How to use miceworks/Mark-SLM-V3-150M 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-SLM-V3-150M:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-SLM-V3-150M:IQ4_NL
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf miceworks/Mark-SLM-V3-150M:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-SLM-V3-150M:IQ4_NL
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf miceworks/Mark-SLM-V3-150M:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama cli -hf miceworks/Mark-SLM-V3-150M:IQ4_NL
docker model run hf.co/miceworks/Mark-SLM-V3-150M:IQ4_NL
How to use miceworks/Mark-SLM-V3-150M with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miceworks/Mark-SLM-V3-150M" # 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-SLM-V3-150M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use miceworks/Mark-SLM-V3-150M with Ollama:
ollama run hf.co/miceworks/Mark-SLM-V3-150M:IQ4_NL
How to use miceworks/Mark-SLM-V3-150M with Docker Model Runner:
How to use miceworks/Mark-SLM-V3-150M with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miceworks/Mark-SLM-V3-150M:IQ4_NL
lemonade run user.Mark-SLM-V3-150M-IQ4_NL
lemonade list
https://huggingface.co/miceworks/Mark-SLM-V3-150M/resolve/main/tokenizer.json
hf download hf://miceworks/Mark-SLM-V3-150M/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/miceworks/Mark-SLM-V3-150M/resolve/main/tokenizer.json
