privacy-filter-nemotron-GGUF is an open-weight GGUF model for detecting and redacting personally identifiable information. It is intended for local inference through llama.cpp, LocalAI, and compatible applications.
In the Other AI space, Privacy Filter Nemotron takes a focused approach. It focuses on detecting and removing personally identifiable information from text during local model inference. It is built as an open-source project for developers building privacy-preserving AI applications. The project is open source (MIT). It runs on the web, the command line, and Linux, and it can be self-hosted.
It is developed by LocalAI, and it first shipped in 2026. The project is developed in the open on GitHub with 286 stars and 44 commits in the last 90 days. Among its 5 catalogued features are PII detection, privacy redaction, and GGUF quantization.
Summary written by a language model from the project’s public pages.
What PulseGate has recorded for this listing
Closest matches by what these projects do