# 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.