How to use miceworks/Mark-VL-2.2B with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf miceworks/Mark-VL-2.2B:Q4_K_M # Run inference directly in the terminal: llama cli -hf miceworks/Mark-VL-2.2B:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf miceworks/Mark-VL-2.2B:Q4_K_M # Run inference directly in the terminal: llama cli -hf miceworks/Mark-VL-2.2B:Q4_K_M
# 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-VL-2.2B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf miceworks/Mark-VL-2.2B:Q4_K_M
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-VL-2.2B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf miceworks/Mark-VL-2.2B:Q4_K_M
docker model run hf.co/miceworks/Mark-VL-2.2B:Q4_K_M
How to use miceworks/Mark-VL-2.2B with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miceworks/Mark-VL-2.2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "miceworks/Mark-VL-2.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
How to use miceworks/Mark-VL-2.2B with Ollama:
ollama run hf.co/miceworks/Mark-VL-2.2B:Q4_K_M
How to use miceworks/Mark-VL-2.2B with Docker Model Runner:
How to use miceworks/Mark-VL-2.2B with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miceworks/Mark-VL-2.2B:Q4_K_M
lemonade run user.Mark-VL-2.2B-Q4_K_M
lemonade list
