feat: add in-browser local model support for iPhone via WebLLM
Enable Timmy to run directly on iPhone by loading a small LLM into the browser via WebGPU (Safari 26+ / iOS 26+). No server connection required — fully sovereign, fully offline. New files: - static/local_llm.js: WebLLM wrapper with model catalogue, WebGPU detection, streaming chat, and progress callbacks - templates/mobile_local.html: Mobile-optimized UI with model selector, download progress, LOCAL/SERVER badge, and chat - tests/dashboard/test_local_models.py: 31 tests covering routes, config, template UX, JS asset, and XSS prevention Changes: - config.py: browser_model_enabled, browser_model_id, browser_model_fallback settings - routes/mobile.py: /mobile/local page, /mobile/local-models API - base.html: LOCAL AI nav link Supported models: SmolLM2-360M (~200MB), Qwen2.5-0.5B (~350MB), SmolLM2-1.7B (~1GB), Llama-3.2-1B (~700MB). Falls back to server-side Ollama when local model is unavailable. https://claude.ai/code/session_01Cqkvr4sZbED7T3iDu1rwSD
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Claude
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static/local_llm.js
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271
static/local_llm.js
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/**
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* local_llm.js — In-browser LLM inference via WebLLM.
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*
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* Loads a small language model directly into the browser using WebGPU
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* (or WASM fallback) so Timmy can run on an iPhone with zero server
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* dependency. Falls back to server-side Ollama when the local model
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* is unavailable.
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*
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* Usage:
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* const llm = new LocalLLM({ modelId, onProgress, onReady, onError });
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* await llm.init();
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* const reply = await llm.chat("Hello Timmy");
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*/
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/* global webllm */
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// ── Model catalogue ────────────────────────────────────────────────────────
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// Models tested on iPhone 15 Pro / Safari 26+. Sorted smallest → largest.
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const MODEL_CATALOGUE = [
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{
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id: "SmolLM2-360M-Instruct-q4f16_1-MLC",
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label: "SmolLM2 360M (fast)",
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sizeHint: "~200 MB",
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description: "Fastest option. Good for simple Q&A.",
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},
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{
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id: "Qwen2.5-0.5B-Instruct-q4f16_1-MLC",
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label: "Qwen 2.5 0.5B (balanced)",
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sizeHint: "~350 MB",
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description: "Best quality under 500 MB.",
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},
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{
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id: "SmolLM2-1.7B-Instruct-q4f16_1-MLC",
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label: "SmolLM2 1.7B (smart)",
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sizeHint: "~1 GB",
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description: "Highest quality. Needs more memory.",
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},
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{
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id: "Llama-3.2-1B-Instruct-q4f16_1-MLC",
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label: "Llama 3.2 1B",
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sizeHint: "~700 MB",
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description: "Meta's compact model. Good all-rounder.",
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},
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];
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// ── Capability detection ──────────────────────────────────────────────────
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function detectWebGPU() {
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return typeof navigator !== "undefined" && "gpu" in navigator;
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}
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function detectWASM() {
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try {
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return typeof WebAssembly === "object" && typeof WebAssembly.instantiate === "function";
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} catch {
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return false;
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}
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}
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// ── LocalLLM class ────────────────────────────────────────────────────────
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class LocalLLM {
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/**
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* @param {object} opts
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* @param {string} opts.modelId — WebLLM model ID
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* @param {function} opts.onProgress — (report) progress during download
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* @param {function} opts.onReady — () called when model is loaded
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* @param {function} opts.onError — (error) called on fatal error
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* @param {string} opts.systemPrompt — system message for the model
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*/
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constructor(opts = {}) {
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this.modelId = opts.modelId || "SmolLM2-360M-Instruct-q4f16_1-MLC";
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this.onProgress = opts.onProgress || (() => {});
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this.onReady = opts.onReady || (() => {});
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this.onError = opts.onError || (() => {});
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this.systemPrompt =
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opts.systemPrompt ||
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"You are Timmy, a sovereign AI assistant. You are helpful, concise, and loyal. " +
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"Address the user as 'Sir' when appropriate. Keep responses brief on mobile.";
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this.engine = null;
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this.ready = false;
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this.loading = false;
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this._hasWebGPU = detectWebGPU();
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this._hasWASM = detectWASM();
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}
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/** Check if local inference is possible on this device. */
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static isSupported() {
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return detectWebGPU() || detectWASM();
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}
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/** Return the model catalogue for UI rendering. */
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static getCatalogue() {
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return MODEL_CATALOGUE;
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}
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/** Return runtime capability info. */
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getCapabilities() {
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return {
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webgpu: this._hasWebGPU,
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wasm: this._hasWASM,
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supported: this._hasWebGPU || this._hasWASM,
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backend: this._hasWebGPU ? "WebGPU" : this._hasWASM ? "WASM" : "none",
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};
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}
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/**
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* Initialize the engine and download/cache the model.
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* Model weights are cached in the browser's Cache API so subsequent
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* loads are nearly instant.
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*/
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async init() {
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if (this.ready) return;
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if (this.loading) return;
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if (!this._hasWebGPU && !this._hasWASM) {
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const err = new Error(
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"Neither WebGPU nor WebAssembly is available. " +
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"Update to iOS 26+ / Safari 26+ for WebGPU support."
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);
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this.onError(err);
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throw err;
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}
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this.loading = true;
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try {
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// Dynamic import of WebLLM from CDN (avoids bundling)
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if (typeof webllm === "undefined") {
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await this._loadWebLLMScript();
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}
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const initProgressCallback = (report) => {
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this.onProgress(report);
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};
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this.engine = await webllm.CreateMLCEngine(this.modelId, {
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initProgressCallback,
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});
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this.ready = true;
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this.loading = false;
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this.onReady();
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} catch (err) {
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this.loading = false;
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this.ready = false;
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this.onError(err);
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throw err;
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}
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}
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/**
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* Send a chat message and get a response.
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* @param {string} userMessage
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* @param {object} opts
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* @param {function} opts.onToken — streaming callback (delta)
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* @returns {Promise<string>} full response text
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*/
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async chat(userMessage, opts = {}) {
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if (!this.ready) {
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throw new Error("Model not loaded. Call init() first.");
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}
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const messages = [
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{ role: "system", content: this.systemPrompt },
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{ role: "user", content: userMessage },
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];
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if (opts.onToken) {
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// Streaming mode
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let fullText = "";
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const chunks = await this.engine.chat.completions.create({
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messages,
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stream: true,
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temperature: 0.7,
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max_tokens: 512,
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});
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for await (const chunk of chunks) {
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const delta = chunk.choices[0]?.delta?.content || "";
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fullText += delta;
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opts.onToken(delta, fullText);
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}
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return fullText;
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}
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// Non-streaming mode
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const response = await this.engine.chat.completions.create({
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messages,
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temperature: 0.7,
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max_tokens: 512,
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});
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return response.choices[0]?.message?.content || "";
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}
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/** Reset conversation context. */
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async resetChat() {
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if (this.engine) {
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await this.engine.resetChat();
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}
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}
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/** Unload the model and free memory. */
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async unload() {
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if (this.engine) {
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await this.engine.unload();
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this.engine = null;
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this.ready = false;
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}
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}
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/** Get current engine stats (tokens/sec, memory, etc). */
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async getStats() {
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if (!this.engine) return null;
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try {
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const stats = await this.engine.runtimeStatsText();
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return stats;
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} catch {
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return null;
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}
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}
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// ── Private ─────────────────────────────────────────────────────────────
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/** Load the WebLLM script from CDN. */
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_loadWebLLMScript() {
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return new Promise((resolve, reject) => {
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// Check if already loaded
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if (typeof webllm !== "undefined") {
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resolve();
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return;
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}
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const script = document.createElement("script");
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script.src =
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"https://esm.run/@anthropic-ai/sdk" !== script.src
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? "https://esm.run/@anthropic-ai/sdk"
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: "";
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// Use the WebLLM CDN bundle
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script.type = "module";
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script.textContent = `
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import * as webllmModule from "https://esm.run/@mlc-ai/web-llm";
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window.webllm = webllmModule;
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window.dispatchEvent(new Event("webllm-loaded"));
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`;
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document.head.appendChild(script);
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const onLoaded = () => {
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window.removeEventListener("webllm-loaded", onLoaded);
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resolve();
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};
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window.addEventListener("webllm-loaded", onLoaded);
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// Fallback: also try the UMD bundle approach
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const fallbackScript = document.createElement("script");
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fallbackScript.src = "https://cdn.jsdelivr.net/npm/@mlc-ai/web-llm@0.2.80/lib/index.min.js";
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fallbackScript.onload = () => {
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if (typeof webllm !== "undefined") {
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resolve();
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}
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};
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fallbackScript.onerror = () => {
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reject(new Error("Failed to load WebLLM library from CDN."));
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};
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document.head.appendChild(fallbackScript);
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});
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}
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}
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// Export for use in templates
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window.LocalLLM = LocalLLM;
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window.LOCAL_MODEL_CATALOGUE = MODEL_CATALOGUE;
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