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Multistream Transformers

A Python/PyTorch library implementing a multi-stream Transformer, with attention pooling across tokens at the same position and configurable causal behavior, sequence length, depth and stream count.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Multistream Transformers — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a Python/PyTorch experiment calls for a configurable multi-stream Transformer and you want to examine how stream count or causal settings affect a model.

Who it is for

  • Researchers prototyping multi-stream language or sequence models.
  • Developers comparing Transformer configurations, including stream count and causal operation.

Documented uses

  • Construct a Transformer model with configurable numbers of streams.
  • Explore causal or non-causal sequence-modeling configurations.
  • Compare a multi-stream setup with a regular Transformer; the README identifies one stream as equivalent to a regular Transformer.
Before you choose

Requirements & limits

The public GitHub repository reports an MIT license. The README documents installation through pip.

  • The implementation uses attention pooling across tokens at the same position instead of the paper’s skip connection across all streams.
  • The constructor exposes vocabulary size, dimension, depth, causal mode, maximum sequence length and number of streams.
  • The README gives a Python example using token IDs and a mask to obtain logits.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the library and configure a model

Set up the package and define a MultistreamTransformer with documented model settings.

Before you start

  • A Python environment with PyTorch available.

Steps

  1. Install the package with the command below.

    Documented command
    pip install multistream-transformers

  2. Import MultistreamTransformer from multistream_transformers.

  3. Set the vocabulary size, dimension, depth, causal mode, maximum sequence length and number of streams in the constructor.

Expected outcome

A configured MultistreamTransformer object ready to receive inputs in a Python program.

Guide limitations

  • The installation and constructor example do not document training, inference workflows beyond a forward call, or deployment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
lucidrains/multistream-transformers · Official homepage title · retained in its source language
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