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Palm Jax

Palm Jax is a Python implementation of the PaLM Transformer architecture using JAX and Equinox. It provides model construction parameters and a usage example that passes a token sequence to the model and returns logits.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Palm Jax — 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 you want a JAX and Equinox implementation to explore PaLM architecture configurations or incorporate PaLM-style model construction in Python work.

Who it is for

  • Python developers exploring PaLM-style Transformer configurations.
  • Researchers working with model construction in the JAX and Equinox ecosystem.

Documented uses

  • Configure a PaLM Transformer by setting vocabulary size, dimensions, depth and attention heads.
  • Pass a token sequence through the model and inspect its logits.
  • Explore the repository's recorded enwik8 training and ALiBi positional-encoding work for PaLM-lite.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. Its README documents installation with `pip install PaLM-jax`.

  • The implementation uses JAX and Equinox rather than PyTorch or Flax.
  • The README's forward-pass example uses random tokens and does not show tokenization or text generation.
  • A 540B-scale configuration is included as a parameter example, without resource requirements.

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

Follow the README's documented installation and model-construction example.

Before you start

  • Python environment able to install the PaLM-jax package
  • JAX and Equinox ecosystem
  • Vocabulary size, model dimensions, depth, attention heads and a JAX random key

Steps

  1. Install the package with the command below.

    Documented command
    pip install PaLM-jax

  2. Import jax and PaLM from palm_jax.

  3. Create a JAX random key and instantiate PaLM with the documented configuration parameters.

Expected outcome

A PaLM model object configured with the selected vocabulary size and Transformer dimensions.

Guide limitations

  • The installation and construction steps do not document a complete training or deployment workflow.
Documentation 1 ↗
Source-based practical guide

Pass a token sequence through the model

Use the README example to obtain logits from a constructed model.

Before you start

  • A constructed PaLM model
  • JAX and a JAX random key
  • A token sequence with values within the configured vocabulary range

Steps

  1. Create a sequence with jax.random.randint, using the shape and vocabulary bounds shown in the README.

  2. Call the model with the sequence.

  3. Inspect the returned logits; the example gives the shape (1, 1024, 20000).

Expected outcome

Logits for the input sequence, with the example returning one vocabulary-sized output per sequence position.

Guide limitations

  • The example uses randomly generated input and does not document text tokenization or generation.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
lucidrains/PaLM-jax · Official homepage title · retained in its source language
How we review sourcesSuggest a correction

Related options

PaLM-pytorch — The repository links to a PyTorch version of the PaLM implementation.

PaLM-flax — The repository links to a Flax version of the PaLM implementation.