ruvector/crates/ruvllm-wasm

@ruvector/ruvllm-wasm

Browser-compatible LLM inference runtime with WebAssembly. Semantic routing, adaptive learning, KV cache management, and chat template formatting — directly in the browser, no server required.

Features

• KV Cache Management — Two-tier cache (FP32 tail + u8 quantized store) for efficient token storage
• Memory Pooling — Arena allocator + buffer pool for minimal allocation overhead
• Chat Templates — Llama3, Mistral, Qwen, ChatML, Phi, Gemma format support
• HNSW Semantic Router — 150x faster pattern matching with bidirectional graph search
• MicroLoRA — Sub-millisecond model adaptation (rank 1-4)
• SONA Instant Learning — EMA quality tracking + adaptive rank adjustment
• Web Workers — Parallel inference with SharedArrayBuffer detection
• Full TypeScript — Complete .d.ts type definitions for all exports

Install

npm install @ruvector/ruvllm-wasm

Quick Start

import init, {
  RuvLLMWasm,
  ChatTemplateWasm,
  ChatMessageWasm,
  HnswRouterWasm,
  healthCheck
} from '@ruvector/ruvllm-wasm';

// Initialize WASM module
await init();

// Verify module loaded
console.log(healthCheck()); // true

// Format chat conversations
const template = ChatTemplateWasm.llama3();
const messages = [
  ChatMessageWasm.system("You are a helpful assistant."),
  ChatMessageWasm.user("What is WebAssembly?"),
];
const prompt = template.format(messages);

// Semantic routing with HNSW
const router = new HnswRouterWasm(384, 1000);
router.addPattern(new Float32Array(384).fill(0.1), "coder", "code tasks");
const result = router.route(new Float32Array(384).fill(0.1));
console.log(result.name, result.score); // "coder", 1.0

API

Core Types

Type	Description
RuvLLMWasm	Main inference engine with KV cache + buffer pool
GenerateConfig	Generation parameters (temperature, top_k, top_p, repetitionPenalty)
KvCacheWasm	Two-tier KV cache for token management
InferenceArenaWasm	O(1) bump allocator for inference temporaries
BufferPoolWasm	Pre-allocated buffer pool (1KB-256KB size classes)

Chat Templates

// Auto-detect from model ID
const template = ChatTemplateWasm.detectFromModelId("meta-llama/Llama-3-8B");
// Or use directly
const template = ChatTemplateWasm.mistral();
const prompt = template.format([
  ChatMessageWasm.system("You are helpful."),
  ChatMessageWasm.user("Hello!"),
]);

Supported: llama3(), mistral(), chatml(), phi(), gemma(), custom(name, pattern)

HNSW Semantic Router

const router = new HnswRouterWasm(384, 1000); // dimensions, max_patterns
router.addPattern(embedding, "agent-name", "metadata");
const result = router.route(queryEmbedding);
console.log(result.name, result.score);

// Persistence
const json = router.toJson();
const restored = HnswRouterWasm.fromJson(json);

MicroLoRA Adaptation

const config = new MicroLoraConfigWasm();
config.rank = 2;
config.inFeatures = 384;
config.outFeatures = 384;

const lora = new MicroLoraWasm(config);
const adapted = lora.apply(inputVector);
lora.adapt(new AdaptFeedbackWasm(0.9)); // quality score

SONA Instant Learning

const config = new SonaConfigWasm();
config.hiddenDim = 384;
const sona = new SonaInstantWasm(config);

const result = sona.instantAdapt(inputVector, 0.85); // quality
console.log(result.applied, result.qualityEma);

sona.recordPattern(embedding, "agent", true); // success pattern
const suggestion = sona.suggestAction(queryEmbedding);

Parallel Inference (Web Workers)

import { ParallelInference, feature_summary } from '@ruvector/ruvllm-wasm';

console.log(feature_summary()); // browser capability report

const engine = await new ParallelInference(4); // 4 workers
const result = await engine.matmul(a, b, m, n, k);
engine.terminate();

Build from Source

# Install prerequisites
rustup target add wasm32-unknown-unknown
cargo install wasm-pack

# Release build (workaround for Rust 1.91 codegen bug)
CARGO_PROFILE_RELEASE_CODEGEN_UNITS=256 CARGO_PROFILE_RELEASE_LTO=off \
  wasm-pack build crates/ruvllm-wasm --target web --scope ruvector --release

# Dev build
wasm-pack build crates/ruvllm-wasm --target web --scope ruvector --dev

# With WebGPU support
CARGO_PROFILE_RELEASE_CODEGEN_UNITS=256 CARGO_PROFILE_RELEASE_LTO=off \
  wasm-pack build crates/ruvllm-wasm --target web --scope ruvector --release -- --features webgpu

Browser Compatibility

Browser	Version	Notes
Chrome	57+	Full support
Edge	79+	Full support
Firefox	52+	Full support
Safari	11+	Full support

Optional enhancements:

• SharedArrayBuffer: Requires Cross-Origin-Opener-Policy: same-origin + Cross-Origin-Embedder-Policy: require-corp
• WebGPU: Available with webgpu feature flag (Chrome 113+)

Size

~435 KB release WASM (~178 KB gzipped)

Related

• @ruvector/ruvllm — Node.js LLM orchestration
• ruvector — Full RuVector CLI + MCP tools
• ADR-084 — Build documentation and known limitations

License

MIT