Instructions to use emqnuele/Ludomi-3 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 emqnuele/Ludomi-3 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 emqnuele/Ludomi-3:Q4_K_M # Run inference directly in the terminal: llama cli -hf emqnuele/Ludomi-3:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf emqnuele/Ludomi-3:Q4_K_M # Run inference directly in the terminal: llama cli -hf emqnuele/Ludomi-3:Q4_K_M
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 emqnuele/Ludomi-3:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf emqnuele/Ludomi-3:Q4_K_M
Use Docker
docker model run hf.co/emqnuele/Ludomi-3:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use emqnuele/Ludomi-3 with Ollama:
ollama run hf.co/emqnuele/Ludomi-3:Q4_K_M
- Unsloth Desktop
- Pi
How to use emqnuele/Ludomi-3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf emqnuele/Ludomi-3:Q4_K_M
Configure the model in Pi
# 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": "emqnuele/Ludomi-3:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use emqnuele/Ludomi-3 with Docker Model Runner:
docker model run hf.co/emqnuele/Ludomi-3:Q4_K_M
- Lemonade
How to use emqnuele/Ludomi-3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull emqnuele/Ludomi-3:Q4_K_M
Run and chat with the model
lemonade run user.Ludomi-3-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use emqnuele/Ludomi-3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf emqnuele/Ludomi-3:Q4_K_M
Configure Hermes
# 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 emqnuele/Ludomi-3:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use emqnuele/Ludomi-3 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf emqnuele/Ludomi-3:Q4_K_M
Configure OpenClaw
# 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 "emqnuele/Ludomi-3:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- Ludomi3-2b.Q4_K_M.gguf +3 -0
- Modelfile +20 -0
.gitattributes
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Ludomi3-2b.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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Ludomi3-2b.Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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size 1274396256
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FROM ./Ludomi3-2b-q4km.gguf
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TEMPLATE """<|im_start|>system
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{{ .System }}<|im_end|>
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<|im_start|>user
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{{ .Prompt }}<|im_end|>
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<|im_start|>assistant
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{{ .Response }}<|im_end|>
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"""
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SYSTEM """Sei Ludomi-3."""
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PARAMETER stop "<|im_start|>"
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|endoftext|>"
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PARAMETER temperature 0.8
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PARAMETER top_p 0.95
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PARAMETER top_k 50
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PARAMETER repeat_penalty 1.05
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