MonikaV2
Collection
My second group of Monika finetunes for MonikAI • 4 items • Updated • 2
How to use Green-eyedDevil/Monika-9B with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Green-eyedDevil/Monika-9B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Green-eyedDevil/Monika-9B:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Green-eyedDevil/Monika-9B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Green-eyedDevil/Monika-9B:Q4_K_M
# 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 Green-eyedDevil/Monika-9B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Green-eyedDevil/Monika-9B:Q4_K_M
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 Green-eyedDevil/Monika-9B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Green-eyedDevil/Monika-9B:Q4_K_M
docker model run hf.co/Green-eyedDevil/Monika-9B:Q4_K_M
How to use Green-eyedDevil/Monika-9B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Green-eyedDevil/Monika-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Green-eyedDevil/Monika-9B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/Green-eyedDevil/Monika-9B:Q4_K_M
How to use Green-eyedDevil/Monika-9B with Ollama:
ollama run hf.co/Green-eyedDevil/Monika-9B:Q4_K_M
How to use Green-eyedDevil/Monika-9B with Pi:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Green-eyedDevil/Monika-9B:Q4_K_M
# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "Green-eyedDevil/Monika-9B:Q4_K_M"
}
]
}
}
}# Start Pi in your project directory: pi
How to use Green-eyedDevil/Monika-9B with Docker Model Runner:
docker model run hf.co/Green-eyedDevil/Monika-9B:Q4_K_M
How to use Green-eyedDevil/Monika-9B with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Green-eyedDevil/Monika-9B:Q4_K_M
lemonade run user.Monika-9B-Q4_K_M
lemonade list
How to use Green-eyedDevil/Monika-9B with Hermes Agent:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Green-eyedDevil/Monika-9B:Q4_K_M
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Green-eyedDevil/Monika-9B:Q4_K_M
hermes
How to use Green-eyedDevil/Monika-9B with OpenClaw:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Green-eyedDevil/Monika-9B:Q4_K_M
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Green-eyedDevil/Monika-9B:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
openclaw agent --local --agent main --message "Hello from Hugging Face"
This model is designed to be used with MonikAI.
RP
Should really only be used for Monika related purposes.
Thanks Sylphar for making the dataset.
Trained with Axolotl on my Blackwell Pro 6000 Max-Q. 48 rank, 96 alpha, 2 epochs, 0.0025 learning rate. Took about 20 minutes and used about 25GB of VRAM.