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DataBubble·

Model Detail

openai logo

privacy-filter

▲ 6.2%
Provider: OpenAICategory: llmPipeline: token-classification
DB Score
1.3
Downloads
388K
Likes
2K
Day
+6.2%
Week
+0.0%
Month
+19.1%
Overview

privacy-filter is a large language model with 700M parameters released by OpenAI. The model is registered under the token-classification pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.

Technical

privacy-filter ships with 700M parameters. Total weight footprint is approximately 1.4 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Trending Signal

Downloads of privacy-filter have moved +6.2% over the past 24 hours, +19.1% over the trailing thirty days. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.

Read about databubble_score →
Use Cases

privacy-filter is best fit for general-purpose chat and instruction-following workloads. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.

Download History
Model Info
Licenseapache-2.0
Recent newsView all news →
Related News
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NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs

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Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering

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Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

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A Systematic Evaluation of Traditional Privacy Policy Analysis Tools Against LLMs

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