redax-8b

redax-8b

Qwen3-8B fine-tuned to find personally identifying information in text and return the exact spans. Built as the LLM strategy of redax, a schema-driven de-identification engine.

Model Details

• Developed by: Dylan Murzello
• Model type: causal LM, full-parameter SFT for schema-conditioned span extraction
• Language: English
• License: Apache-2.0
• Finetuned from: Qwen/Qwen3-8B

Uses

The system prompt names a schema: the labels to find and guard rules for lookalikes that must be left alone. The model reads the input text and returns a JSON array of {"text": ..., "label": ...} objects — substrings copied character-for-character, nothing rewritten. When nothing qualifies it answers [], and it means it: roughly a fifth of the training data is traps (clinical values, order numbers, codes that look sensitive and are not).

Out-of-scope: this is not a compliance tool. It will miss spans sometimes, and de-identification regulations (HIPAA, GDPR) are standards a model cannot certify on its own — keep a human, or at least an ensemble with pattern matching, in the loop for anything real. English only. Not for re-identification of individuals.

How to Get Started

ollama pull huggingface.co/dylanmurzello/redax-8b:Q4_K_M

One quirk: output opens with an empty <think></think> block (Qwen3 training-template artifact). Strip it, then parse the JSON.

Training Details

53,141 schema-conditioned examples (clinical / financial / general PII), mixed from public corpora (Nemotron-PII, Gretel) plus targeted synthetic generation, deduped and 8-gram-decontaminated against the eval benchmark.

Evaluation

Benchmark rows (strict/relaxed span F1, hard-negative false positives, privacy leak rate) get added here once the eval suite has run — including the Q4_K_M vs Q8_0 quantization delta.

Environmental Impact

One evening on a single rented H100 (~2.5 GPU-hours). The whole fine-tune cost about as much as a burrito.