distil-labs/Distil-PII-Llama-3.2-3B-Instruct
Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:3.2BQuant:BF16Ctx Length:32kPublished:Oct 13, 2025License:llama3.2Architecture:Transformer0.0K Warm

Distil-PII-Llama-3.2-3B-Instruct is a 3.2 billion parameter small language model developed by Distil Labs, fine-tuned from Meta's Llama-3.2-3B-Instruct. It specializes in policy-aware PII redaction, outputting a single JSON object with redacted text and identified entities. Optimized for local deployment, its primary use case is redacting sensitive personal data from support chats, logs, and transcripts while preserving operational signals.

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Distil-PII-Llama-3.2-3B-Instruct Overview

Distil-PII-Llama-3.2-3B-Instruct is a 3.2 billion parameter small language model (SLM) developed by Distil Labs, fine-tuned from meta-llama/Llama-3.2-3B-Instruct. This model is specifically optimized for policy-aware PII redaction, designed to run efficiently in local environments. It processes plain-text inputs and consistently outputs a single JSON object containing the redacted_text and a detailed list of entities that were replaced.

Key Capabilities

  • Precise PII Redaction: Identifies and redacts various PII types including names, emails, phone numbers, addresses, SSNs, national IDs, UUIDs, credit card/IBAN last-4s, gender, age, race, and marital status.
  • Schema Adherence: Guarantees output in a strict JSON format, including redacted_text and an array of entities with value, replacement_token, and reason fields.
  • Operational Signal Preservation: Designed to remove identity information while retaining crucial operational data like order numbers, ticket IDs, and last-4 digits of financial identifiers.
  • High Accuracy: Achieves an evaluation score of **0.82

Good For

  • Support Chat & Log Redaction: Ideal for anonymizing customer support interactions, system logs, and incident tickets.
  • Transcript Processing: Suitable for redacting sensitive information from call transcripts and other spoken-word data.
  • Local Deployment: Optimized for running on-premises using frameworks like vLLM or Ollama, ensuring data privacy and control.
  • Structured Data Output: Provides a reliable JSON output, simplifying integration into automated data processing pipelines.
Finetunes
2 models