Instructions to use faradailab/guard-laya 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 faradailab/guard-laya 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 faradailab/guard-laya:Q4_K_M # Run inference directly in the terminal: llama cli -hf faradailab/guard-laya:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf faradailab/guard-laya:Q4_K_M # Run inference directly in the terminal: llama cli -hf faradailab/guard-laya: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 faradailab/guard-laya:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf faradailab/guard-laya: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 faradailab/guard-laya:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf faradailab/guard-laya:Q4_K_M
Use Docker
docker model run hf.co/faradailab/guard-laya:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use faradailab/guard-laya with Ollama:
ollama run hf.co/faradailab/guard-laya:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use faradailab/guard-laya with Docker Model Runner:
docker model run hf.co/faradailab/guard-laya:Q4_K_M
- Lemonade
How to use faradailab/guard-laya with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull faradailab/guard-laya:Q4_K_M
Run and chat with the model
lemonade run user.guard-laya-Q4_K_M
List all available models
lemonade list
- Atomic Chat
FaradAiGuard Laya-Guard Security SLM π‘οΈ
Deep heuristic security evaluation model with agent-to-agent prompt injection defense. Part of the FaradAiGuard Security SLM Factory.
π Instant Ollama Run
Run this model directly without local configuration or compilation:
ollama run hf.co/faradailab/guard-laya:latest
βοΈ Model Details
- Distribution: Hugging Face Hub (
faradailab/guard-laya) - Format: GGUF (
guard-laya.Q4_K_M.gguf) - System Prompt & Strict JSON Grammar: Pre-configured via
Modelfile. - Repository: https://github.com/kurtfi/FaradAiGuard
π Strict Output Schema (GBNF Grammar)
FaradAiGuard models return strictly validated JSON:
{
"verdict": "ALLOW" | "BLOCK",
"risk_score": 0.0 - 1.0,
"reason": "Brief technical security explanation",
"cwe": "CWE-ID or empty string"
}
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