Text Generation
GGUF
English
Portuguese
mark
mark-ai
edge-kernel
synthetic-microbenchmarks
experimental
non-conversational
conversational
Instructions to use miceworks/Mark-Engineer-302M 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-302M 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-302M:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-Engineer-302M: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-302M:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-Engineer-302M: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-302M:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf miceworks/Mark-Engineer-302M: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-302M:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf miceworks/Mark-Engineer-302M:IQ4_NL
Use Docker
docker model run hf.co/miceworks/Mark-Engineer-302M:IQ4_NL
- Llama new
- LM Studio
- Jan
- vLLM
How to use miceworks/Mark-Engineer-302M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miceworks/Mark-Engineer-302M" # 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-302M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miceworks/Mark-Engineer-302M:IQ4_NL
- Ollama
How to use miceworks/Mark-Engineer-302M with Ollama:
ollama run hf.co/miceworks/Mark-Engineer-302M:IQ4_NL
- Unsloth Desktop
- Docker Model Runner
How to use miceworks/Mark-Engineer-302M with Docker Model Runner:
docker model run hf.co/miceworks/Mark-Engineer-302M:IQ4_NL
- Lemonade
How to use miceworks/Mark-Engineer-302M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miceworks/Mark-Engineer-302M:IQ4_NL
Run and chat with the model
lemonade run user.Mark-Engineer-302M-IQ4_NL
List all available models
lemonade list
- Atomic Chat
Download mark_engineer_302m_iq4_nl.pt from miceworks/Mark-Engineer-302M: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/miceworks/Mark-Engineer-302M/resolve/main/mark_engineer_302m_iq4_nl.pt
- Command line
-
hf download hf://miceworks/Mark-Engineer-302M/mark_engineer_302m_iq4_nl.pt
-
curl -L -o mark_engineer_302m_iq4_nl.pt https://huggingface.co/miceworks/Mark-Engineer-302M/resolve/main/mark_engineer_302m_iq4_nl.pt
171 MB
- Xet hash:
- 939e84d9098bdb62fb163ad2fef66576670eda9d5e8973919f9df806f2888d74
- Size of remote file:
- 171 MB
- SHA256:
- daf05dd0dab4fc90f620d959cb6e5731c49b3f3c4ddce0ff28765e0751075fd9
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