Small Language Models Demonstrate On-Prem HR Triage
This article demonstrates how to implement privacy-first "Local First, Cloud Last" agents using small language models (1–3B) for an on-premises HR triage system. It details a multi-model pipeline—MiniLM embeddings for intent detection, Phi-3-mini for planning, and Function Gemma for constrained function execution—running on standard hardware and executing end-to-end within roughly 10–30 seconds. The repo, file descriptions, and execution logs illustrate practical deployment steps for enterprises with strict data-locality requirements.
Key Points
- 1Deploys 1–3B SLMs for local intent detection, planning, and execution in HR triage
- 2Reduces data-exfiltration risk and meets strict privacy and data-locality compliance requirements
- 3Enables practitioners to run end-to-end agent pipelines locally on laptops within 10–30 seconds
Scoring Rationale
Practical, directly usable implementation with enterprise relevance; limited novelty and single-source documentation reduces broader impact.
Sources
Public references used for this report.
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