-4b-GGUF-Q4_K_M
    List all available models
    lemonade list
  • Atomic Chat
scout-4b-GGUF
35.8 GB
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  • 1 contributor
History: 18 commits
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mradermacher
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