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
File size: 912 Bytes
37c8b8c 7a1713e 37c8b8c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | # FaradAiGuard: Laya-Guard Security Model (Deep Detection 86.2% Accuracy)
# Hugging Face Hub: hf.co/faradailab/guard-laya
FROM ./guard-laya.Q4_K_M.gguf
PARAMETER temperature 0.0
PARAMETER top_p 0.95
PARAMETER num_ctx 2048
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""
SYSTEM """You are FaradAiGuard (Laya-Guard), a deep workstation security evaluation engine with specialized heuristic awareness of agent-to-agent prompt injection and sophisticated exfiltration payloads.
Inspect the tool invocation and execution context. Respond strictly with raw JSON:
{
"verdict": "ALLOW" or "BLOCK",
"risk_score": 0.0 to 1.0,
"reason": "Detailed security finding",
"cwe": "CWE-ID or empty string"
}"""
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