Instructions to use gwyntel/PII-Redact-General-oQ4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use gwyntel/PII-Redact-General-oQ4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download gwyntel/PII-Redact-General-oQ4 --local-dir PII-Redact-General-oQ4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
PII-Redact-General-oQ4
This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization.
Quantization details
- Model type: llama
- Bits: 4
- Group size: 64
- Format: MLX safetensors
Quantization of OpenPipe/PII-Redact-General (oQ4).
Usage
This quant is meant to be served by oMLX and driven through the PII-redaction fork, which pairs it with the Name model and handles tagging, redaction, and fake-data replacement:
https://github.com/gwyntel-git/pii-redact-mlx
pip install "git+https://github.com/gwyntel-git/pii-redact-mlx"
# list the models an oMLX server is serving
pii-redact list-models --omlx-url http://localhost:27473/v1
# redact a trace/JSONL file
pii-redact convert-traces input.jsonl output.jsonl \
--backend omlx --omlx-url http://localhost:27473/v1 --auto-max-tokens
The fork also provides a transformers/torch backend, concurrent inference, and
a labelled validation harness. Validated on an 18-document labelled set: recall
matches the full-precision model, F1 0.816 vs 0.833 (precision 0.870 vs 0.909).
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Model size
1B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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4-bit
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Model tree for gwyntel/PII-Redact-General-oQ4
Base model
OpenPipe/PII-Redact-General