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dylanmurzello
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Model Details
Model Provider
dylanmurzello
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
| file | what |
|---|---|
model.safetensors | bf16 reference weights |
redax-8b-Q4_K_M.gguf | 5 GB, runs on a laptop |
redax-8b-Q8_0.gguf | 8.7 GB, near-lossless |
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.
markdown
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.
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.
| method | full-parameter SFT (TRL 1.9, assistant-only loss) |
| epochs | 2 (824 steps, packed 2048 ctx, effective batch 32) |
| lr | 1e-5, cosine |
| precision | bf16 |
| final eval loss | 0.0185, no train/eval gap |
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.
One evening on a single rented H100 (~2.5 GPU-hours). The whole fine-tune cost about as much as a burrito.