How to use from
llama.cppInstall 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_MUse 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_MBuild 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_MUse Docker
docker model run hf.co/faradailab/guard-laya:Q4_K_MQuick Links
FaradAiGuard Laya-Guard Security SLM 🛡️
Deep heuristic security evaluation model with agent-to-agent prompt injection defense. Part of the FaradAiGuard Security SLM Factory.
🚀 Instant Ollama Run
Run this model directly without local configuration or compilation:
ollama run hf.co/faradailab/guard-laya:latest
⚙️ 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
📐 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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Install (macOS, Linux)
# 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