picoLLM Inference Engine Web Binding

Made in Vancouver, Canada by Picovoice

picoLLM Inference Engine

picoLLM Inference Engine is a highly accurate and cross-platform SDK optimized for running compressed large language models. picoLLM Inference Engine is:

• Accurate; picoLLM Compression improves GPTQ by significant margins
• Private; LLM inference runs 100% locally.
• Cross-Platform
• Runs on CPU and GPU
• Free for open-weight models

Compatibility

• Chrome / Edge
• Firefox
• Safari

NOTE: IndexedDB and SIMD are required to use picoLLM.

Installation

Using Yarn:

yarn add @picovoice/picollm-web

or using npm:

npm install --save @picovoice/picollm-web

Models

picoLLM Inference Engine on Web supports the following open-weight models. The models are on Picovoice Console.

• Gemma
  • gemma-2b
  • gemma-2b-it
• Llama-2
  • llama-2-7b
  • llama-2-7b-chat
  • llama-3-8b
  • llama-3-8b-instruct
• Mistral
  • mistral-7b-v0.1
  • mistral-7b-instruct-v0.1
  • mistral-7b-instruct-v0.2
• Phi-2
  • phi2

NOTE: Only gemma and Phi-2 models have been tested on multiple browsers across different platforms. The rest of the models depend on the user's system in order to run properly.

AccessKey

AccessKey is your authentication and authorization token for deploying Picovoice SDKs, including picoLLM. Anyone who is using Picovoice needs to have a valid AccessKey. You must keep your AccessKey secret. You would need internet connectivity to validate your AccessKey with Picovoice license servers even though the LLM inference is running 100% offline and completely free for open-weight models. Everyone who signs up for Picovoice Console receives a unique AccessKey.

Usage

picoLLM Model File Types

picoLLM accepts model files in three different types:

File URL(s):

const modelFile = `${SERVER_URL}/${PATH_TO_MODEL_FILE}`;

or if the model file is too big (2GB or larger) consider using chunks:

const modelFile = [
  `${SERVER_URL}/${PATH_TO_MODEL_FILE_PART1}`,
  `${SERVER_URL}/${PATH_TO_MODEL_FILE_PART2}`,
  `...` // add more parts if needed
];

File Object(s):

const modelFile = new File([/* file contents */]);

or if the model file is too big (2GB or larger) consider using chunks:

const modelFile = [
  new File([/* file contents part 1 */]),
  new File([/* file contents part 2 */]),
  ... // add more parts if needed
];

File objects are usually used with HTML's input tag:

<input id="modelFile" type="file" accept="pllm" />

<script>
  const modelFile = document.getElementById("modelFile").files;
</script>

Blob Object(s):

const modelFile = new Blob([new Uint8Array(/* model bytes */)]);

or if the model file is too big (2GB or larger) consider using chunks:

const modelFile = [
  new Blob([new Uint8Array(/* model bytes part 1 */)]),
  new Blob([new Uint8Array(/* model bytes part 2 */)]), 
  ... // add more parts if needed
];

picoLLM Model

picoLLM saves and caches your parameter model file (.pllm) in IndexedDB to be used by Web Assembly. Use a different cacheFilePath variable to hold and cache multiple model values and set the cacheFileOverwrite value to true to force re-save the model file. If the model file changes, cacheFileVersion should be incremented to force the cached models to be updated. Use numFetchRetries to change the number of fetch retry attempts for the model file.

const picoLLMModel = {
  modelFile: modelFile, // Based on the sections before,
  
  // Optional
  cacheFilePath: 'custom_model',
  cacheFileOverwrite: true,
  cacheFileVersion: 1,
  numFetchRetries: 0,
}

Initialize picoLLM

Initialize an instance of picoLLM in a worker thread:

const picoLLM = await PicoLLMWorker.create(
  ${ACCESS_KEY},
  picoLLMModel,
);

Replace ${ACCESS_KEY} with yours obtained from Picovoice Console.

Generate Completion

const res = await picoLLM.generate(`${PROMPT}`);
console.log(res.completion);

Replace ${PROMPT} with a prompt string.

Instruction-tuned models

Instruction-tuned models (e.g., llama-2-7b-chat, and gemma-2b-it) have a specific chat template. You can either directly format the prompt or use a dialog helper:

const dialog = picoLLM.getDialog()
dialog.addHumanRequest(prompt)

const res = await picoLLM.generate(dialog.prompt())
dialog.addLLMResponse(res.completion)
print(res.completion)

Clean Up

Clean up used resources by picoLLM or picoLLMWorker:

await picoLLM.release()

Demos

Refer to our Web demos for examples using LLM completion and chat using picoLLM.