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armiger

Text Classification
ONNX
torrent
metadata-classification
byte-transformer
int8
cpu-inference
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Align model cards and setup links to Armiger v3.2.1 (model bundle v3.0.0)
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metadata 
license: apache-2.0
library_name: onnx
pipeline_tag: text-classification
language:
  - en
  - ja
  - fr
  - de
  - es
  - it
  - ru
  - ko
  - zh
tags:
  - torrent
  - metadata-classification
  - byte-transformer
  - onnx
  - int8
  - cpu-inference

Armiger v3.2.1 (Model Bundle v3.0.0 / v3.0-gold-r5) — 15.7 MB INT8 ONNX CPU Classifier

Release: v3.2.1 · Model Version: 3.0.0 (v3.0-gold-r5) · Artifact: 15.7 MB (armiger.int8.onnx / armiger.onnx, per-channel asymmetric INT8 with FP32 embeddings) · CPU latency: 2.44 ms/item (326 items/s batch-64, M4 Pro CPU) · License: Apache-2.0

Armiger (NorviTech/armiger) is the CPU-only INT8 edition of the 9-head byte-level RoPE Transformer (8,295,882 parameters). It needs only numpy + onnxruntime and keeps 99.90% parity with the 32.5 MB FP32 model. Library and container release 3.2.1 uses model bundle 3.0.0. For FP32 weights and all 6 plugins (SwarmSurvivalNet, SwarmPriors, ZeroCueCatalog, AudioLanguageGuard, CalibrationAndDHT, ArmigerDHTDBSync), use NorviTech/armiger-ultra.

Neural Outputs & Benchmark Summary

  • 9 outputs, one CPU pass: category (11-class), adult, junk/malware, anime, english_subs, spoken lang, title_language, owner_scope, and per-byte span_bio title/year/episode tags via a 7-tag Viterbi mask (0 illegal transitions).
  • INT8 accuracy (all 3 suites):
    • Suite B LOTO N=1,025: 96.85% 6-dimension composite (95% CI [96.33%, 97.36%] with plugins) vs. Gemini Flash (94.56%), OpenAI GPT Fast Tier (93.49%, 5-dim; Junk N/A (Unprompted)), Claude Haiku 4.5 (92.88%).
    • W5A-Test N=703 (Rule-3 Gold): 97.01% 11-class accuracy (95% Wilson CI [95.47%, 98.04%]); 96.87% 6-core-head exact match.
    • R5-Test N=996 (7-key zero-leakage): 91.16% INT8 9-class accuracy (FP32 91.27%; 99.90% parity), 99.22% Title Language (n=511), 99.09% English Subtitles (n=110), 98.90% Owner Scope (n=996), 90.32% BIO Balanced Byte Accuracy (n=882), 85.84% all-heads exact match (n=996).

Suite B LOTO Overall & 6-Dimension Comparison

Quickstart (Standalone CPU ONNX Runtime)

Option A — Install the Python package (v3.2.1) with verified model download 

python -m pip install "armiger @ git+https://github.com/spencernorton/armiger.git@v3.2.1"
python -m armiger download --directory ./models-cpu --variant cpu

from armiger import ArmigerStandaloneONNX

model = ArmigerStandaloneONNX(
    onnx_path="./models-cpu/armiger.int8.onnx",
    vocab_path="./models-cpu/vocab.json",
    calibration_path="./models-cpu/calibration.json",
)
res = model.predict({
    "name": "Tears.of.Steel.2012.1080p.BluRay.x264-OPEN",
    "size": 8_500_000_000,
    "files": [{"path": "Tears.of.Steel.2012.mkv", "size": 8_500_000_000}],
})
print(res["heads"]["category"], res["matching_identity"], res["mondrian_gates"]["passed_all"])

Option B — Run directly from a checkout of this Hugging Face repository 

from armiger_inference import ArmigerStandaloneONNX

model = ArmigerStandaloneONNX()  # auto-loads armiger.onnx, vocab.json, calibration.json
res = model.predict({
    "name": "Tears.of.Steel.2012.1080p.BluRay.x264-OPEN",
    "size": 8_500_000_000,
    "files": [{"path": "Tears.of.Steel.2012.mkv", "size": 8_500_000_000}],
})
print(res["heads"]["category"], res["matching_identity"], res["mondrian_gates"]["passed_all"])

Ready-Made Docker Setups & Integrations (v3.2.1)