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Llama CPP

Compatibility

Only available on Node.js.

This module is based on the node-llama-cpp Node.js bindings for llama.cpp, allowing you to work with a locally running LLM. This allows you to work with a much smaller quantized model capable of running on a laptop environment, ideal for testing and scratch padding ideas without running up a bill!

Setup​

You'll need to install the node-llama-cpp module to communicate with your local model.

npm install -S node-llama-cpp

You will also need a local Llama 2 model (or a model supported by node-llama-cpp). You will need to pass the path to this model to the LlamaCpp module as a part of the parameters (see example).

Out-of-the-box node-llama-cpp is tuned for running on a MacOS platform with support for the Metal GPU of Apple M-series of processors. If you need to turn this off or need support for the CUDA architecture then refer to the documentation at node-llama-cpp.

For advice on getting and preparing llama2 see the documentation for the LLM version of this module.

A note to LangChain.js contributors: if you want to run the tests associated with this module you will need to put the path to your local model in the environment variable LLAMA_PATH.

Usage​

Basic use​

In this case we pass in a prompt wrapped as a message and expect a response.

import { ChatLlamaCpp } from "langchain/chat_models/llama_cpp";
import { HumanMessage } from "langchain/schema";

const llamaPath = "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin";

const model = new ChatLlamaCpp({ modelPath: llamaPath });

const response = await model.call([
  new HumanMessage({ content: "My name is John." }),
]);
console.log({ response });

/*
  AIMessage {
    lc_serializable: true,
    lc_kwargs: {
      content: 'Hello John.',
      additional_kwargs: {}
    },
    lc_namespace: [ 'langchain', 'schema' ],
    content: 'Hello John.',
    name: undefined,
    additional_kwargs: {}
  }
*/

API Reference:

System messages​

We can also provide a system message, note that with the llama_cpp module a system message will cause the creation of a new session.

import { ChatLlamaCpp } from "langchain/chat_models/llama_cpp";
import { SystemMessage, HumanMessage } from "langchain/schema";

const llamaPath = "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin";

const model = new ChatLlamaCpp({ modelPath: llamaPath });

const response = await model.call([
  new SystemMessage(
    "You are a pirate, responses must be very verbose and in pirate dialect, add 'Arr, m'hearty!' to each sentence."
  ),
  new HumanMessage("Tell me where Llamas come from?"),
]);
console.log({ response });

/*
  AIMessage {
    lc_serializable: true,
    lc_kwargs: {
      content: "Arr, m'hearty! Llamas come from the land of Peru.",
      additional_kwargs: {}
    },
    lc_namespace: [ 'langchain', 'schema' ],
    content: "Arr, m'hearty! Llamas come from the land of Peru.",
    name: undefined,
    additional_kwargs: {}
  }
*/

API Reference:

Chains​

Finally we can also use this module with chains, note that using more complex chains will require suitably powerful version of llama2 such as the 70B version.

import { ChatLlamaCpp } from "langchain/chat_models/llama_cpp";
import { PromptTemplate } from "langchain/prompts";
import { LLMChain } from "langchain/chains";

const llamaPath = "/Replace/with/path/to/your/model/gguf-llama2-q4_0.bin";

const model = new ChatLlamaCpp({ modelPath: llamaPath, temperature: 0.5 });

const prompt = PromptTemplate.fromTemplate(
  "What is a good name for a company that makes {product}?"
);
const chain = new LLMChain({ llm: model, prompt });

const response = await chain.call({ product: "colorful socks" });

console.log({ response });

/*
  {
  text: `I'm not sure what you mean by "colorful socks" but here are some ideas:\n` +
    '\n' +
    '- Sock-it to me!\n' +
    '- Socks Away\n' +
    '- Fancy Footwear'
  }
*/

API Reference: