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privacy-filter-mlx

Token Classification
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privacy
pii-detection
bioes
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Initial int4 v0.1.0 bundle
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metadata 
license: apache-2.0
base_model: openai/privacy-filter
tags:
  - mlx
  - token-classification
  - privacy
  - pii-detection
  - bioes
library_name: mlx
pipeline_tag: token-classification

privacy-filter-mlx (int4)

MLX-converted, int4-quantized weights of openai/privacy-filter, packaged for use with PrivacyFilterKit — a Swift package that runs on-device PII detection on Apple platforms via MLX-Swift.

Bundle contents

File Purpose
weights.safetensors int4 affine-quantized weights (group_size=64). Embedding + classifier head kept full-precision.
tokenizer.json Hugging Face tokenizer (copied verbatim from upstream).
tokenizer_config.json Tokenizer config.
id2label.json 33-label BIOES table (8 entity types: account_number, private_address, private_date, private_email, private_person, private_phone, private_url, secret).
model_config.json Architecture parameters consumed by the Swift runtime.
MANIFEST.json SHA-256 hashes of every file in the bundle.

Architecture

  • 8 transformer layers, hidden size 640, 14 attention heads (2 KV heads, GQA)
  • 128 local experts, top-4 MoE routing
  • 200 064 vocab, 131 072 max position embeddings, sliding-window attention (128)
  • 33-label BIOES head; the Swift decoder derives a BIOES validity mask at runtime (no learned CRF transition matrix in the upstream checkpoint)

Usage (Swift) 

import PrivacyFilterKit

let bundle = URL(fileURLWithPath: "/path/to/privacy-filter-int4-v0.1.0")
let filter = try await PrivacyFilter(source: .directory(bundle))
let entities = try await filter.detect(in: "Email me at jane@example.com")

See the PrivacyFilterKit README for the full API.

Conversion pipeline

The conversion was produced by the scripts in privacy-filter-swift/scripts/:

  1. 01_download_hf.py — download the upstream checkpoint
  2. 02_export_config.py — extract label table, tokenizer, normalized model config
  3. 03_convert_mlx.py — rename keys, downcast to bf16, write MLX-friendly safetensors
  4. 04_quantize_mlx.py — int4 affine quantization (embedding + classifier head full-precision)
  5. 06_export_bundle.py — assemble bundle + MANIFEST + tar.gz archive

License

Apache 2.0, inherited from the upstream model. See LICENSE.

Modifications from upstream

This bundle is a derivative of openai/privacy-filter. Significant changes:

  • Weights converted from PyTorch safetensors to MLX-format safetensors (key rename + bf16 cast).
  • int4 affine-quantized (group_size=64). Embedding, classifier head, and any transition matrix are kept full-precision.
  • Bundle adds model_config.json, id2label.json, and MANIFEST.json for the Swift runtime; no model logic is changed.

Credits