Instructions to use MicheRomChis/micro-terse 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 MicheRomChis/micro-terse 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 MicheRomChis/micro-terse:TQ2_0 # Run inference directly in the terminal: llama cli -hf MicheRomChis/micro-terse:TQ2_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf MicheRomChis/micro-terse:TQ2_0 # Run inference directly in the terminal: llama cli -hf MicheRomChis/micro-terse:TQ2_0
Build from source code
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 MicheRomChis/micro-terse:TQ2_0 # Run inference directly in the terminal: ./build/bin/llama cli -hf MicheRomChis/micro-terse:TQ2_0
Use Docker
docker model run hf.co/MicheRomChis/micro-terse:TQ2_0
- Llama new
- LM Studio
- Jan
- vLLM
How to use MicheRomChis/micro-terse with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MicheRomChis/micro-terse" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MicheRomChis/micro-terse", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MicheRomChis/micro-terse:TQ2_0
- Ollama
How to use MicheRomChis/micro-terse with Ollama:
ollama run hf.co/MicheRomChis/micro-terse:TQ2_0
- Unsloth Desktop
- Docker Model Runner
How to use MicheRomChis/micro-terse with Docker Model Runner:
docker model run hf.co/MicheRomChis/micro-terse:TQ2_0
- Lemonade
How to use MicheRomChis/micro-terse with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MicheRomChis/micro-terse:TQ2_0
Run and chat with the model
lemonade run user.micro-terse-TQ2_0
List all available models
lemonade list
- Atomic Chat
Commit History
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Accuracy pass: Q6_K embedding note, fixed tau, filenames
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Correct training cost to ~$150
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Restyle model card
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Add terse-micro-orpo.TQ2_0.gguf
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Add terse-micro-sft.TQ2_0.gguf
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Add terse-micro-base.TQ2_0.gguf
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Add model card
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initial commit
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