- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MLVXN/microllm 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 MLVXN/microllm:F16
# Run inference directly in the terminal:
llama cli -hf MLVXN/microllm:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MLVXN/microllm:F16
# Run inference directly in the terminal:
llama cli -hf MLVXN/microllm:F16
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 MLVXN/microllm:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf MLVXN/microllm:F16
Use Docker
docker model run hf.co/MLVXN/microllm:F16
- Llama new
- LM Studio
- Jan
- vLLM
How to use MLVXN/microllm with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MLVXN/microllm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "MLVXN/microllm",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Use Docker
docker model run hf.co/MLVXN/microllm:F16
- Ollama
How to use MLVXN/microllm with Ollama:
ollama run hf.co/MLVXN/microllm:F16
- Unsloth Desktop
- Docker Model Runner
How to use MLVXN/microllm with Docker Model Runner:
docker model run hf.co/MLVXN/microllm:F16
- Lemonade
How to use MLVXN/microllm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull MLVXN/microllm:F16
Run and chat with the model
lemonade run user.microllm-F16
List all available models
lemonade list
- Atomic Chat
