Rampart PII NER — MLX
An MLX build of Rampart, a compact encoder-only BERT (MiniLM-L6, hidden 384, 6 layers, ~18.5M params) with a 35-label BIO token-classification head for detecting personally identifiable information (PII). Intended for on-device, client-side PII redaction on Apple Silicon.
This repository ships float (fp16) MLX weights in model.safetensors plus a
small self-contained MLX implementation (rampart_mlx.py).
Provenance
This is an independent MLX conversion of the original
nationaldesignstudio/rampart.
The original is distributed as a 4-bit quantized ONNX export; the float weights
here were recovered directly from that export (4-bit MatMulNBits linears and
INT8 embeddings dequantized to float) and then stored in MLX safetensors.
The conversion was verified to reproduce the original ONNX model exactly: on the validation prompts, MLX vs. ONNX Runtime token-label agreement is 100% with a maximum logit difference of ~1e-5 (floating-point rounding).
No third-party MLX port was used in producing these weights.
Labels
17 entity types in BIO format (35 classes incl. O): GIVEN_NAME, SURNAME,
EMAIL, PHONE, URL, TAX_ID, BANK_ACCOUNT, ROUTING_NUMBER,
GOVERNMENT_ID, PASSPORT, DRIVERS_LICENSE, BUILDING_NUMBER, STREET_NAME,
SECONDARY_ADDRESS, CITY, STATE, ZIP_CODE.
Usage
pip install mlx tokenizers
python demo.py "My name is John Smith, email john.smith@example.com"
import mlx.core as mx
from tokenizers import Tokenizer
from rampart_mlx import load
model, cfg = load(".")
tok = Tokenizer.from_file("tokenizer.json")
enc = tok.encode("Call me at (555) 123-4567")
logits = model(mx.array([enc.ids]), mx.array([enc.attention_mask]))
label_ids = mx.argmax(logits[0], axis=-1).tolist()
labels = [cfg.id2label[i] for i in label_ids]
See demo.py for BIO span decoding using the tokenizer's char offsets (needed to
map predicted labels back onto the original text for redaction).
Files
| File | Purpose |
|---|---|
model.safetensors | fp16 MLX weights (HuggingFace-style key names) |
config.json | model architecture + id2label |
rampart_mlx.py | self-contained MLX model + loader |
demo.py | tokenize → infer → decode spans |
tokenizer.json, vocab.txt, tokenizer_config.json, special_tokens_map.json | WordPiece tokenizer |
License & attribution
Released under CC-BY-4.0, the same license as the upstream model. Attribution:
- Original model:
nationaldesignstudio/rampart(CC-BY-4.0) - Base encoder:
nreimers/MiniLM-L6-H384-uncased - Training data:
ai4privacy/pii-masking-openpii-1.5m(CC-BY-4.0)