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miceworks
/
Mark-Engineer-1.5B

Text Generation
GGUF
Rust
English
Portuguese
mark
mark-ai
code
systems-engineering
cpp
c
linux-kernel
ollama
conversational
Model card Files Files and versions
xet
Community

Instructions to use miceworks/Mark-Engineer-1.5B 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 miceworks/Mark-Engineer-1.5B 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 miceworks/Mark-Engineer-1.5B:IQ4_NL
    # Run inference directly in the terminal:
    llama cli -hf miceworks/Mark-Engineer-1.5B:IQ4_NL
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf miceworks/Mark-Engineer-1.5B:IQ4_NL
    # Run inference directly in the terminal:
    llama cli -hf miceworks/Mark-Engineer-1.5B:IQ4_NL
    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 miceworks/Mark-Engineer-1.5B:IQ4_NL
    # Run inference directly in the terminal:
    ./llama-cli -hf miceworks/Mark-Engineer-1.5B:IQ4_NL
    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 miceworks/Mark-Engineer-1.5B:IQ4_NL
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf miceworks/Mark-Engineer-1.5B:IQ4_NL
    Use Docker
    docker model run hf.co/miceworks/Mark-Engineer-1.5B:IQ4_NL
  • Llama new
  • LM Studio
  • Jan
  • vLLM

    How to use miceworks/Mark-Engineer-1.5B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "miceworks/Mark-Engineer-1.5B"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "miceworks/Mark-Engineer-1.5B",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/miceworks/Mark-Engineer-1.5B:IQ4_NL
  • Ollama

    How to use miceworks/Mark-Engineer-1.5B with Ollama:

    ollama run hf.co/miceworks/Mark-Engineer-1.5B:IQ4_NL
  • Unsloth Desktop
  • Docker Model Runner

    How to use miceworks/Mark-Engineer-1.5B with Docker Model Runner:

    docker model run hf.co/miceworks/Mark-Engineer-1.5B:IQ4_NL
  • Lemonade

    How to use miceworks/Mark-Engineer-1.5B with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull miceworks/Mark-Engineer-1.5B:IQ4_NL
    Run and chat with the model
    lemonade run user.Mark-Engineer-1.5B-IQ4_NL
    List all available models
    lemonade list
  • Atomic Chat
Mark-Engineer-1.5B
1.56 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
miceworks's picture
miceworks
fix: add config.json specifying exact parameter count
ed694b2 verified about 6 hours ago
  • .gitattributes
    1.59 kB
    feat: upload GGUF weights (mark_engineer_1_5b_iq4_nl.gguf) about 23 hours ago
  • Modelfile
    534 Bytes
    feat: upload MODELFILE weights (Modelfile) about 23 hours ago
  • README.md
    5.67 kB
    docs: update model card with sober technical analysis and realistic benchmarks about 23 hours ago
  • config.json
    1.26 kB
    fix: add config.json specifying exact parameter count about 6 hours ago
  • mark_engineer_1_5b_iq4_nl.gguf
    704 MB
    xet
    feat: upload GGUF weights (mark_engineer_1_5b_iq4_nl.gguf) about 23 hours ago
  • mark_engineer_1_5b_iq4_nl.pt
    854 MB
    xet
    feat: upload PT weights (mark_engineer_1_5b_iq4_nl.pt) about 23 hours ago
  • mark_engineer_1_5b_metadata.json
    389 Bytes
    feat: upload METADATA weights (mark_engineer_1_5b_metadata.json) about 23 hours ago