mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-29 19:33:34 +00:00
7704c94624
Model weight decompilation:
- GGUF v2/v3 parser (self-contained, no ruvllm dep)
- Safetensors JSON header parser
- Architecture inference from tensor shapes (GQA, FFN, vocab)
- Tokenizer extraction, quantization detection
- Witness chain for model provenance
- 6 integration tests, behind `model` feature flag
API probing (live tested):
- Probes Claude, OpenAI, Gemini APIs without weight access
- Detects: streaming, tools, system_prompt, vision capabilities
- Measures: latency, tokens/sec, tokenizer type
- Model fingerprinting via self-identification + math tests
- Verified: Gemini 2.0 Flash (556ms, 46 tok/s, all caps detected)
CLI: npx ruvector decompile --model file.gguf
npx ruvector decompile --api gemini-2.0-flash
78 Rust tests passing.
Co-Authored-By: claude-flow <ruv@ruv.net>
50 lines
1.5 KiB
TOML
50 lines
1.5 KiB
TOML
[package]
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name = "ruvector-decompiler"
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version.workspace = true
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edition.workspace = true
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rust-version.workspace = true
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license.workspace = true
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authors.workspace = true
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repository.workspace = true
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description = "SOTA JavaScript bundle decompiler using MinCut graph partitioning, self-learning name inference, and RVF witness chains"
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keywords = ["decompiler", "javascript", "mincut", "source-map", "reverse-engineering"]
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categories = ["development-tools", "algorithms"]
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[dependencies]
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ruvector-mincut = { path = "../ruvector-mincut", default-features = false }
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sha3 = "0.10"
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serde = { workspace = true }
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serde_json = { workspace = true }
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regex = "1"
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thiserror = { workspace = true }
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once_cell = "1"
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rayon = { workspace = true, optional = true }
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memchr = "2"
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ort = { version = "=2.0.0-rc.10", optional = true, default-features = false, features = ["ndarray", "std"] }
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ndarray = { version = "0.16", optional = true }
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[features]
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default = ["parallel"]
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# Parallel Louvain via rayon (not available in WASM)
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parallel = ["rayon"]
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# WASM compatibility: pass through to ruvector-mincut wasm feature
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wasm = ["ruvector-mincut/wasm"]
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# Enable neural name inference using ONNX Runtime (via `ort` crate)
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# or a GGUF/RVF model file. Adds model loading + inference capability.
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neural = ["ort", "ndarray"]
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# LLM model weight decompilation (GGUF, Safetensors). See ADR-138.
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model = []
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[dev-dependencies]
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criterion = { version = "0.5", features = ["html_reports"] }
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[[bench]]
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name = "bench_parser"
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harness = false
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[[bench]]
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name = "bench_pipeline"
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harness = false
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[lib]
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crate-type = ["rlib"]
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