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09ee555 4289dff 09ee555 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | # 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.
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