Views
No views yet
main now
carries the VOCABULARY-TRIMMED export: XLM-R-large's 250,002-piece vocabulary reduced to the 53,751
pieces the EN+FR training corpus actually used (plus every byte-level/single-character fallback
piece), tools/guard-train/trim_vocab.py. Same checkpoint, same weights on every kept row — this
is row-selection on the embedding matrix, not a retrain, and measured byte-identical to the
untrimmed export's masking decisions across a 259-document evaluation corpus.model.onnx
(under the 2 GB protobuf limit, so no companion model.onnx_data any more).09451885cc53531ecd28aa3bbc1be7b7986ffb9e if you need the full 250,002-piece vocabulary.guard-ft-v19-trimmed-fp16
(CUDA-only, ~716 MB) and guard-ft-v19-trimmed-int8
(CPU-only, ~360 MB, measurable precision trade-off).