NeMo Guardrails, the Ultimate Open-Source LLM Security Toolkit
Source: https://towardsdatascience.com/safeguarding-llms-with-guardrails-4f5d9f57cff2 Published: 9 February 2024 · Author: Wenqi Glantz
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Summary
A head-to-head of the two mainstream guardrail frameworks, with a working RAG implementation behind it.
The Comparison
Llama Guard — a fine-tuned Llama 2 derivative acting as an input-output safeguard, with six built-in unsafe categories extensible with custom classifications.
NeMo Guardrails (NVIDIA) — the broader programmable framework: content moderation, topic steering, hallucination reduction, and response shaping.
| Result | |
|---|---|
| Input-moderation accuracy (18 security test prompts) | 89% for both |
| Hardware | NeMo: free-tier T4 · Llama Guard: A100 |
Equal accuracy at materially different hardware cost is the operative finding.
Implementation Stack
LlamaIndex with RecursiveRetrieverSmallToBigPack · Colang for rail flows · OpenAI GPT-3.5-turbo · config files config.yml, prompts.yml, bot_flows.co, actions.py
Related Concepts
- LLM Guardrails — primary topic
- RAG · Hallucination Detection · Text Chunking