Instructions to use nikowru/koalipi-slm 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 nikowru/koalipi-slm 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 nikowru/koalipi-slm:F16 # Run inference directly in the terminal: llama cli -hf nikowru/koalipi-slm:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nikowru/koalipi-slm:F16 # Run inference directly in the terminal: llama cli -hf nikowru/koalipi-slm:F16
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 nikowru/koalipi-slm:F16 # Run inference directly in the terminal: ./llama-cli -hf nikowru/koalipi-slm:F16
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 nikowru/koalipi-slm:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf nikowru/koalipi-slm:F16
Use Docker
docker model run hf.co/nikowru/koalipi-slm:F16
- LM Studio
- Jan
- Ollama
How to use nikowru/koalipi-slm with Ollama:
ollama run hf.co/nikowru/koalipi-slm:F16
- Unsloth Desktop
- Docker Model Runner
How to use nikowru/koalipi-slm with Docker Model Runner:
docker model run hf.co/nikowru/koalipi-slm:F16
- Lemonade
How to use nikowru/koalipi-slm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nikowru/koalipi-slm:F16
Run and chat with the model
lemonade run user.koalipi-slm-F16
List all available models
lemonade list
- Atomic Chat
Update README.md
Browse files
README.md
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## Instruction Format
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No system prompt needed. Uses ChatML format:
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**Document parsing:**
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## Instruction Format
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No system prompt needed. Uses ChatML format:
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**Document parsing:**
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Parse this document. Return ONLY a valid JSON with these fields:
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topics, difficulty, subject, key_concepts, has_equations, equations, summary.
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No explanation, just JSON.
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**MCQ generation:**
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Generate 3 multiple choice questions about {topic}.
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Return ONLY valid JSON array.
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**Filipino Q&A:**
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{question in Filipino or English}
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## Intended Use
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- Filipino high school and college STEM students
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- On-device inference via llama.rn
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- Part of the KoaliPi AI study app
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## Limitations
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- mmproj (vision encoder) not yet exportable via llama.cpp for Qwen2.5-VL
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- Image/VL features require mmproj — text features fully functional
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- 30 training steps — suitable for demo, expand dataset for production
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