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Inc"},"license":"Apache-2.0","keywords":["web","ai","offline","dom","llm"],"description":"picoLLM Inference Engine is a highly accurate and cross-platform SDK optimized for running compressed large language models.","maintainers":[{"name":"kyeo","email":"kyeo@picovoice.ai"},{"name":"kenarsa","email":"alireza@picovoice.ai"},{"name":"ilavery","email":"ian@picovoice.ai"},{"name":"erismikpico","email":"eric@picovoice.ai"},{"name":"matt200-ok","email":"matthew@picovoice.ai"},{"name":"gabe-picovoice","email":"gabriel@picovoice.ai"}],"readme":"# picoLLM Inference Engine Web Binding\n\nMade in Vancouver, Canada by [Picovoice](https://picovoice.ai)\n\n## picoLLM Inference Engine\n\npicoLLM Inference Engine is a highly accurate and cross-platform SDK optimized for running compressed large language\nmodels. picoLLM Inference Engine is:\n\n- Accurate; picoLLM Compression improves GPTQ by [significant margins](https://picovoice.ai/blog/picollm-towards-optimal-llm-quantization/)\n- Private; LLM inference runs 100% locally.\n- Cross-Platform\n- Runs on CPU and GPU\n- Free for open-weight models\n\n## Compatibility\n\n- Chrome / Edge\n- Firefox\n- Safari\n\n## Requirements\n\nPicoLLM Web Binding uses [SharedArrayBuffer](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/SharedArrayBuffer).\n\nInclude the following headers in the response to enable the use of `SharedArrayBuffers`:\n\n```\nCross-Origin-Opener-Policy: same-origin\nCross-Origin-Embedder-Policy: require-corp\n```\n\nRefer to our [Web demo](../../demo/web) for an example on creating a server with the corresponding response headers.\n\n**NOTE**: IndexedDB, SIMD and SharedArrayBuffers are required to use `picoLLM`. `PicoLLM` only supports running in a worker thread.\n\n## Installation\n\nUsing `Yarn`:\n\n```console\nyarn add @picovoice/picollm-web\n```\n\nor using `npm`:\n\n```console\nnpm install --save @picovoice/picollm-web\n```\n\n## Models\n\npicoLLM Inference Engine on Web supports the following open-weight models. The models are on\n[Picovoice Console](https://console.picovoice.ai/).\n\n- DeepSeek-OCR-2\n  - `deepseek-ocr-2`\n- EmbeddingGemma\n  - `embeddinggemma-300m`\n- Gemma\n  - `gemma-2b`\n  - `gemma-2b-it`\n- Gemma3\n  - `gemma-3-270m`\n  - `gemma-3-270m-it`\n- Llama-2\n  - `llama-2-7b`\n  - `llama-2-7b-chat`\n- Llama-3\n  - `llama-3-8b`\n  - `llama-3-8b-instruct`\n- Llama-3.2\n  - `llama3.2-1b-instruct`\n  - `llama3.2-3b-instruct`\n- Mistral\n  - `mistral-7b-v0.1`\n  - `mistral-7b-instruct-v0.1`\n  - `mistral-7b-instruct-v0.2`\n- Phi-2\n  - `phi2`\n- Phi-3\n  - `phi3`\n- Phi-3.5\n  - `phi3.5`\n- Qwen3-VL\n  - `qwen3-vl-2b-it`\n\n**NOTE**: Only Gemma, Phi-2, and Phi-3 models have been tested on multiple browsers across different platforms.\nThe rest of the models depend on the user's system in order to run properly.\n\n## AccessKey\n\nAccessKey is your authentication and authorization token for deploying Picovoice SDKs, including picoLLM. Anyone who is\nusing Picovoice needs to have a valid AccessKey. You must keep your AccessKey secret. You would need internet\nconnectivity to validate your AccessKey with Picovoice license servers even though the LLM inference is running 100%\noffline and completely free for open-weight models. Everyone who signs up for\n[Picovoice Console](https://console.picovoice.ai/) receives a unique AccessKey.\n\n## Usage\n\n### picoLLM Model File Types\n\n`picoLLM` accepts model files in three different types:\n\n#### File URL(s):\n\n```typescript\nconst modelFile = `${SERVER_URL}/${PATH_TO_MODEL_FILE}`;\n```\n\nor if the model file is too big (2GB or larger) consider using chunks:\n\n```typescript\nconst modelFile = [\n  `${SERVER_URL}/${PATH_TO_MODEL_FILE_PART1}`,\n  `${SERVER_URL}/${PATH_TO_MODEL_FILE_PART2}`,\n  `...` // add more parts if needed\n];\n```\n\n#### File Object(s):\n\n```typescript\nconst modelFile = new File([/* file contents */]);\n```\n\nor if the model file is too big (2GB or larger) consider using chunks:\n\n```typescript\nconst modelFile = [\n  new File([/* file contents part 1 */]),\n  new File([/* file contents part 2 */]),\n  ... // add more parts if needed\n];\n```\n\nFile objects are usually used with HTML's input tag:\n\n```html\n<input id=\"modelFile\" type=\"file\" accept=\"pllm\" />\n\n<script>\n  const modelFile = document.getElementById(\"modelFile\").files;\n</script>\n```\n\n#### Blob Object(s):\n\n```typescript\nconst modelFile = new Blob([new Uint8Array(/* model bytes */)]);\n```\n\nor if the model file is too big (2GB or larger) consider using chunks:\n\n```typescript\nconst modelFile = [\n  new Blob([new Uint8Array(/* model bytes part 1 */)]),\n  new Blob([new Uint8Array(/* model bytes part 2 */)]),\n  ... // add more parts if needed\n];\n```\n\n#### picoLLM Model\n\n`picoLLM` saves and caches your parameter model file (`.pllm`) in IndexedDB to be\nused by Web Assembly. Use a different `cacheFilePath` variable to hold and cache\nmultiple model values and set the `cacheFileOverwrite` value to true to force\nre-save the model file. If the model file changes, `cacheFileVersion` should be\nincremented to force the cached models to be updated. Use `numFetchRetries` to\nchange the number of fetch retry attempts for the model file.\n\n```typescript\nconst picoLLMModel = {\n  modelFile: modelFile, // Based on the sections before,\n\n  // Optional\n  cacheFilePath: 'custom_model',\n  cacheFileOverwrite: true,\n  cacheFileVersion: 1,\n  numFetchRetries: 0,\n}\n```\n\n### Initialize picoLLM\n\nInitialize an instance of `picoLLM` in a worker thread:\n\n```typescript\nconst picoLLM = await PicoLLMWorker.create(\n  ${ACCESS_KEY},\n  picoLLMModel,\n);\n```\n\nReplace `${ACCESS_KEY}` with yours obtained from Picovoice Console.\n\n### Generate Completion (Text Models)\n\n```typescript\nconst res = await picoLLM.generate(`${PROMPT}`);\nconsole.log(res.completion);\n```\n\nReplace `${PROMPT}` with a prompt string.\n\n### Instruction-tuned models\n\nInstruction-tuned models (e.g., `llama-2-7b-chat`, and `gemma-2b-it`) have a specific chat\ntemplate. You can either directly format the prompt or use a dialog helper:\n\n```typescript\nconst dialog = picoLLM.getDialog()\ndialog.addHumanRequest(prompt)\n\nconst res = await picoLLM.generate(dialog.prompt())\ndialog.addLLMResponse(res.completion)\nprint(res.completion)\n```\n\n### Interrupt Text Generation\n\n```typescript\npicoLLM.interrupt();\n```\n\nThis will stop text generation and if it was properly interrupted, it will set `res.completion.endpoint`\nas an interrupted state.\n\n### Clean Up\n\nClean up used resources by `picoLLM` or `picoLLMWorker`:\n\n```typescript\nawait picoLLM.release()\n```\n\n### Vision models\n\nTo run a VLM such as `qwen3-vl-2b-it`:\n\n```typescript\nconst image: PicoLLMImage = {\n  width: ${IMAGE_NUM_PIXELS_WIDTH},\n  height: ${IMAGE_NUM_PIXELS_HEIGHT},\n  data: ${IMAGE_DATA},\n};\n\nconst res = await picoLLM.generateWithImage(`${PROMPT}`, image);\nconsole.log(res.completion);\n```\n\nReplace `${PROMPT}` with a text prompt. For the image, you will need to get image height and width in number of pixels and the raw pixel values of the image in 8-bit, RGB format.\n\n### OCR models\n\nTo run an OCR model such as `deepseek-ocr-2`:\n\n```typescript\nconst image: PicoLLMImage = {\n  width: ${IMAGE_NUM_PIXELS_WIDTH},\n  height: ${IMAGE_NUM_PIXELS_HEIGHT},\n  data: ${IMAGE_DATA},\n};\n\nconst res = await picoLLM.generateOCR(image);\nconsole.log(res.completion);\n```\n\nFor the image, you will need to get image height and width in number of pixels and the raw pixel values of the image in 8-bit, RGB format.\n\n### Embedding models\n\nTo run an embedding model such as `embeddinggemma-300m`:\n\n```typescript\nconst res = await picoLLM.generateEmbeddings(`${PROMPT}`, image);\nres.embeddings.forEach(embedding => {\n  console.log(embedding);\n});\n```\n\nReplace `${PROMPT}` with a text prompt that you want to generate embeddings for.\n\n## Demos\n\nRefer to our [Web demos](https://github.com/Picovoice/picollm/tree/master/demo/web) for examples using LLM completion\nand chat using `picoLLM`.\n","readmeFilename":"README.md"}