Practical overview
When it fits
Use it when you want a Python/PyTorch starting point for experimenting with a model that interleaves Mamba and transformer components.
Who it is for
- Developers prototyping hybrid state-space and attention architectures
- Researchers experimenting with sequence and long-context language modeling
Documented uses
- Configure a hybrid model with chosen dimensions and Mamba and transformer depths.
- Pass token tensors through the model and inspect its output.
- Use the implementation as a starting point for sequence-modeling experiments.
Requirements & limits
- The README specifies an MIT license.
- The documented usage requires Python and PyTorch.
- The example demonstrates model construction and a forward pass, not a complete training or text-generation workflow.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install and pass a token tensor through the model
Set up the package and follow the README example to instantiate the model and obtain an output tensor.
Before you start
- Python environment with pip3
- PyTorch, imported as torch
Steps
Install the package with the command below.
pip3 install mambatransformerImport
torchandMambaTransformerfrommamba_transformer.Create an integer tensor such as
torch.randint(0, 100, (1, 10))and instantiate the model with the documented configuration parameters.Pass the tensor to the model and inspect the returned output shape.
Expected outcome
A model instance processes the example token tensor and returns an output tensor.
Guide limitations
- The README example does not document a complete training procedure.
- Tokenizer setup and a complete text-generation workflow are not provided.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 8 October 2026
- Original website title
- kyegomez/MambaTransformer · Official homepage title · retained in its source language