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Add @quantcpp/wasm npm package + ROADMAP realism update
ROADMAP: - Split Direction 2 into "production-ready" (uniform_4b) and "building blocks (research, not yet production-ready)" sections, reflecting the honest TurboQuant reproduction status from issue #14. wasm/ — npm package layout for @quantcpp/wasm: - package.json with proper metadata (Apache 2.0, keywords, repo) - index.mjs: ESM Quant class with create/loadModel/generate/free - index.js: CJS shim that lazy-imports the ESM module - index.d.ts: full TypeScript surface (KVType, GenerateOptions, etc.) - README.md: install + quick start + API reference + KV type table - .npmignore: keep build artifacts out of the published tarball The package wraps the existing 192KB quant.wasm so any web project can do `npm install @quantcpp/wasm` and run GGUF inference client-side. This is part of the embedded/edge moat — none of the other TurboQuant impls have a browser story. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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ROADMAP.md

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## Direction 2: KV Compression Research Platform
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The reference implementation for KV cache quantization research.
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A C reference engine for KV cache quantization research.
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### Done
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- [x] 7 quantization types (Polar, QJL, Turbo, Uniform, TurboKV)
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### Production-ready
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- [x] `uniform_4b` KV quantization (4–7x compression, +6.3% PPL on Llama 3.2 3B)
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- [x] `uniform_4b` + Q4 V combo (6.9x KV memory reduction)
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- [x] Delta compression (P-frame encoding)
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- [x] QK-norm aware compression
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- [x] QK-norm aware compression (Gemma 4 / hybrid attention models)
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- [x] Plugin architecture (3 functions to add new type)
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- [x] 34 unit tests
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### In Progress
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- [ ] "Add Your Own Type" tutorial (docs/custom-quantization.md)
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- [x] 35 unit tests
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### Building blocks (research, not yet production-ready)
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- [x] Random Hadamard Transform (`tq_rht.c`)
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- [x] Lloyd-Max-Gaussian codebook quantizer (`tq_codebook.c`)
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- [x] 1-bit QJL sign hash (`tq_qjl.c`)
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- [x] PolarQuant (polar coordinate) compression (`tq_polar.c`)
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- [x] `turbo_kv_*` types composing the building blocks (paper structure, gap in quality)
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### Open: TurboQuant paper reproduction
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- [ ] Close the gap on `turbo_kv_*` quality vs Google paper — see issue #14
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- [ ] Per-channel outlier handling (paper's 32-channel split)
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- [ ] QJL constant verification for Rademacher rows
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- [ ] Per-head rotation seeds
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- [ ] Regression test pinning `turbo_kv_4b` PPL on Llama 3.2 3B ≤ 14.5
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### Planned (after Direction 2 reproduction)
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- [ ] "Add Your Own Type" tutorial polish (docs/custom-quantization.md)
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- [ ] Arxiv tech report
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### Planned
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- [ ] llama.cpp KV type PR (ggml type registration)
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- [ ] llama.cpp KV type PR (ggml type registration) — only after paper reproduction works
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- [ ] vLLM KV compression plugin
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- [ ] Benchmarking suite (PPL across models × KV types)
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- [ ] Learned codebook quantization
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- [ ] Per-head adaptive bit allocation
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## Non-Goals
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wasm/.npmignore

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_headers
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build.sh
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quant_wasm.c
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test/
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.npmignore

wasm/README.md

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# @quantcpp/wasm
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> Single-header C LLM inference engine compiled to WebAssembly. **192 KB** binary. Runs GGUF models in your browser with KV cache compression.
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[![npm version](https://img.shields.io/npm/v/@quantcpp/wasm.svg)](https://www.npmjs.com/package/@quantcpp/wasm)
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[![Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
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## Install
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```bash
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npm install @quantcpp/wasm
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```
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## Quick start
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```html
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<script type="module">
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import { Quant } from '@quantcpp/wasm';
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const q = await Quant.create({
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scriptUrl: 'node_modules/@quantcpp/wasm/quant.js',
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modelUrl: 'https://huggingface.co/bartowski/SmolLM2-135M-Instruct-GGUF/resolve/main/SmolLM2-135M-Instruct-Q8_0.gguf',
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onStatus: (msg) => console.log('[quant]', msg),
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});
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await q.generate('The capital of France is', {
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maxTokens: 32,
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temperature: 0.0,
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onToken: (text) => document.body.append(text),
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onDone: ({ nTokens, elapsedMs }) => {
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console.log(`Generated ${nTokens} tokens in ${elapsedMs.toFixed(0)} ms`);
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},
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});
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q.free();
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</script>
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```
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## Why?
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- **192 KB binary.** The entire inference engine — tokenizer, transformer forward pass, KV cache compression — fits in less than most JPEGs.
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- **Zero server.** Models load and run entirely client-side. Nothing is uploaded.
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- **Real models.** Llama 3, Qwen 3.5, Gemma 3, SmolLM2, and any other GGUF model under your memory budget.
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- **KV compression built in.** Run 4–7× longer context than FP16 KV cache.
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- **One file at the source.** Powered by [`quant.h`](https://github.com/quantumaikr/quant.cpp), a 628 KB single-header C library you can drop into any project.
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## API
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See [`index.d.ts`](./index.d.ts) for the full TypeScript surface.
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```ts
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import { Quant } from '@quantcpp/wasm';
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const q = await Quant.create({
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scriptUrl: './quant.js', // path to the loaded WASM glue
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modelUrl: '/models/llama.gguf', // optional eager model load
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kvType: 'uniform_4b', // KV cache quantization
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vQuant: 'q4', // value cache quantization
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});
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await q.generate('Hello', {
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maxTokens: 64,
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temperature: 0.7,
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onToken: (text) => process.stdout.write(text),
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});
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q.free();
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```
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### Supported KV quantization types
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| Type | Bits/elem | Notes |
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|---|---|---|
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| `fp32` | 32 | baseline |
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| `uniform_4b`| 4 | recommended; +6.3% PPL on Llama 3.2 3B |
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| `uniform_2b` | 2 | maximum compression, lower quality |
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| `polar_3b` / `polar_4b` | 3 / 4 | PolarQuant-style |
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| `qjl_1b` | 1 | sign-hash baseline |
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| `turbo_kv_3b` / `turbo_kv_4b` | 3 / 4 | TurboQuant-structure (research; see [issue #14](https://github.com/quantumaikr/quant.cpp/issues/14)) |
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## Build from source
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```bash
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git clone https://github.com/quantumaikr/quant.cpp
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cd quant.cpp/wasm
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bash build.sh # requires emscripten (brew install emscripten)
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```
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Output: `quant.wasm` (192 KB) and `quant.js` (~30 KB glue).
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## License
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Apache 2.0. See [LICENSE](../LICENSE).
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## Citation
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If you use quant.cpp's KV compression building blocks in research, please cite the underlying papers:
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- [TurboQuant — Zandieh et al., ICLR 2026](https://arxiv.org/abs/2504.19874)
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- [PolarQuant — AISTATS 2026](https://arxiv.org/abs/2502.02617)
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- [QJL — AAAI 2025](https://arxiv.org/abs/2406.03482)

wasm/index.d.ts

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/**
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* Type definitions for @quantcpp/wasm
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*/
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export type KVType =
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| 'fp32'
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| 'uniform_4b'
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| 'uniform_2b'
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| 'uniform_3b'
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| 'polar_3b'
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| 'polar_4b'
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| 'qjl_1b'
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| 'turbo_3b'
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| 'turbo_4b'
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| 'turbo_kv_1b'
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| 'turbo_kv_3b'
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| 'turbo_kv_4b';
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export type VQuant = 'fp16' | 'q4' | 'q2';
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export interface QuantCreateOptions {
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/** URL to load quant.js from. Default: './quant.js' */
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scriptUrl?: string;
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/** Optional URL of a .gguf model to load on creation. */
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modelUrl?: string;
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/** KV cache quantization type. Default: 'uniform_4b'. */
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kvType?: KVType;
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/** Value cache quantization. Default: 'fp16'. */
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vQuant?: VQuant;
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/** Status callback for engine messages. */
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onStatus?: (message: string) => void;
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}
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export interface GenerateOptions {
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/** Maximum tokens to generate. Default: 128. */
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maxTokens?: number;
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/** Sampling temperature. Default: 0.7. */
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temperature?: number;
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/** Per-token callback for streaming. */
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onToken?: (text: string) => void;
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/** Called when generation completes. */
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onDone?: (nTokens: number, elapsedMs: number) => void;
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}
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export interface GenerateResult {
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nTokens: number;
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elapsedMs: number;
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}
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export class Quant {
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private constructor(module: unknown);
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/**
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* Create a Quant instance. If `modelUrl` is provided, the model is
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* loaded before the promise resolves.
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*/
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static create(options?: QuantCreateOptions): Promise<Quant>;
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/** Load a GGUF model from a URL. */
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loadModel(url: string): Promise<void>;
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/** Generate text from a prompt. Returns when generation completes. */
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generate(prompt: string, options?: GenerateOptions): Promise<GenerateResult>;
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/** Free model resources. */
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free(): void;
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/** Whether a model is currently loaded. */
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isReady(): boolean;
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}
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export default Quant;

wasm/index.js

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/**
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* @quantcpp/wasm — CommonJS entry point
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*
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* Re-exports the ESM module for CommonJS consumers.
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*/
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'use strict';
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let _esmPromise = null;
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function loadEsm() {
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if (!_esmPromise) {
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_esmPromise = import('./index.mjs');
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}
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return _esmPromise;
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}
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module.exports = {
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/**
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* Async-load the ESM Quant class.
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* Usage:
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* const { Quant } = await require('@quantcpp/wasm').load();
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*/
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load: async function () {
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return loadEsm();
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},
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};

wasm/index.mjs

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/**
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* @quantcpp/wasm — ESM entry point
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*
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* Single-header C LLM inference engine in your browser.
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*
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* Usage:
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*
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* import { Quant } from '@quantcpp/wasm';
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*
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* const q = await Quant.create({
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* modelUrl: '/models/SmolLM2-135M-Instruct-Q8_0.gguf',
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* kvType: 'uniform_4b',
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* vQuant: 'q4',
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* onStatus: (msg) => console.log('[status]', msg),
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* });
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*
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* await q.generate('Hello, my name is', {
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* maxTokens: 64,
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* temperature: 0.7,
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* onToken: (text) => process.stdout.write(text),
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* });
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*
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* q.free();
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*/
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let _modulePromise = null;
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function loadEmscriptenModule(scriptUrl) {
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if (_modulePromise) return _modulePromise;
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_modulePromise = new Promise((resolve, reject) => {
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if (typeof window === 'undefined') {
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reject(new Error('Node.js loader not implemented yet — use the browser build for now'));
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return;
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}
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const script = document.createElement('script');
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script.src = scriptUrl;
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script.onload = () => {
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// Emscripten modularize=0 attaches Module to globalThis
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if (typeof Module === 'undefined') {
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reject(new Error('quant.js loaded but Module is undefined'));
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return;
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}
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Module.onRuntimeInitialized = () => resolve(Module);
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};
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script.onerror = () => reject(new Error(`Failed to load ${scriptUrl}`));
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document.head.appendChild(script);
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});
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return _modulePromise;
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}
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export class Quant {
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constructor(module) {
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this._m = module;
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this._loaded = false;
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}
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/**
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* Create a Quant instance, optionally loading a model.
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* @param {object} opts
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* @param {string} [opts.scriptUrl='./quant.js']
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* @param {string} [opts.modelUrl] - URL to a .gguf model file
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* @param {string} [opts.kvType='uniform_4b'] - one of fp32, uniform_4b, turbo_kv_3b, ...
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* @param {string} [opts.vQuant='fp16'] - one of fp16, q4, q2
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* @param {function} [opts.onStatus] - status callback
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*/
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static async create(opts = {}) {
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const scriptUrl = opts.scriptUrl || './quant.js';
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const module = await loadEmscriptenModule(scriptUrl);
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if (opts.onStatus) module.onStatus = opts.onStatus;
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const q = new Quant(module);
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if (opts.modelUrl) {
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await q.loadModel(opts.modelUrl);
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}
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return q;
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}
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/**
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* Load a GGUF model from a URL into the WASM filesystem.
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*/
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async loadModel(url) {
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const resp = await fetch(url);
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if (!resp.ok) throw new Error(`Failed to fetch model: ${resp.status} ${resp.statusText}`);
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const buf = new Uint8Array(await resp.arrayBuffer());
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const path = '/model.gguf';
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this._m.FS.writeFile(path, buf);
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const ret = this._m.ccall('wasm_load_model', 'number', ['string'], [path]);
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if (ret !== 0) throw new Error(`wasm_load_model failed (rc=${ret})`);
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this._loaded = true;
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}
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/**
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* Generate text from a prompt.
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* @param {string} prompt
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* @param {object} opts
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* @param {number} [opts.maxTokens=128]
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* @param {number} [opts.temperature=0.7]
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* @param {function} [opts.onToken] - called per token with (text) string
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* @param {function} [opts.onDone] - called with (nTokens, elapsedMs)
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*/
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generate(prompt, opts = {}) {
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if (!this._loaded) throw new Error('No model loaded — call loadModel() first or pass modelUrl to create()');
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if (opts.onToken) this._m.onToken = opts.onToken;
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if (opts.onDone) this._m.onDone = opts.onDone;
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const maxTokens = opts.maxTokens ?? 128;
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const temperature = opts.temperature ?? 0.7;
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return new Promise((resolve) => {
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this._m.onDone = (nTokens, elapsedMs) => {
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if (opts.onDone) opts.onDone(nTokens, elapsedMs);
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resolve({ nTokens, elapsedMs });
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};
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this._m.ccall('wasm_generate', 'number', ['string', 'number', 'number'], [prompt, maxTokens, temperature]);
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});
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}
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/**
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* Free model resources. Call when done.
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*/
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free() {
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if (this._loaded) {
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this._m.ccall('wasm_free_model', null, [], []);
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this._loaded = false;
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}
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}
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isReady() {
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return this._loaded;
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}
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}
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export default Quant;

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