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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- Node.js 18+\n- Runs on Linux (x86_64), macOS (arm64, x86_64), Windows (x86_64, arm64), and Raspberry Pi (3, 4, 5).\n\n## Installation\n\nUsing `Yarn`:\n\n```console\nyarn add @picovoice/picollm-node\n```\n\nor using `npm`:\n\n```console\nnpm install --save @picovoice/picollm-node\n```\n\n## Models\n\npicoLLM Inference Engine 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  - `gemma-7b`\n  - `gemma-7b-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-2-13b`\n  - `llama-2-13b-chat`\n  - `llama-2-70b`\n  - `llama-2-70b-chat`\n- Llama-3\n  - `llama-3-8b`\n  - `llama-3-8b-instruct`\n  - `llama-3-70b`\n  - `llama-3-70b-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- Mixtral\n  - `mixtral-8x7b-v0.1`\n  - `mixtral-8x7b-instruct-v0.1`\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## 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\nCreate an instance of the engine and generate a prompt completion:\n\n```javascript\nconst { PicoLLM } = require(\"@picovoice/picollm-node\");\n\nconst pllm = new PicoLLM(\n    '${ACCESS_KEY}',\n    '${MODEL_PATH}');\n\nconst res = await pllm.generate('${PROMPT}');\nconsole.log(res.completion);\n```\n\nReplace `${ACCESS_KEY}` with yours obtained from Picovoice Console, `${MODEL_PATH}` with the path to a model file\ndownloaded from Picovoice Console, and `${PROMPT}` with a prompt string.\n\nTo interrupt completion generation before it has finished:\n```javascript\npllm.interrupt()\n```\n\nInstruction-tuned models (e.g., `llama-3-8b-instruct`, `llama-2-7b-chat`, and `gemma-2b-it`) have a specific chat\ntemplate. 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