Instructions to use AugustLight/LLightPro 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 AugustLight/LLightPro 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 AugustLight/LLightPro:Q4_K_M # Run inference directly in the terminal: llama cli -hf AugustLight/LLightPro:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AugustLight/LLightPro:Q4_K_M # Run inference directly in the terminal: llama cli -hf AugustLight/LLightPro:Q4_K_M
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 AugustLight/LLightPro:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AugustLight/LLightPro:Q4_K_M
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 AugustLight/LLightPro:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AugustLight/LLightPro:Q4_K_M
Use Docker
docker model run hf.co/AugustLight/LLightPro:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AugustLight/LLightPro with Ollama:
ollama run hf.co/AugustLight/LLightPro:Q4_K_M
- Unsloth Desktop
- Pi
How to use AugustLight/LLightPro with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AugustLight/LLightPro: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": "AugustLight/LLightPro:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use AugustLight/LLightPro with Docker Model Runner:
docker model run hf.co/AugustLight/LLightPro:Q4_K_M
- Lemonade
How to use AugustLight/LLightPro with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AugustLight/LLightPro:Q4_K_M
Run and chat with the model
lemonade run user.LLightPro-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use AugustLight/LLightPro with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AugustLight/LLightPro: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 AugustLight/LLightPro:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use AugustLight/LLightPro with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AugustLight/LLightPro: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 "AugustLight/LLightPro: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"
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Verified signature🧙♂️ LLightPro    Small model. Massive logic. This is a high-fidelity fine-tune of the experimental p-e-w/Qwen3-4B-Instruct-2507-heretic base model, sharpened for complex reasoning, coding, and logic puzzles using the elite GrandMaster2 dataset. Unlike standard LoRA fine-tunes, this model uses DoRA (Weight-Decomposed Low-Rank Adaptation), allowing it to learn subtle nuances in reasoning without catastrophic forgetting. The training was performed in pure bfloat16 (no quantization during training) on an NVIDIA RTX 4090 to ensure maximum precision. 🚀 Key Features 🧠 Advanced Architecture: Built upon the experimental "Heretic" Qwen3 build. ⚡ DoRA Technology: Uses Weight-Decomposed LoRA (r=64, alpha=128) for superior learning capacity compared to standard LoRA. 💎 Uncompromised Quality: Trai…5ea2c48 -
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