| --- |
| 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](https://huggingface.co/openai/privacy-filter), |
| packaged for use with [PrivacyFilterKit](https://github.com/kokluch/privacy-filter-swift) — a Swift package |
| that runs on-device PII detection on Apple platforms via [MLX-Swift](https://github.com/ml-explore/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) |
|
|
| ```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](https://github.com/kokluch/privacy-filter-swift) for the full API. |
|
|
| ## Conversion pipeline |
|
|
| The conversion was produced by the scripts in [`privacy-filter-swift/scripts/`](https://github.com/kokluch/privacy-filter-swift/tree/main/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](https://www.apache.org/licenses/LICENSE-2.0.txt). |
|
|
| ## 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 |
|
|
| - Upstream model: [`openai/privacy-filter`](https://huggingface.co/openai/privacy-filter) |
| - Swift runtime: [PrivacyFilterKit](https://github.com/kokluch/privacy-filter-swift) |
| - Conversion runtime: [MLX](https://github.com/ml-explore/mlx) / [MLX-Swift](https://github.com/ml-explore/mlx-swift) |
|
|
