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privacy-filter-multilingual
A multilingual PII token-classification model: a fine-tune of openai/privacy-filter by OpenMed. It labels every token with a BIOES tag over 54 PII categories (217 classes) across 16 languages (ar, bn, de, en, es, fr, hi, it, ja, ko, nl, pt, te, tr, vi, zh), spanning identity, contact, address, financial, vehicle, digital, and crypto entities. In LocalAI this is a PII detector for the NER redactor tier: set known_usecases to [token_classify] (as below), and any model opts into redaction by listing this one under pii.detectors. The detection policy (which categories to mask vs block, and the score threshold) lives on this model's own pii_detection block - see the overrides below. It runs locally with no Python, served by the standalone privacy-filter backend's TokenClassify RPC (constrained BIOES Viterbi decode into UTF-8 byte-offset entity spans). Architecture: gpt-oss-style sparse MoE (8 layers, 128 experts top-4, ~50M active per token), bidirectional banded attention, o200k tokenizer; served via the openai-privacy-filter architecture. F16, ~2.7 GB.

Repository: localaiLicense: apache-2.0

privacy-filter-nemotron
A fine-grained English PII token-classification model: a fine-tune of openai/privacy-filter by OpenMed on NVIDIA's Nemotron-PII dataset. It labels every token with a BIOES tag over 55 PII categories (221 classes), trading the multilingual sibling's language breadth for category depth - identity, contact, address, dates, government IDs, financial, healthcare, enterprise, vehicle and digital entities (including api_key, ipv4/ipv6 and mac_address). For multilingual text prefer privacy-filter-multilingual instead. In LocalAI this is a PII detector for the NER redactor tier: set known_usecases to [token_classify] (as below), and any model opts into redaction by listing this one under pii.detectors. The detection policy (which categories to mask vs block, and the score threshold) lives on this model's own pii_detection block - see the overrides below. It runs locally with no Python, served by the standalone privacy-filter backend's TokenClassify RPC (constrained BIOES Viterbi decode into UTF-8 byte-offset entity spans). Architecture: gpt-oss-style sparse MoE (8 layers, d_model 640, 128 experts top-4, ~1.5B total / ~50M active per token), bidirectional banded attention, o200k tokenizer and a 221-way token-classification head; served via the openai-privacy-filter architecture. F16, ~2.8 GB. (A smaller Q8_0 quant exists on the GGUF repo for RAM-constrained use - validate it on your own data, since for PII a single dropped span is a leak.)

Repository: localaiLicense: apache-2.0

privacy-filter-nemotron-q8
Q8_0 quant of privacy-filter-nemotron (~1.64 GB, vs ~2.8 GB for F16) for RAM-constrained / edge use (e.g. a 4 GB Raspberry Pi 5). The MoE expert weights are stored 8-bit; attention, embeddings and the classifier head stay F16. Same model, policy and runtime as the F16 entry - see privacy-filter-nemotron for the full description. Prefer the F16 entry when you can afford it: it is the reference artifact. On a mixed-PII document the publisher measured q8 matching F16 on 99.93% of token labels with an identical span set at threshold 0.5 - but one token flipped, and for PII a single dropped span is a leak. Treat q8 as a deliberate size/speed tradeoff and validate it on your own data.

Repository: localaiLicense: apache-2.0

secret-filter
A pattern-based PII detector for high-entropy, highly-regular secrets — API keys, tokens, and private-key blocks — that the NER tier cannot catch (it has no credential class, so it fragments a key and may leave the secret part exposed). Detection is bounded restricted-regex compiled to RE2 (linear time, no backtracking); it runs entirely in-process with no model download, no backend, and zero VRAM. Install it, then reference it under another model's pii.detectors (or set it as the instance-wide default detector on the Middleware page) to block leaks of known credential formats out of the box. Add your own patterns under pii_detection.patterns in a restricted regex subset (e.g. "tok-\\w{32,}"); each must carry a fixed literal anchor of at least 3 characters, so open- ended shapes like email addresses are rejected and left to the NER tier.

Repository: localaiLicense: apache-2.0