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aethertp
/
PicoLM-80M-Instruct

Text Generation
Safetensors
GGUF
PyTorch
English
picolm
conversational
causal-lm
slm
on-device
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use aethertp/PicoLM-80M-Instruct 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 aethertp/PicoLM-80M-Instruct 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 aethertp/PicoLM-80M-Instruct
    # Run inference directly in the terminal:
    llama cli -hf aethertp/PicoLM-80M-Instruct
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf aethertp/PicoLM-80M-Instruct
    # Run inference directly in the terminal:
    llama cli -hf aethertp/PicoLM-80M-Instruct
    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 aethertp/PicoLM-80M-Instruct
    # Run inference directly in the terminal:
    ./llama-cli -hf aethertp/PicoLM-80M-Instruct
    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 aethertp/PicoLM-80M-Instruct
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf aethertp/PicoLM-80M-Instruct
    Use Docker
    docker model run hf.co/aethertp/PicoLM-80M-Instruct
  • LM Studio
  • Jan
  • vLLM

    How to use aethertp/PicoLM-80M-Instruct with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "aethertp/PicoLM-80M-Instruct"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "aethertp/PicoLM-80M-Instruct",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/aethertp/PicoLM-80M-Instruct
  • Ollama

    How to use aethertp/PicoLM-80M-Instruct with Ollama:

    ollama run hf.co/aethertp/PicoLM-80M-Instruct
  • Unsloth Desktop
  • Docker Model Runner

    How to use aethertp/PicoLM-80M-Instruct with Docker Model Runner:

    docker model run hf.co/aethertp/PicoLM-80M-Instruct
  • Lemonade

    How to use aethertp/PicoLM-80M-Instruct with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull aethertp/PicoLM-80M-Instruct
    Run and chat with the model
    lemonade run user.PicoLM-80M-Instruct-{{QUANT_TAG}}
    List all available models
    lemonade list
  • Atomic Chat
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Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Overcoming the ARC-Easy floor and vocabulary budget on an 80M footprint

❤️ 1
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#1 opened 1 day ago by
AndrewThompson1233
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