OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Jamba

Jamba is a Python/PyTorch implementation of a hybrid Transformer–Mamba language model, with configurable attention, state-space and expert parameters.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Jamba — 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 Jamba when you want a code-level starting point for configuring and exercising a Transformer–Mamba model in PyTorch, or when you want to investigate the included training entry point.

Who it is for

  • Python and PyTorch developers prototyping language-model inference with token inputs.
  • Researchers and developers experimenting with model depth, attention, state-space and expert settings.
  • Developers exploring a provided training script for a hybrid language model.

Documented uses

  • Install the Jamba package and import its model class.
  • Configure model dimensions and attention, state-space and expert parameters.
  • Pass token tensors through the model to obtain predictions.
  • Invoke the repository's training script.
Before you choose

Requirements & limits

  • The README demonstrates constructing a model and running a forward pass; it does not document pretrained weights or tokenization.
  • Training is referenced through python3 train.py, but dataset preparation and training configuration are not explained.
  • The repository specifies an MIT license.

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 Jamba and run the README model example

Set up the package and pass a token tensor through a configured Jamba model.

Before you start

  • Python environment with PyTorch available.
  • The README specifies installing the package with pip.

Steps

  1. Install the package with the command below.

    Documented command
    pip install jamba

  2. Import torch and Jamba from jamba.model.

  3. Create a token tensor and initialize Jamba with the desired model parameters.

  4. Call the model with the tensor and inspect its predictions.

Expected outcome

A forward pass returns model predictions for the supplied token tensor.

Guide limitations

  • The example uses randomly generated token IDs; it does not document tokenization or pretrained weights.
  • The guide reflects README instructions and is not a human execution test.
Documentation 1 ↗
Source-based practical guide

Start the provided training script

Invoke the training script named in the README.

Before you start

  • Python environment with the project dependencies available.
  • A local copy of the repository containing train.py.

Steps

  1. Obtain the repository.

  2. Run the command below from the project directory.

    Documented command
    python3 train.py

Expected outcome

The documented training command is invoked.

Guide limitations

  • The README does not specify dataset preparation, training options, expected outputs or hardware requirements.
  • This is source-based guidance, not a verified training run.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
kyegomez/Jamba · Official homepage title · retained in its source language
How we review sourcesSuggest a correction