MODIFICATIONS.md

Modifications from the upstream model

gliclass-std-base-v3-daecore-5facet-qint8-v2 is not an unmodified copy of knowledgator/gliclass-base-v3.0. It is not endorsed by Knowledgator or by Microsoft, whose deberta-v3-base backbone the upstream model builds on.

What changed:

• Fine-tune. The upstream GLiClass single-pass classifier was trained further on 59,886 labeled passages for five fixed facets (trap, decision, constraint, mechanism, procedure) with the five label prompts recorded in classifier-metadata.json. Labels were frontier-model judgments under a frozen protocol, not human annotations.
• Export. The fine-tuned weights were exported to ONNX (opset 17) as a single graph taking input_ids and attention_mask and emitting five logits.
• Quantization. Only the token-embedding Gather tables were quantized to signed INT8 (per-channel off, reduce-range off); every matrix product stays FP32. Twenty-four constant identity nodes were folded. The compression was qualified as equivalent to the FP32 export on a 5,298-row panel.
• Calibration. Per-facet temperature scaling and two frozen threshold tables (recall_leaning, contract) travel in classifier-metadata.json and are part of the artifact's identity.
• Vulkan graph preparation. Bounded integer/boolean mask calculations use exact FP32 equivalents before their original output types are restored. Learned parameters, tokenizers, label prompts, calibration and thresholds are unchanged. vulkan-derivation.json binds the source and derived graph; classifier-metadata.json records the new graph's byte identity.

Unchanged: the tokenizer vocabulary and the <<LABEL>> / <<SEP>> prompt convention of the upstream model.