tasq/node_modules/ruvector-attention-wasm/package.json

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{
"name": "ruvector-attention-wasm",
"type": "module",
"version": "0.1.32",
"description": "High-performance WebAssembly attention mechanisms for transformers and LLMs: Multi-Head, Flash Attention, Hyperbolic, Linear (Performer), MoE, Local-Global, and CGT Sheaf Attention with coherence gating. GPU-accelerated with SIMD fallback.",
"license": "MIT OR Apache-2.0",
"author": "RuVector Team <team@ruvector.dev>",
"repository": {
"type": "git",
"url": "git+https://github.com/ruvnet/ruvector.git"
},
"homepage": "https://ruv.io/ruvector",
"bugs": {
"url": "https://github.com/ruvnet/ruvector/issues"
},
"main": "ruvector_attention_wasm.js",
"module": "ruvector_attention_wasm.js",
"types": "ruvector_attention_wasm.d.ts",
"files": [
"ruvector_attention_wasm_bg.wasm",
"ruvector_attention_wasm.js",
"ruvector_attention_wasm.d.ts",
"ruvector_attention_wasm_bg.wasm.d.ts",
"README.md"
],
"sideEffects": [
"./snippets/*"
],
"keywords": [
"wasm",
"webassembly",
"attention",
"transformer",
"llm",
"machine-learning",
"neural-networks",
"multi-head-attention",
"flash-attention",
"hyperbolic",
"moe",
"mixture-of-experts",
"coherence",
"cgt",
"sheaf-attention",
"ai",
"deep-learning",
"gpu",
"simd",
"infonce",
"contrastive-learning",
"performer",
"linear-attention"
],
"engines": {
"node": ">=16.0.0"
},
"publishConfig": {
"access": "public"
}
}