privacy-filter
OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable.
Params
1.4 B
Context
131,072
Downloads 30d
234 K
Likes
1,760
Download history
daily snapshots · 55 days
▲ 223 K in the last 30 days (48.8%)
457 K231 K
Aug 22Sep 1Sep 11Sep 20
536 K231 K
Jul 28Aug 15Sep 2Sep 20
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | bf16 | 5.6 GB | 6.9 GB | ✅ Runs comfortably |
| model.safetensors (bf16, full) | bf16 + 128K ctx | 5.6 GB | 10.0 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- OpenAIPrivacyFilterForTokenClassification
- Parameters
- 1.4 B
- Tensor type
- BF16
- Context length
- 131,072
- Vocabulary
- 200,064
- Layers / heads
- 8 / 14
- Licence
- apache-2.0
- First seen on the Hub
- 2026-04-17
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
Compare with any token-classification model