mirror of
https://github.com/ruvnet/RuVector.git
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76cec5641e
## Summary - Add PowerInfer-style sparse inference engine with precision lanes - Add memory module with QuantizedWeights and NeuronCache - Fix compilation and test issues - Demonstrated 2.9-8.7x speedup at typical sparsity levels - Published to crates.io as ruvector-sparse-inference v0.1.30 ## Key Features - Low-rank predictor using P·Q matrix factorization for fast neuron selection - Sparse FFN kernels that only compute active neurons - SIMD optimization for AVX2, SSE4.1, NEON, and WASM SIMD - GGUF parser with full quantization support (Q4_0 through Q6_K) - Precision lanes (3/5/7-bit layered quantization) - π integration for low-precision systems 🤖 Generated with [Claude Code](https://claude.com/claude-code)
53 lines
1.2 KiB
TOML
53 lines
1.2 KiB
TOML
[package]
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name = "ruvector-sparse-inference"
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version.workspace = true
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edition.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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rust-version.workspace = true
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description = "PowerInfer-style sparse inference engine for efficient neural network inference on edge devices"
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keywords = ["sparse-inference", "neural-network", "quantization", "simd", "edge-ai"]
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categories = ["science", "algorithms"]
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readme = "README.md"
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[dependencies]
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# Math and numerics
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ndarray = { version = "0.16", features = ["serde"] }
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rand = { workspace = true }
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rand_distr = { workspace = true }
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# Serialization
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serde = { workspace = true }
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serde_json = { workspace = true }
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rkyv = { workspace = true }
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# Error handling
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thiserror = { workspace = true }
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anyhow = { workspace = true }
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# Logging
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tracing = { workspace = true }
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# Performance
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rayon = { workspace = true }
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parking_lot = { workspace = true }
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# Memory mapping for model loading
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memmap2 = { workspace = true }
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# GGUF model loading support
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byteorder = "1.5"
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half = "2.4"
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[dev-dependencies]
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criterion = { workspace = true }
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proptest = { workspace = true }
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mockall = { workspace = true }
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[[bench]]
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name = "sparse_inference_bench"
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harness = false
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[lib]
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bench = false
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