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LLM inference for the browser and Node.js — text, vision, speech (TTS/STT), and embeddings on a native WebGPU engine. Offline, private, no API keys. React hooks, MCP, and Vercel AI SDK included.","maintainers":[{"name":"eyaltoledano","email":"eutait@gmail.com"},{"name":"crunchyman-ralph","email":"rkhreish@gmail.com"}],"readme":"<p align=\"center\">\n  <img src=\"https://em-content.zobj.net/source/twitter/408/hamster_1f439.png\" width=\"80\" />\n</p>\n\n<h1 align=\"center\">Gerbil</h1>\n\n<p align=\"center\">\n  <strong>On-device LLM inference for the browser and Node. Text, vision, embeddings, and speech — all on the GPU via WebGPU/WGSL, private and offline.</strong>\n</p>\n\n<p align=\"center\">\n  <a href=\"https://gerbilsdk.com\"><strong>gerbilsdk.com</strong></a> •\n  <a href=\"#install\">Install</a> •\n  <a href=\"#native-webgpu-engine\">Engine</a> •\n  <a href=\"#react-quickstart\">React</a> •\n  <a href=\"#embeddings\">Embeddings</a> •\n  <a href=\"#vision\">Vision</a> •\n  <a href=\"#speech\">Speech</a> •\n  <a href=\"https://gerbilsdk.com/docs/frameworks/ai-sdk\">AI SDK</a> •\n  <a href=\"https://gerbilsdk.com/docs/cli\">CLI</a>\n</p>\n\n<p align=\"center\">\n  <img src=\"https://img.shields.io/npm/v/@tryhamster/gerbil?style=flat-square\" alt=\"npm version\" />\n  <img src=\"https://img.shields.io/badge/license-MIT-blue?style=flat-square\" alt=\"license\" />\n  <img src=\"https://img.shields.io/badge/node-%3E%3D18-green?style=flat-square\" alt=\"node version\" />\n</p>\n\n<p align=\"center\">\n  <img src=\"https://img.shields.io/badge/AI_SDK-black?style=flat-square&logo=vercel\" alt=\"AI SDK\" />\n  <img src=\"https://img.shields.io/badge/Next.js-black?style=flat-square&logo=next.js\" alt=\"Next.js\" />\n  <img src=\"https://img.shields.io/badge/Express-black?style=flat-square&logo=express\" alt=\"Express\" />\n  <img src=\"https://img.shields.io/badge/React-black?style=flat-square&logo=react\" alt=\"React\" />\n  <img src=\"https://img.shields.io/badge/LangChain-black?style=flat-square\" alt=\"LangChain\" />\n  <img src=\"https://img.shields.io/badge/Hono-black?style=flat-square\" alt=\"Hono\" />\n</p>\n\n---\n\n```typescript\nimport { WebGPUEngine } from \"@tryhamster/gerbil/gpu\";\n\nconst engine = await WebGPUEngine.create({ repo: \"mlx-community/Qwen3.5-0.8B-4bit\" });\nconst { text } = await engine.generate(\"Explain recursion in one sentence\");\n```\n\n> **📚 Full docs → [gerbilsdk.com/docs](https://gerbilsdk.com/docs)** · live playground & demos at **[gerbilsdk.com](https://gerbilsdk.com)**\n\n## Why Gerbil?\n\n- **One native engine** — pure WebGPU/WGSL compute shaders, nothing extra to ship.\n- **~90 KB gzipped** — the _entire_ multimodal engine. No heavyweight ML runtime; model\n  weights stream from the HuggingFace Hub at run time.\n- **Multimodal, all native** — text, vision (image→text), embeddings, and speech run on the\n  same engine, loading safetensors directly from the HuggingFace Hub.\n- **Browser & Node** — Chrome 113+, Safari 26+ (iOS 26+), Firefox 141+, and Node via Dawn\n  (`webgpu` npm), anywhere there's a real GPU.\n- **Local & private** — no API keys, nothing leaves the device.\n- **React-first** — `useEngine` owns load / unload / hot-swap and shares one engine\n  across components (reference-counted), with `dtype: \"auto\"` picking int4 on mobile.\n- **Framework ready** — Vercel AI SDK v5, Next.js, Express, LangChain adapters.\n- **Skills & tools** — built-in + custom skills with Zod validation; agentic tool calling.\n\n## Install\n\n```bash\n# Try without installing (one-off usage)\nnpx @tryhamster/gerbil\n\n# Install globally\nnpm install -g @tryhamster/gerbil\n\n# Or install in your project\nnpm install @tryhamster/gerbil\n```\n\nAfter global install, use `gerbil` directly instead of `npx @tryhamster/gerbil`.\n\n## Native WebGPU Engine\n\nGerbil runs LLMs directly on the GPU — pure WebGPU/WGSL compute, no Python, no native\nruntime, nothing to install. The same code runs in a browser tab and in Node (via Dawn),\nstreaming weights straight from the HuggingFace Hub and loading only the tensors a request\nneeds — skip the vision tower you don't.\n\n```typescript\nimport { WebGPUEngine } from \"@tryhamster/gerbil/gpu\";\n\n// dtype \"auto\" picks int4 on mobile, the repo's native precision on desktop.\nconst engine = await WebGPUEngine.create({\n  repo: \"mlx-community/Qwen3.5-0.8B-4bit\",\n  dtype: \"auto\",\n});\n\n// Generate\nconst { text, tokensPerSecond } = await engine.generate(\"Write a haiku about gerbils\");\nconsole.log(text, `(${tokensPerSecond.toFixed(1)} tok/s)`);\n\n// Stream\nfor await (const token of engine.stream(\"Tell me a story\")) {\n  process.stdout.write(token);\n}\n\nengine.destroy();\n```\n\n`WebGPUEngine.create({ repo, dtype, enableVision, embedding, maxSeqLen })` returns an\nengine with `generate`, `stream`, `describeImage`, `embed`, and `speak`. See the\n[native engine docs](#supported-models) below for the model lineup.\n\n### Instant starts\n\nWarm loads skip quantization entirely: after the first load of a model, Gerbil caches\nthe ready-to-run INT4 tensors, so the next load reads them straight into the GPU\n(a warm Qwen3.5-0.8B load drops from ~8s to ~0.4s on an M4 Max). Pair that with the\nshared engine and a preload, and the first generate is effectively instant:\n\n```typescript\nimport { getEngine, preloadEngine } from \"@tryhamster/gerbil\";\n\npreloadEngine(); // start loading at app startup (safe to fire and forget)\n\n// later, anywhere in your app: same options, same engine, already warm\nconst engine = await getEngine();\nconst { text } = await engine.generate(\"hi\");\n```\n\n`getEngine(options)` returns one shared engine per config for the whole process or\npage, coalescing concurrent loads, so nothing ever loads the same model twice. Call\n`engine.dispose()` to release it. In React, `preloadEngine` from\n`@tryhamster/gerbil/hooks` warms the same registry `useEngine` reads from.\n\n### Benchmark it\n\nThe same engine runs server-side on Node (via Dawn), so you can watch local decode\nrip on your own GPU — no API key, no cloud:\n\n```bash\nnpx @tryhamster/gerbil bench           # tok/s + first-token latency on your machine\n```\n\nReports steady-state decode tok/s, time-to-first-token, and the device it ran on.\nFor a copy-pasteable version see [`examples/benchmark.ts`](./examples/benchmark.ts)\n(and the rest of [`examples/`](./examples/)).\n\n### Batched decode & per-lane adapters (Node/Dawn)\n\nOn Node the engine can decode many sequences in one lockstep batch — and each lane\ncan run a **different LoRA adapter** over one shared base (SGMV-style). Both are\ndefault-on and token-exact: calling `generateBatch`, `createScheduler`, or\n`createSwarm` enables the batch machinery automatically, and every lane's output\nmatches the same prompt run solo. No env vars needed.\n\n```typescript\nconst engine = await WebGPUEngine.create({ repo: \"mlx-community/Qwen3.5-0.8B-4bit\" });\n\n// Register adapters once — their factors are packed into shared GPU buffers,\n// and any number of lanes can run them concurrently.\nawait engine.registerAdapter(\"pirate\", \"you/qwen3.5-0.8b-pirate-lora\");\nawait engine.registerAdapter(\"legal\", \"you/qwen3.5-0.8b-legal-lora\");\n\nconst results = await engine.generateBatch(\n  [promptA, promptB, promptC, promptD],\n  {\n    sampling: { temperature: 0 },\n    // Per-lane adapter selection: null = bare base model.\n    adapters: [\"pirate\", \"pirate\", \"legal\", null],\n  },\n);\n\n// The continuous-batching scheduler takes an adapter per request — mixed\n// adapters, queueing, and slot reuse stay token-exact vs solo runs:\nconst scheduler = engine.createScheduler();\nconst reply = await scheduler.submit(prompt, {\n  sampling: { temperature: 0 },\n  adapter: \"legal\",\n});\n\n// And a swarm fans ONE input out across tuned members concurrently (MoTA\n// mode B — one batched pass, default plan broadcasts, default reduce returns\n// a member-keyed map):\nconst swarm = await engine.createSwarm({\n  members: { pirate: \"you/pirate-lora\", legal: \"you/legal-lora\", base: null },\n});\nconst answers = await swarm.run(prompt, { sampling: { temperature: 0 } });\n```\n\n| Env flag | Effect |\n|----------|--------|\n| `GERBIL_BATCH=N` | Override the lockstep lane count (default: prompt count for `generateBatch`, 4 for the scheduler/swarm). `=0` disables the batch path |\n| `GERBIL_BATCH_LORA=0` | Disable per-lane adapters (`registerAdapter`, the `adapters` option, per-request `adapter` on `scheduler.submit`, `createSwarm`) |\n| `GERBIL_GEMM_FAST=1` | Opt-in relaxed-parity f16-accumulate GEMM tier (faster aggregate; CHANGES OUTPUTS — never on by default) |\n| `GERBIL_DECODE_WINDOW=K` | Opt-in: records K decode steps per GPU submit (2–8; Vulkan-targeted, flat on Metal) |\n\n### Swarm quickstart\n\nA swarm is a set of named specialists: LoRA adapters tuned on one shared base\nmodel, plus optionally the bare base itself. All members decode together in one\nbatched pass on one GPU, so asking four specialists costs about as much as\nasking the slowest one, not four times as much.\n\nTry it with the runnable example (Node with the native GPU path):\n\n```bash\nnode scripts/engine/example-swarm.mjs\n```\n\nWith no configuration it finds tuned adapters in your local caches, or falls\nback to generated demo adapters so you can see the mechanics either way. Point\nit at your own tuned adapters like this:\n\n```bash\nGERBIL_SWARM_MEMBERS=\"support=hf:you/support-lora,legal=hf:you/legal-lora\" \\\n  node scripts/engine/example-swarm.mjs\n```\n\nThe example shows both patterns: routing different prompts to different\nmembers, and broadcasting one input to every member with `swarm.run()`.\nBatched adapter decode is default-on — `createSwarm()` enables it on first\nuse, so your own scripts need no env vars either.\n\n## React Quickstart\n\n`useEngine` (from `@tryhamster/gerbil/hooks`) owns the full engine lifecycle —\nload, unload, hot-swap on config change, and reference-counted sharing so multiple\ncomponents never upload the same weights to the GPU twice.\n\n```tsx\nimport { useEngine } from \"@tryhamster/gerbil/hooks\";\n\nfunction Chat() {\n  const { complete, completion, isLoading, isGenerating, tps } = useEngine({\n    model: \"mlx-community/Qwen3.5-0.8B-4bit\",\n    autoLoad: true, // dtype defaults to \"auto\": int4 on mobile, native on desktop\n  });\n\n  if (isLoading) return <div>Loading model…</div>;\n  return (\n    <div>\n      <button onClick={() => complete(\"What is 2+2?\")} disabled={isGenerating}>\n        Generate\n      </button>\n      <p>{completion}</p>\n      {isGenerating && <span>{tps?.toFixed(1)} tok/s</span>}\n    </div>\n  );\n}\n```\n\nThe same hook exposes `describeImage` (vision), `embed`/`similarity` (embeddings), `stop`,\nand `dispose`. Pass `enableVision: true` or `embedding: true` to load those modalities.\n\nThe engine stays warm across route changes and remounts by default, so navigating away\nand back never re-uploads weights (set `keepAlive: false` to tear down when the last\nconsumer unmounts). To have the model ready before any component mounts, call\n`preloadEngine()` from `@tryhamster/gerbil/hooks` in your app shell or on hover; the\nnext `useEngine` with the same config attaches to the already-loading engine.\n\n**Gate your app behind the download.** Wrap your app in `<GerbilGate>` to show a splash\nuntil the engine is ready — and keep it warm across navigation (no GPU re-upload):\n\n```tsx\nimport { GerbilGate } from \"@tryhamster/gerbil/hooks\";\n\n<GerbilGate\n  model=\"mlx-community/Qwen3.5-0.8B-4bit\"\n  fallback={({ progress }) => <Splash percent={progress} />}\n>\n  <App />\n</GerbilGate>;\n```\n\n## Structured Output\n\n`generateObject` makes the model return a JSON object: it generates, extracts the JSON,\nvalidates it, and retries with a corrective nudge until it's valid (or `maxRetries` is hit).\nValidate with a predicate `(o) => boolean` or a minimal `{ required: [...] }` schema; omit\n`schema` to accept any valid JSON.\n\n```typescript\nimport { generateObject } from \"@tryhamster/gerbil\";\n\nconst { object, attempts } = await generateObject<{ name: string; age: number }>(\n  'Extract {name, age} from: \"I am Sarah, 28\"',\n  { schema: { required: [\"name\", \"age\"] } },\n);\n// object === { name: \"Sarah\", age: 28 }\n```\n\nIt's available on the engine, the `Gerbil` class, and the one-liner API:\n\n```typescript\nimport { Gerbil, WebGPUEngine } from \"@tryhamster/gerbil\";\n\nconst g = new Gerbil();\nawait g.loadModel(\"qwen3.5-0.8b\");\nconst { object } = await g.generateObject(\"List 3 primes as {primes: number[]}\", {\n  schema: (o) => Array.isArray((o as any).primes),\n});\n\n// Or directly on the engine:\nconst engine = await WebGPUEngine.create({ repo: \"mlx-community/Qwen3.5-0.8B-4bit\" });\nawait engine.generateObject(\"…\", { schema: { required: [\"title\"] } });\n```\n\nIn React, use `useObject` (from `@tryhamster/gerbil/hooks`):\n\n```tsx\nimport { useObject } from \"@tryhamster/gerbil/hooks\";\n\nconst { generate, object, isGenerating } = useObject<{ city: string }>();\nawait generate(\"Extract the city from: I live in Paris\", {\n  schema: { required: [\"city\"] },\n});\n```\n\nFrom the CLI:\n\n```bash\ngerbil object \"Extract {name, age}: I am Sarah, 28\" --schema person.json\n# person.json: { \"required\": [\"name\", \"age\"] }\n```\n\n## Embeddings\n\nNative text embeddings via **EmbeddingGemma-300M** (mean-pooled Gemma3 encoder + Dense\nhead, 768-dim, L2-normalized). EmbeddingGemma is asymmetric — pass `taskType` so queries\nand documents get the right prefix.\n\n```typescript\nimport { WebGPUEngine } from \"@tryhamster/gerbil/gpu\";\n\nconst engine = await WebGPUEngine.create({\n  repo: \"mlx-community/embeddinggemma-300m-4bit\",\n  embedding: true,\n});\n\nconst query = await engine.embed(\"capital of France\", { taskType: \"query\" });\nconst doc = await engine.embed(\"Paris is the capital of France\", { taskType: \"document\" });\n\n// Vectors are unit-norm, so cosine similarity is a dot product.\nconst sim = query.reduce((s, v, i) => s + v * doc[i], 0);\n```\n\n📖 **[Full Embeddings Documentation →](https://gerbilsdk.com/docs)**\n\n## Vision\n\nImage-in → text-out via the native vision towers (Qwen3.5 ViT and Gemma 4 ViT). Load with\n`enableVision: true`, then call `describeImage`.\n\n```typescript\nconst engine = await WebGPUEngine.create({\n  repo: \"Qwen/Qwen3.5-0.8B\",\n  enableVision: true,\n});\n\n// In Node, decode the image to RGB pixels (HWC, 0..255) yourself; in the browser the\n// React hook's describeImage() takes a URL / data-URL directly.\nconst { text } = await engine.describeImage(\n  { pixels, width, height },\n  \"What's in this image?\",\n);\n```\n\n📖 **[Full Vision Documentation →](https://gerbilsdk.com/docs/vision)**\n\n## Speech\n\n**Text-to-speech** — native **Kani-TTS-2** (LFM2-350M codec-LM + NVIDIA NeMo NanoCodec).\n`engine.speak()` returns 22.05 kHz mono PCM.\n\n```typescript\nconst engine = await WebGPUEngine.create({ repo: \"nineninesix/kani-tts-2-en\" });\nconst { pcm, sampleRate } = await engine.speak(\"Hello, I'm Gerbil!\"); // sampleRate === 22050\n```\n\n**Streaming TTS** — native **Gepard-1.0** (Apache-2.0). Gepard is a stock Qwen3.5\ncodec-LM with 32 parallel FSQ classifier heads that emit one whole audio frame per\nforward pass, so speech is frame synchronous: audio streams out chunk by chunk while\nthe model is still generating. Pass `onChunk` to receive PCM as it is produced.\n\n```typescript\nconst engine = await WebGPUEngine.create({ repo: \"nineninesix/gepard-1.0\" });\nconst { pcm, sampleRate } = await engine.speak(\"Hello from Gepard.\", {\n  temperature: 0.4,\n  onChunk: ({ pcm, frameIndex, isFinal }) => {\n    // play or buffer each ~1.5s chunk as soon as it is decoded\n  },\n}); // sampleRate === 22050\n```\n\nRun the end to end example and write a .wav:\n\n```bash\nnode scripts/engine/example-gepard-tts.mjs \"The quick brown fox jumps over the lazy dog.\"\n```\n\nVoice cloning (the reference speaker compressor) is present in the checkpoint but not\nwired yet, so Gepard ships a single default voice for now.\n\n**Speech-to-text** — native **Moonshine** (raw-waveform encoder/decoder, no FFT/log-mel)\nvia the dedicated `MoonshineSTT` class.\n\n```typescript\nimport { MoonshineSTT } from \"@tryhamster/gerbil/gpu\";\n\nconst stt = await MoonshineSTT.create({ repo: \"UsefulSensors/moonshine-base\" });\nconst { text, noSpeech } = await stt.transcribe(pcm16kMono); // noSpeech flags silence\n```\n\n`transcribe` returns `noSpeech` (RMS VAD + min-duration + marker denylist) so you can skip\nsilent/empty clips; `useSTT` surfaces it too, with an `onNoSpeech` callback.\n\n📖 **[Full TTS Documentation →](https://gerbilsdk.com/docs/tts)** · **[Full STT Documentation →](https://gerbilsdk.com/docs/stt)**\n\n## Skills\n\nBuilt-in AI skills with Zod-validated inputs:\n\n```typescript\nimport { commit, summarize, explain, review } from \"@tryhamster/gerbil/skills\";\n\nconst msg = await commit({ type: \"conventional\" });\nconst summary = await summarize({ content: doc, length: \"short\" });\nconst explanation = await explain({ content: code, level: \"beginner\" });\n```\n\n### Custom Skills\n\n```typescript\nimport { defineSkill, loadSkills, useSkill } from \"@tryhamster/gerbil/skills\";\n\n// Define inline\nconst sentiment = defineSkill({\n  name: \"sentiment\",\n  description: \"Analyze text sentiment\",\n  input: z.object({ text: z.string() }),\n  async run(input, gerbil) {\n    return gerbil.json(`Sentiment of: ${input.text}`, { schema: outputSchema });\n  },\n});\n\n// Or load from files\nawait loadSkills(\"./skills\");  // loads *.skill.ts\nconst skill = useSkill(\"my-skill\");\n```\n\n📖 **[Full Skills Documentation →](https://gerbilsdk.com/docs/skills)**\n\n## Tools & Agents\n\nGerbil supports tool calling with Qwen3 models for agentic workflows. A tool is a\nplain object (`AgentTool`) — give it a `name`, `description`, optional `parameters`,\nand an `execute` function:\n\n```typescript\nimport type { AgentTool } from \"@tryhamster/gerbil/gpu\";\n\nconst weatherTool: AgentTool = {\n  name: \"get_weather\",\n  description: \"Get weather for a city\",\n  parameters: { city: \"string\" },\n  execute: ({ city }) => `Weather in ${city}: 72°F, sunny`,\n};\n```\n\n**Agentic loop, on-device.** `engine.generateWithTools` (and the `useAgent` React hook)\nrun the whole loop — generate → call a tool → feed the result back → repeat — and return a\nstep trace for UIs:\n\n```tsx\nimport { useAgent } from \"@tryhamster/gerbil/hooks\";\n\nconst { run, steps, answer, isRunning } = useAgent({\n  model: \"mlx-community/Qwen3.5-0.8B-4bit\",\n  tools: [\n    {\n      name: \"get_weather\",\n      description: \"Get the weather for a city\",\n      parameters: { city: \"string\" },\n      execute: ({ city }) => `Weather in ${city}: 72°F, sunny`,\n    },\n  ],\n});\nawait run(\"What's the weather in Paris?\"); // steps[]: tool_call → tool_result → answer\n```\n\n**Built-in tools:**\n- `gerbil_docs` — Search Gerbil documentation\n- `run_skill` — Execute any Gerbil skill\n\nIn the REPL, **Agent mode** is on by default and enables tool calling:\n\n```bash\nnpx @tryhamster/gerbil repl\n# Press ⌘A to toggle agent mode on/off\n# Ask: \"how do I use gerbil with next.js?\"\n# Gerbil will call the docs tool and synthesize an answer\n```\n\n📖 **[Full Tools Documentation →](https://gerbilsdk.com/docs/tools)**\n\n## Autocomplete & Rewrite\n\nInline autocomplete — `engine.autocomplete(prefix)` and the debounced `useAutocomplete`\nhook return a brief single-line continuation (low-latency defaults + cleanup):\n\n```tsx\nimport { useAutocomplete } from \"@tryhamster/gerbil/hooks\";\n\nconst { suggestion, onInput, accept, dismiss } = useAutocomplete({\n  model: \"mlx-community/Qwen3.5-0.8B-4bit\",\n});\n// <input onChange={(e) => onInput(e.target.value)} /> — render `suggestion` as ghost text;\n// Tab → accept(), Esc → dismiss()\n```\n\nTone rewrite — `engine.rewrite(text, { tone })` (and `useEngine().rewrite`) re-generates\ntext in a target tone (`\"professional\"`, `\"friendly\"`, `\"concise\"`, `\"playful\"`,\n`\"pirate\"`) or with free-form `instructions`.\n\n📖 **[Full Autocomplete Documentation →](https://gerbilsdk.com/docs/autocomplete)**\n\n## CLI\n\n```bash\n# Without installing (use npx)\nnpx @tryhamster/gerbil                        # Interactive REPL (default)\nnpx @tryhamster/gerbil \"Write a haiku\"        # Generate text\n\n# After installing globally (npm i -g @tryhamster/gerbil)\ngerbil                                        # Interactive REPL\ngerbil \"Write a haiku\"                        # Generate text\ngerbil commit                                 # Commit message from staged changes\ngerbil summarize README.md                    # Summarize file\ngerbil chat --thinking                        # Interactive chat\ngerbil speak \"Hello world\"                    # Text-to-speech (Kani-TTS-2)\ngerbil transcribe audio.wav                   # Speech-to-text\ngerbil serve --mcp                            # MCP server for Claude/Cursor\ngerbil update                                 # Update to latest version\n```\n\n> **Updates**: Gerbil checks for updates but never installs without permission. Press `u` in REPL or run `gerbil update`.\n\n📖 **[Full CLI Documentation →](https://gerbilsdk.com/docs/cli)**\n\n## Browser Usage\n\nRun LLMs directly in the browser with WebGPU — no server required. The React hooks\nlive at `@tryhamster/gerbil/hooks` and run pure WebGPU compute:\n\n```tsx\nimport { useChat } from \"@tryhamster/gerbil/hooks\";\n\nfunction Chat() {\n  const { messages, send, isLoading, isGenerating } = useChat();\n\n  if (isLoading) return <div>Loading model...</div>;\n\n  return (\n    <div>\n      {messages.map((m, i) => <div key={i}>{m.role}: {m.content}</div>)}\n      <button onClick={() => send(\"Hello!\")} disabled={isGenerating}>Send</button>\n    </div>\n  );\n}\n```\n\n`@tryhamster/gerbil/browser` exports the device & storage utilities\n(`isModelSafeForDevice`, `detectMemoryCrash`, `downloadModelChunked`,\n`getRecommendedModels`, `requestPersistentStorage`, …).\n\n📖 **[Full Browser Documentation →](https://gerbilsdk.com/docs/browser)**\n\n## Integrations\n\n| Integration | Import | Docs |\n|-------------|--------|------|\n| **Browser** | `@tryhamster/gerbil/browser` | [📖 Browser](https://gerbilsdk.com/docs/browser) |\n| **AI SDK v5** | `@tryhamster/gerbil/ai` | [📖 AI SDK](https://gerbilsdk.com/docs/frameworks/ai-sdk) |\n| **Next.js** | `@tryhamster/gerbil/next` | [📖 Next.js](https://gerbilsdk.com/docs/frameworks/nextjs) |\n| **Express** | `@tryhamster/gerbil/express` | [📖 Express](https://gerbilsdk.com/docs/frameworks/express) |\n| **LangChain** | `@tryhamster/gerbil/langchain` | [📖 LangChain](https://gerbilsdk.com/docs/frameworks/langchain) |\n| **MCP Server** | `npx @tryhamster/gerbil serve --mcp` | [📖 MCP](https://gerbilsdk.com/docs/mcp) |\n\n**Native engine:** `import { WebGPUEngine } from \"@tryhamster/gerbil/gpu\"` (or `useEngine` from `@tryhamster/gerbil/hooks` for React) is the primary surface for text, vision, embeddings, and speech.\n\n## Supported Models\n\nThe native engine runs these modalities today. All load straight from the HuggingFace Hub\nvia `WebGPUEngine.create({ repo })`.\n\n### Text\n\n| Model | Repo | Notes |\n|-------|------|-------|\n| **Qwen3.5-0.8B** | `mlx-community/Qwen3.5-0.8B-4bit` | Default text model; vision-capable (`Qwen/Qwen3.5-0.8B` for the ViT) |\n| **Qwen3.5-2B** | `Qwen/Qwen3.5-2B` | Higher quality; 262k context; multimodal (vision-capable) |\n| **Qwen3.5-4B** | `mlx-community/Qwen3.5-4B-4bit` | Highest quality that fits browser WebGPU (~2.5 GB INT4); 262k context; vision-capable; thinking |\n| **LFM2.5-350M** | `LiquidAI/LFM2.5-350M` | Hybrid conv/attention, very fast, ~199 MB q4 |\n| **Gemma 4 E2B** | `mlx-community/gemma-4-e2b-it-4bit` | PLE CPU-streamed; vision-capable |\n\n### Vision (image → text, `describeImage`)\n\n| Tower | From | Notes |\n|-------|------|-------|\n| **Qwen3.5 ViT** | `Qwen/Qwen3.5-0.8B` (`enableVision: true`) | Bit-exact vs HF |\n| **Gemma 4 ViT** | `mlx-community/gemma-4-e2b-it-4bit` (`enableVision: true`) | Native projector |\n\n### Embeddings (`embed`)\n\n| Model | Repo | Notes |\n|-------|------|-------|\n| **EmbeddingGemma-300M** | `mlx-community/embeddinggemma-300m-4bit` | 768-dim, asymmetric (`taskType`), runs on iPad |\n\n### Speech\n\n| Model | Type | Repo | Notes |\n|-------|------|------|-------|\n| **Kani-TTS-2** | TTS | `nineninesix/kani-tts-2-en` | `engine.speak()` → 22.05 kHz PCM |\n| **Gepard-1.0** | TTS | `nineninesix/gepard-1.0` | Streaming `engine.speak({ onChunk })` → 22.05 kHz PCM, Apache-2.0 |\n| **Moonshine** | STT | `UsefulSensors/moonshine-base` | `MoonshineSTT.transcribe()`, raw-waveform |\n\n### Quantization & dtype\n\n`dtype: \"auto\"` (the React-hook default) picks int4 on mobile and the repo's native\nprecision on desktop. For Qwen3.5-0.8B on Dawn/Node:\n\n| Format | Download | tok/s | Notes |\n|---|---|---|---|\n| MLX 4-bit (affine) | 404 MB | fastest | Smallest. Recommended. |\n| GPTQ (AutoRound) | 734 MB | fast | Pre-quantized linears, F16 embed |\n| F32 (on-the-fly Q4) | 1666 MB | slowest | No pre-quantization needed |\n\n> Throughput moves run-to-run and across the optimization loop; treat these as relative,\n> not promises.\n\n### WGSL Kernels\n\nMatMul, MatMulInt4, EmbeddingInt4, RMSNorm, RoPE, GQA Attention (flash-style, causal +\nbidirectional), SwiGLU/GeGLU, CrossAttention, CausalConv1d, M-RoPE, EmbedSplice, FSQ +\nHiFi-GAN (NanoCodec decoder), and more.\n\n> **High-level `Gerbil` class.** `import { Gerbil } from \"@tryhamster/gerbil\"` (plus the\n> one-liner and `@tryhamster/gerbil/skills`) is a supported convenience wrapper over the\n> native `WebGPUEngine` — ideal for quick scripts, the CLI, and the AI SDK. Reach for\n> `WebGPUEngine` / `useEngine` directly when you want lower-level control over loading,\n> vision, embeddings, and speech.\n\n## Documentation\n\nFull documentation, guides, and a live playground live at **[gerbilsdk.com/docs](https://gerbilsdk.com/docs)**.\n\n| Guide | Description |\n|-------|-------------|\n| [📖 Getting Started](https://gerbilsdk.com/docs/getting-started) | Install, load a model, core concepts |\n| [📖 Structured Output](https://gerbilsdk.com/docs) | `generateObject` / `useObject` — validated JSON with retries |\n| [📖 Embeddings](https://gerbilsdk.com/docs) | EmbeddingGemma semantic search, similarity, RAG |\n| [📖 Vision](https://gerbilsdk.com/docs/vision) | Image → text with Qwen3.5 ViT & Gemma 4 ViT |\n| [📖 Text-to-Speech](https://gerbilsdk.com/docs/tts) | Native Kani-TTS-2 (`engine.speak()`) |\n| [📖 Speech-to-Text](https://gerbilsdk.com/docs/stt) | Native Moonshine (`MoonshineSTT`) |\n| [📖 Browser](https://gerbilsdk.com/docs/browser) | WebGPU inference, React hooks |\n| [📖 Hooks](https://gerbilsdk.com/docs/hooks) | `useEngine` / `useObject` / `useTTS` / `useSTT` |\n| [📖 Skills](https://gerbilsdk.com/docs/skills) | Built-in skills, custom skill development |\n| [📖 Tools](https://gerbilsdk.com/docs/tools) | Tool calling, agentic workflows |\n| [📖 REPL](https://gerbilsdk.com/docs/repl) | Interactive terminal dashboard |\n| [📖 AI SDK](https://gerbilsdk.com/docs/frameworks/ai-sdk) | Vercel AI SDK v5 (LLM, TTS, STT, Embeddings) |\n| [📖 Frameworks](https://gerbilsdk.com/docs/frameworks) | Next.js, Express, React, LangChain |\n| [📖 CLI](https://gerbilsdk.com/docs/cli) | All CLI commands and options |\n| [📖 Mobile](https://gerbilsdk.com/docs/mobile) | iOS / iPadOS guidance & memory guards |\n| [📖 MCP](https://gerbilsdk.com/docs/mcp) | MCP server for Claude Desktop & Cursor |\n\n## Requirements\n\nThe native engine needs a real GPU and a WebGPU runtime:\n\n- **Browser** — Chrome/Edge 113+, Safari 26+ (iOS/iPadOS 26+), or Firefox 141+\n- **Node** — Node.js 18+ with the `webgpu` package (Dawn) installed\n\nOn devices without WebGPU the engine throws a clear error rather than silently degrading.\n\n## License\n\nMIT\n\n---\n\n<p align=\"center\">\n  <sub>Part of the <a href=\"https://tryhamster.com\">Hamster</a> family 🐹</sub>\n</p>\n","readmeFilename":"README.md"}