rUv
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36f2599774
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feat(training): source map extraction + v2 model (83.67% val accuracy)
- Extract 14,198 training pairs from 6,941 source maps in node_modules
- Train v2 model (4-layer, 192-dim, 6-head transformer, 1.9M params)
- Val accuracy: 83.67% (up from 75.72%), exact match: 12.3% (up from 0.1%)
- Export weights.bin (7.3MB) for Rust runtime inference
- Add decompiler dashboard (React + Tailwind + Vite)
- Add runnable RVF (7,350 vectors, 49 segments, witness chain)
- Update evaluate-model.py to support configurable model architectures
- All 13 Rust tests pass, all 45 RVF files have valid SFVR headers
Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-04-03 04:57:47 +00:00 |
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rUv
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d5b3be56b8
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feat(decompiler): ONNX Runtime neural inference + 8,226 training pairs
Neural inference (behind `neural` feature flag):
- Full ONNX Runtime integration via `ort` crate
- Loads .onnx models, encodes context as byte tensors
- Softmax confidence scoring, character-level decoding
- Falls back to pattern-based when model unavailable
Training data expansion: 1,602 → 8,226 pairs
- 200+ function names, 90+ class names, 170+ variable names
- 16 minifier styles, 5 context variations per entry
- Extracted identifier dictionaries (381 lines)
Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-04-03 02:30:41 +00:00 |
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