@realtimex/node-llama-cpp

node-llama-cpp

Run AI models locally on your machine

_{Pre-built bindings are provided with a fallback to building from source with cmake}

✨ gpt-oss is here! ✨

Features

• Run LLMs locally on your machine
• Metal, CUDA and Vulkan support
• Pre-built binaries are provided, with a fallback to building from source without node-gyp or Python
• Adapts to your hardware automatically, no need to configure anything
• A Complete suite of everything you need to use LLMs in your projects
• Use the CLI to chat with a model without writing any code
• Up-to-date with the latest llama.cpp. Download and compile the latest release with a single CLI command
• Enforce a model to generate output in a parseable format, like JSON, or even force it to follow a specific JSON schema
• Provide a model with functions it can call on demand to retrieve information or perform actions
• Embedding and reranking support
• Safe against special token injection attacks
• Great developer experience with full TypeScript support, and complete documentation
• Much more

Documentation

• Getting started guide
• API reference
• CLI help
• Blog
• Changelog

Try It Without Installing

Chat with a model in your terminal using a single command:

npx -y @realtimex/node-llama-cpp chat

Installation

npm install @realtimex/node-llama-cpp

This package comes with pre-built binaries for macOS, Linux and Windows.

On linux-x64 and win-x64 with CUDA, the large fallback CUDA backend may be downloaded from the matching GitHub release the first time it is needed. If pre-built binaries are not available for your platform, or the downloaded CUDA fallback backend still does not work with your machine, it'll fallback to download a release of llama.cpp and build it from source with cmake. To disable this behavior, set the environment variable NODE_LLAMA_CPP_SKIP_DOWNLOAD to true.

Usage

import {fileURLToPath} from "url";
import path from "path";
import {getLlama, LlamaChatSession} from "@realtimex/node-llama-cpp";

const __dirname = path.dirname(fileURLToPath(import.meta.url));

const llama = await getLlama();
const model = await llama.loadModel({
    modelPath: path.join(__dirname, "models", "Meta-Llama-3.1-8B-Instruct.Q4_K_M.gguf")
});
const context = await model.createContext();
const session = new LlamaChatSession({
    contextSequence: context.getSequence()
});


const q1 = "Hi there, how are you?";
console.log("User: " + q1);

const a1 = await session.prompt(q1);
console.log("AI: " + a1);


const q2 = "Summarize what you said";
console.log("User: " + q2);

const a2 = await session.prompt(q2);
console.log("AI: " + a2);

For more examples, see the getting started guide

Contributing

To contribute to node-llama-cpp read the contribution guide.

Acknowledgements

• llama.cpp: ggml-org/llama.cpp

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