Instructions to use Jibbscript/rptools-privacy-filter-multilingual-v2-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Jibbscript/rptools-privacy-filter-multilingual-v2-mlx-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir rptools-privacy-filter-multilingual-v2-mlx-8bit Jibbscript/rptools-privacy-filter-multilingual-v2-mlx-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
license: apache-2.0
library_name: mlx
pipeline_tag: token-classification
tags:
- mlx
- privacy
- pii-detection
- token-classification
- rptools
RPTools Privacy Filter — Multilingual v2 (MLX, 8-bit)
On-device PII / privacy token classifier packaged for the RPTools / RedactFlow
Apple-Silicon MLX runtime (rptools-mlx manifest format, openai-privacy-filter
architecture family, BIOES-Viterbi decode). Ships the rptools-mlx.json manifest
the RPTools Swift loader expects.
Provenance (honest): the weights, tokenizer, config, and label map are a
byte-for-byte copy of
OpenMed/privacy-filter-multilingual-v2-mlx-8bit
(revision df97c293841c59d253541960908e23b2d3a68212). Only the MLX manifest is
re-emitted under the RPTools rptools-mlx format string (and source_model_id
rebranded); the load-bearing architecture identifier openai-privacy-filter is
preserved. Redistributed under the upstream Apache-2.0 license.
Files
weights.safetensors— 8-bit affine-quantized weights (group size 64)tokenizer.json,tokenizer_config.json— tiktoken-style tokenizerconfig.json,id2label.json— model config + 217-label maprptools-mlx.json— RPTools MLX artifact manifest