Vicuna 7B

Vicuna 7B is a large language model that runs in the browser.

This library is a port of the fantastic web-llm implementation that exposes programmatic local access to the model with minimal configuration.

Demo

Prerequisites

See the Instructions section here for more information on required prerequisites, and confirm that the demo UI works with your hardware.

Getting Started

Install with:

npm install vicuna-7b

Then, you can import it into your project with:

import Vicuna7B from 'vicuna-7b';

const llm = new Vicuna7B();

llm.generate(`Tell me a joke about otters!`).then(response => console.log(response));

API

constructor

const initCallback = ({ progress, timeElapsed, text }) => {
  console.log(progress, timeElapsed, text);
};
new Vicuna7B({
  initCallback,
});

The constructor accepts an optional payload of arguments:

• initCallback - An optional initialization callback
• logger - An optional general logging function callback;
• runtimeURL - An optional string denoting a URL to the runtime URL (corresponds to lib/tvmjs_runtime.wasi.js)
• bundleURL - An optional string denoting a URL to the bundle URL (corresponds to lib/tvmjs.bundle.js)
• tokenizerURL - An optional string denoting a URL to the tokenizer URL (corresponds to lib//tokenizer.model)
• vicunaURL - An optional string denoting a URL to the model URL (corresponds to lib/vicuna-7b_webgpu.wasm)
• sentencePieceURL - An optional string denoting a URL to the sentence piece URL (corresponds to lib/sentencepiece/index.js)
• cacheURL - An optional string denoting a URL to the cache URL (corresponds to https://huggingface.co/mlc-ai/web-lm/resolve/main/vicuna-0b/)

By default the library will load its runtime requirements from a CDN, https://www.jsdelivr.com/, if no URL arguments are provided.

An example with full options looks like:

const initCallback = ({ progress, timeElapsed, text }) => {
  console.log(progress, timeElapsed, text);
};
new Vicuna7B({
  initCallback,
  logger: console.log,
  runtimeURL: 'http://path-to-url',
  bundleURL: 'http://path-to-url',
  tokenizerURL: 'http://path-to-url',
  vicunaURL: 'http://path-to-url',
  sentencePieceURL: 'http://path-to-url',
  cacheURL: 'http://path-to-url',
});

generate

const prompt = `Tell me a joke about otters!`;
const generateCallback = (step, text) => {
  console.log(text);
}
const config = {
  maxGenLength: 32,
  stopWords: ['Q:'],
  temperature: 0.5,
  top_p: 0.95,
  callback: generateCallback,
};
llm.generate(prompt, config).then(response => {
  console.log(response);
});

generate accepts two arguments:

• prompt - the text prompt to pass to the LLM model
• params - an optional set of config parameters to pass to the model

The callback argument from the params will be invoked on every step of the model generation. This callback receives a step parameter indicating the current step integer, and the text generated at that step. This can be useful if you wish to show the output as the model generates it.

reset

llm.reset().then(() => {
  console.log('done');
});

Resets the LLM.

You can optionally pass in a new set of constructor parameters that will then be persisted.

getRuntimeStats

llm.getRuntimeStats().then(stats => {
  console.log(stats);
})

Exposes runtime stats information. Runtime stats is returned in the format:

{
  encoding: float,
  decoding: float,
}

You can see an example in the original UI.

License

Apache 2.0

Credits

All credit goes to the original implementation at web-llm.

Simon Willison has a great piece on his blog about his experiments with the LLM.