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Palm Pytorch

Palm Pytorch is a Python and PyTorch implementation of the PaLM Transformer architecture, with configurable model construction, token-ID input and vocabulary-logit output, plus an Enwik8 training example.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Palm Pytorch — 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 inspecting or experimenting with a compact educational implementation of the PaLM architecture, rather than seeking code intended to scale to the paper's largest model.

Who it is for

  • Learners studying Transformer and language-model architecture in PyTorch.
  • Researchers or developers exploring small-scale PaLM-style configurations and language-modeling examples.

Documented uses

  • Configure a PaLM-style Transformer by choosing vocabulary size, dimensions, depth and attention-head settings.
  • Pass token IDs through the model and obtain vocabulary logits.
  • Explore the repository's Enwik8 training example.
Before you choose

Requirements & limits

The public GitHub repository is MIT-licensed and documents package installation with pip.

  • The README explicitly describes the code as educational and says it will not scale to the largest model.
  • The documented interface consumes token IDs and returns vocabulary logits.
  • An Enwik8 training example is included through train.py.

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 package and construct a PaLM model

Set up the documented Python package and instantiate a configured model that accepts token IDs and returns vocabulary logits.

Before you start

  • Python environment
  • PyTorch
  • The PaLM-pytorch package

Steps

  1. Install the package with the command below.

    Documented command
    pip install PaLM-pytorch

  2. Import torch and PaLM from palm_pytorch.

  3. Construct PaLM with the documented settings, such as num_tokens=20000, dim=512, depth=12, heads=8, and dim_head=64.

  4. Pass a token-ID tensor to the model; the README example returns logits with shape (1, 2048, 20000).

Expected outcome

A configured PaLM model and the documented input-to-logits usage pattern.

Guide limitations

  • The implementation is framed as educational and is not intended to scale to the largest model in the PaLM paper.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
lucidrains/PaLM-pytorch · Official homepage title · retained in its source language
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