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native Node.js bindings — single omni.node for LLM, TTS, and Image/Video generation","maintainers":[{"name":"ddalcu","email":"david.dalcu@gmail.com"}],"readme":"# node-omni-orcha\n\nUnified native Node.js inference engine — **LLM**, **Vision**, **Image/Video Generation**, and **Text-to-Speech** in a single `omni.node` binary.\n\nBuilt on a [llama.cpp](https://github.com/ggml-org/llama.cpp) fork (synced to [`f49e917`](https://github.com/ggml-org/llama.cpp/commit/f49e917)) with [stable-diffusion.cpp](https://github.com/leejet/stable-diffusion.cpp) and [qwen3-tts.cpp](https://github.com/predict-woo/qwen3-tts.cpp) compiled against a shared ggml backend.\n\n## Features\n\n- **LLM** — Chat completion, streaming, embeddings, tool calling, reasoning budget control\n- **Vision** — Multimodal LLM with image input (Gemma 4, Qwen2-VL, Qwen3-VL, LLaVA, Gemma3, etc.)\n- **Image Generation** — FLUX 2, Wan 2.2, SD/SDXL (text-to-image)\n- **Video Generation** — Wan 2.2 (text-to-video, image-to-video)\n- **Text-to-Speech** — Qwen3-TTS (voice cloning)\n- **GPU accelerated** — Metal (macOS), CUDA (NVIDIA), CPU fallback\n- **Single binary** — One `omni.node` for all engines, one shared ggml, no symbol conflicts\n- **Native N-API** — In-process inference, no child processes or HTTP servers\n- **Node 25** — Native TypeScript, ESM, no build step\n- **Web UI** — Built-in server with chat, TTS, image/video generation (for testing only)\n\n## Quick Start\n\n```bash\nnpm install node-omni-orcha\n\nnpm run build:metal   # macOS\nnpm run build:cuda    # NVIDIA\nnpm run build:cpu     # CPU only\n```\n\n## API\n\n```ts\nimport { loadModel, createModel, detectGpu, readGGUFMetadata } from 'node-omni-orcha'\n```\n\n### LLM\n\n```ts\nconst llm = await loadModel('qwen3.5-4b.gguf', { type: 'llm', contextSize: 4096 })\n\n// With reasoning (default)\nconst result = await llm.complete([\n  { role: 'system', content: 'You are a helpful assistant.' },\n  { role: 'user', content: 'Hello!' },\n], { temperature: 0.7, maxTokens: 512 })\n\nconsole.log(result.content)    // response text\nconsole.log(result.reasoning)  // thinking/reasoning (if model supports it)\nconsole.log(result.usage)      // { inputTokens, outputTokens, totalTokens }\n\n// Without reasoning (direct response, faster)\nconst fast = await llm.complete(messages, { thinkingBudget: 0, maxTokens: 256 })\n\n// With capped reasoning (N tokens of thinking, then respond)\nconst capped = await llm.complete(messages, { thinkingBudget: 64, maxTokens: 512 })\n\n// Streaming\nfor await (const chunk of llm.stream(messages, { thinkingBudget: 0 })) {\n  process.stdout.write(chunk.content ?? '')\n  if (chunk.done) break\n}\n\n// Embeddings\nconst embedding = await llm.embed('some text')\n\n// Tool calling\nconst result = await llm.complete(messages, {\n  tools: [{ name: 'get_weather', description: '...', parameters: { ... } }],\n  toolChoice: 'auto',\n})\n```\n\n### Vision LLM\n\n```ts\nconst vlm = await loadModel('qwen2-vl-2b.gguf', {\n  type: 'llm', contextSize: 4096, mmprojPath: 'mmproj.gguf'\n})\n\nconst result = await vlm.complete([{\n  role: 'user',\n  content: [\n    { type: 'image', data: pngBuffer },            // Buffer, or { path: 'photo.jpg' }\n    { type: 'text', text: 'Describe this image.' },\n  ],\n}])\n```\n\nSupported architectures: Gemma 4, Qwen2-VL, Qwen3-VL, LLaVA, Gemma3, CogVLM, Chameleon, MiniCPM, InternVL, etc.\n\n### Image Generation (FLUX 2)\n\n```ts\nconst img = createModel('flux-2-klein-4b.gguf', 'image')\nawait img.load({\n  llmPath: 'qwen3-4b.gguf',        // text encoder for FLUX 2\n  vaePath: 'flux2-vae.safetensors', // VAE decoder\n  keepVaeOnCpu: true,\n})\n\nconst png = await img.generate('a sunset over mountains', {\n  width: 512, height: 512, steps: 4, cfgScale: 1.0,\n})\nfs.writeFileSync('output.png', png)\n```\n\n### Video Generation (Wan 2.2)\n\n```ts\nconst vid = createModel('wan2.2-5b.gguf', 'image')\nawait vid.load({ t5xxlPath: 'umt5-xxl.gguf', vaePath: 'wan-vae.safetensors' })\n\nconst frames = await vid.generateVideo('a dog playing fetch', {\n  width: 832, height: 480, videoFrames: 33, steps: 30,\n})\n// frames is Buffer[] — one PNG per frame\n```\n\n### Qwen3-TTS (voice cloning)\n\n```ts\nconst tts = createModel('/path/to/qwen3-tts-models/', 'tts')\nawait tts.load()\n\n// Clone any voice from a short WAV reference\nconst wav = await tts.speak('Text to speak in cloned voice.', {\n  referenceAudioPath: '/path/to/reference.wav', // 24kHz mono, 3-10s recommended\n})\n\n// Or generate with default voice (no cloning)\nconst wav2 = await tts.speak('Default voice synthesis.')\n```\n\n### Utilities\n\n```ts\nconst gpu = detectGpu()\n// { backend: 'metal' | 'cuda' | 'cpu' }\n\nconst meta = await readGGUFMetadata('model.gguf')\n// { architecture, contextLength, blockCount, embeddingLength, ... }\n```\n\n## Web Server\n\n```bash\nnpm run serve    # http://localhost:3333\n```\n\nBuilt-in web UI with tabs for Chat, TTS, Image, and Video. Models load on-demand when you click a tab.\n\n## Architecture\n\nAll engines compile against a **single shared ggml** (tensor library + GPU backends) and link into one N-API addon:\n\n```\nengine/\n  ggml/        ← shared tensor ops, Metal/CUDA/CPU backends\n  src/         ← llama.cpp LLM core\n  tts/         ← qwen3-tts.cpp (compiled against shared ggml)\n  mtmd/        ← multimodal/vision (CLIP encoder, compiled against shared ggml)\n  diffusion/   ← stable-diffusion.cpp (compiled against shared ggml)\n\n→ build/Release/omni.node (single output, ~10-30MB depending on platform)\n```\n\n### Why not node-llama-cpp?\n\n[node-llama-cpp](https://github.com/withcatai/node-llama-cpp) is a well-maintained binding — if you only need LLM inference, use it. This project exists because we need multiple engines (LLM, TTS, Vision, Image/Video) sharing **one ggml build** in **one process**:\n\n- **No symbol conflicts** — llama.cpp, stable-diffusion.cpp, and qwen3-tts.cpp all depend on ggml. Separate binaries mean duplicate symbols and linker errors. One unified build solves this.\n- **One GPU backend** — Metal/CUDA is initialized once and shared across all engines. Separate bindings would compete for VRAM and require independent backend negotiation.\n- **Thin by design** — this library handles inference and chat templating (via llama.cpp's native Jinja engine) but not tool-call orchestration or model management. Those are handled by consumers (e.g. agent-orcha).\n\n## Build\n\n```bash\nnpm install\n\nnpm run build          # auto-detect GPU\nnpm run build:metal    # macOS Metal\nnpm run build:cuda     # NVIDIA CUDA\nnpm run build:cpu      # CPU only\n```\n\n## Test\n\n```bash\n# Unit tests (no models needed)\nnpm test\n\n# Download test models\nbash scripts/download-test-models.sh              # LLM (~745MB)\nbash scripts/download-test-models.sh --tts         # + Qwen3-TTS (~1.2GB)\nbash scripts/download-test-models.sh --image       # + FLUX 2 Klein (~5GB)\nbash scripts/download-test-models.sh --video       # + Wan 2.2 5B (~5GB)\nbash scripts/download-test-models.sh --vision      # + Qwen2-VL 2B (~1.8GB)\nbash scripts/download-test-models.sh --all         # Everything\n\n# Integration tests (requires models in ~/.orcha/workspace/.models/)\nnode scripts/full-integration-test.ts              # All engines\nnode scripts/vision-integration-test.ts            # Vision/Multimodal\n```\n\n## Platforms\n\n| Platform | GPU | Status |\n|----------|-----|--------|\n| macOS arm64 | Metal | Tested |\n| Linux x64 | CPU | CI |\n| Linux x64 | CUDA | CI |\n| Linux arm64 | CPU | CI |\n| Windows x64 | CPU | CI |\n| Windows x64 | CUDA | CI |\n\n## Requirements\n\n- **Node.js** >= 25.0.0\n- **CMake** >= 3.15\n- **C++17** compiler (Clang, GCC, MSVC)\n- **macOS**: Xcode Command Line Tools (for Metal)\n- **Linux/Windows**: CUDA Toolkit 13.1+ (for NVIDIA GPU support)\n\n## Upstream Sync Log\n\n| Date | llama.cpp Commit | Notable Additions |\n|------|-----------------|-------------------|\n| 2026-04-03 | [`f49e917`](https://github.com/ggml-org/llama.cpp/commit/f49e917) | Gemma 4 vision projector (`gemma4v`), Gemma 4 audio projector (`gemma4a`), Gemma 4 MoE LLM arch, Gemma 4 tokenizer, AMD ZenDNN label, various mtmd fixes |\n| 2026-04-02 | [`f49e917`](https://github.com/ggml-org/llama.cpp/commit/f49e917) | Initial full sync — Gemma 4 LLM (text-only), all existing vision projectors |\n\n**Local patches** (preserved across syncs):\n- `engine/ggml/src/ggml-cuda/ggml-cuda.cu` — CUDA errors throw `std::runtime_error` instead of `GGML_ABORT` so N-API callers can catch OOM and retry with fewer GPU layers\n- `engine/ggml/src/ggml-metal/ggml-metal-device.m` — Metal device customization\n\n## License\n\nMIT\n","readmeFilename":"README.md"}