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TextCortex
/
laya-cybersec

Text Classification
Laya
ONNX
Safetensors
English
German
prompt-injection
data-exfiltration
llm-security
agent-security
system-one
multilingual
Model card Files
xet
 Community
laya-cybersec
File size: 3,011 Bytes
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{
  "type": "aggregate_saved_runtime_measurements",
  "comparison": "Different hardware, cohorts, windows and timing units; not a matched speed ranking or speedup measurement.",
  "quantiles": "NumPy median and linear-interpolated 0.95 quantile",
  "measurements": [
    {
      "model": "TextCortex/laya-cybersec",
      "checkpoint": "R2a",
      "subset": "single_window",
      "hardware": "Apple MPS (local Mac GPU)",
      "unit": "complete single-window input",
      "scope": "All 279 one-window inputs from the saved 1042-input run; sequential batch-one Laya API scoring, including encoding; no dedicated all-shape warmup. Excludes model loading and PDF extraction.",
      "source_sha256": "048522ef775ca1402dd37314cb3bb06800ec8e38829d118ab477d7b564b44f9b",
      "observations": 279,
      "p50_ms": 95.3577909967862,
      "p95_ms": 148.5410204855725
    },
    {
      "model": "TextCortex/laya-cybersec",
      "checkpoint": "R2a",
      "subset": "all_inputs",
      "hardware": "Apple MPS (local Mac GPU)",
      "unit": "complete saved input, including all windows",
      "scope": "All 1042 saved inputs: 723 clean PDF texts, 107 attack excerpts and 212 skills; 1500-character windows with 200-character overlap, scored sequentially. Excludes model loading and PDF extraction.",
      "source_sha256": "048522ef775ca1402dd37314cb3bb06800ec8e38829d118ab477d7b564b44f9b",
      "observations": 1042,
      "p50_ms": 730.2949790027924,
      "p95_ms": 4570.080316357779
    },
    {
      "model": "jev-latest",
      "subset": "pdf_chunk_requests",
      "hardware": "Hosted API",
      "unit": "successful chunk request",
      "scope": "10533 successful requests for 830 saved PDF inputs; includes network and provider time, excludes prior failed attempts and retry backoff. Concurrent request harness; not complete-document wall time. Provider revision unavailable.",
      "source_sha256": "4ecff2adb4f4bf57aff4cffd8fa316144d1d974d3cbe00ac2584336ca3bb4dcc",
      "observations": 10533,
      "p50_ms": 263.58416699804366,
      "p95_ms": 348.8895498017257
    },
    {
      "model": "TextCortex/clef-cybersecurity",
      "checkpoint": "validation-selected epoch 3, calibrated",
      "subset": "length_rank_sample",
      "hardware": "NVIDIA B200",
      "unit": "complete saved input, including all windows",
      "scope": "Complete saved text tokenization and model scoring; excludes loading, PDF extraction, network and queue time. All measured shapes warmed; batch one, accelerated FLA/causal-conv1d runtime. 64 uniformly spaced ranks by character length; independent of labels and scores",
      "documents": 64,
      "repetitions": 2,
      "peak_allocated_gib": 19.843493461608887,
      "source_sha256": "ced52ccc1ad8ef9c5a6342fa70adf37d3d28bb56151e8054f1125105355d19a8",
      "observations": 128,
      "p50_ms": 40.00461706891656,
      "p95_ms": 510.47315176110715
    }
  ],
  "privacy": "Aggregate values only; no customer text, identifiers or individual measurements."
}