Dead simple way to run LLaMA on your computer.

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1. Powered by llama.cpp and llama-dl CDN
2. Hackable web app included
3. Ships with JavaScript API
4. Ships with Socket.io API

Install the 7B model (default) and start a web UI:

npx dalai llama
npx dalai serve

Then go to http:https://localhost:3000

Above two commands do the following:

1. First installs the 7B module (default)
2. Then starts a web/API server at port 3000

Basic install (7B model only)

npx dalai llama

Install all models

npx dalai llama 7B 13B 30B 65B

The install command :

1. Creates a folder named dalai under your home directory (~)
2. Installs and builds the llama.cpp project under ~/llama.cpp
3. Downloads all the requested models from the llama-dl CDN to ~/llama.cpp/models
4. Runs some tasks to convert the LLaMA models so they can be used

Dalai is also an NPM package:

1. programmatically install
2. locally make requests to the model
3. run a dalai server (powered by socket.io)
4. programmatically make requests to a remote dalai server (via socket.io)

Dalai is an NPM package. You can install it using:

npm install dalai

const dalai = new Dalai(home)

• home: (optional) manually specify the llama.cpp folder

By default, Dalai automatically stores the entire llama.cpp repository under ~/llama.cpp.

However, often you may already have a llama.cpp repository somewhere else on your machine and want to just use that folder. In this case you can pass in the home attribute.

Creates a workspace at ~/llama.cpp

const dalai = new Dalai()

Manually set the llama.cpp path:

const dalai = new Dalai("/Documents/llama.cpp")

dalai.request(req, callback)

• req: a request object. made up of the following attributes:
  • prompt: (required) The prompt string
  • model: (required) The model name to query ("7B", "13B", etc.)
  • url: only needed if connecting to a remote dalai server
    • if unspecified, it uses the node.js API to directly run dalai locally
    • if specified (for example ws:https://localhost:3000) it looks for a socket.io endpoint at the URL and connects to it.
  • threads: The number of threads to use (The default is 8 if unspecified)
  • n_predict: The number of tokens to return (The default is 128 if unspecified)
  • seed: The seed. The default is -1 (none)
  • top_k
  • top_p
  • repeat_last_n
  • repeat_penalty
  • temp: temperature
  • batch_size: batch size
  • skip_end: by default, every session ends with \n\n<end>, which can be used as a marker to know when the full response has returned. However sometimes you may not want this suffix. Set skip_end: true and the response will no longer end with \n\n<end>
• callback: the streaming callback function that gets called every time the client gets any token response back from the model

Using node.js, you just need to initialize a Dalai object with new Dalai() and then use it.

const Dalai = require('dalai')
new Dalai().request({
  model: "7B",
  prompt: "The following is a conversation between a boy and a girl:",
}, (token) => {
  process.stdout.write(token)
})

To make use of this in a browser or any other language, you can use thie socket.io API.

First you need to run a Dalai socket server:

// server.js
const Dalai = require('dalai')
new Dalai().serve(3000)     // port 3000

Then once the server is running, simply make requests to it by passing the ws:https://localhost:3000 socket url when initializing the Dalai object:

const Dalai = require("dalai")
new Dalai().request({
  url: "ws:https://localhost:3000",
  model: "7B",
  prompt: "The following is a conversation between a boy and a girl:",
}, (token) => {
  console.log("token", token)
})

Starts a socket.io server at port

dalai.serve(port)

const Dalai = require("dalai")
new Dalai().serve(3000)

connect with an existing http instance (The http npm package)

dalai.http(http)

• http: The http object

This is useful when you're trying to plug dalai into an existing node.js web app

const app = require('express')();
const http = require('http').Server(app);
dalai.http(http)
http.listen(3000, () => {
  console.log("server started")
})

await dalai.install(model1, model2, ...)

• models: the model names to install ("7B"`, "13B", "30B", "65B", etc)

Install the "7B" and "13B" models:

const Dalai = require("dalai");
const dalai = new Dalai()
await dalai.install("7B", "13B")

returns the array of installed models

const models = await dalai.installed()

const Dalai = require("dalai");
const dalai = new Dalai()
const models = await dalai.installed()
console.log(models)     // prints ["7B", "13B"]