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Simplemamba

Simplemamba is a Python library that provides a MambaBlock and an SSM sequence-model class, with PyTorch examples showing component calls and token sequences mapped to vocabulary-sized logits.

Explore the practical guide ↓

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Understand the project

Practical overview

When it fits

Use Simplemamba when you want documented Python examples for incorporating a MambaBlock or trying an SSM sequence model in a PyTorch project.

Who it is for

  • Python developers incorporating a MambaBlock into a PyTorch model.
  • Machine-learning practitioners prototyping state-space sequence modeling.
  • Developers exploring a small language-modeling experiment with token-index inputs and per-position vocabulary logits.

Documented uses

  • Instantiate and call a MambaBlock with configured dimensions and channel parameters.
  • Create an SSM model by specifying vocabulary, embedding and state dimensions, and layer count.
  • Pass a batch of token-index sequences to the SSM and inspect vocabulary-sized logits.
Before you choose

Requirements & limits

The README documents installation with pip install simple-mamba. The repository states an MIT license.

  • The README demonstrates both a standalone MambaBlock call and an SSM sequence-model call, so choose according to whether you need a component or a token-sequence example.
  • The SSM example configures vocabulary size, embedding dimension, state dimension and number of layers.
  • Examples show instantiation and forward calls; they do not provide a documented training or evaluation workflow.

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

Run the documented MambaBlock example

Instantiate a MambaBlock and pass it a sample PyTorch tensor.

Before you start

  • Python environment with PyTorch available.
  • Install the package with pip install simple-mamba.
  • Use the documented parameter values: dim 512, hidden_dim 128, heads 8, in_channels 3, out_channels 3, and kernel_size 3.

Steps

  1. Install SimpleMamba with the command below.

    Documented command
    pip install simple-mamba

  2. Import torch and MambaBlock from simple_mamba.

  3. Create a MambaBlock using the documented parameter values and create x = torch.randn(1, dim, dim).

  4. Call output = mamba_block(x) and print output.shape.

Expected outcome

The example passes the sample tensor through the block and prints the output shape.

Guide limitations

  • The README does not state the output shape value or provide a successful test result.
  • The example does not specify additional environment or version requirements.
Documentation 1 ↗
Source-based practical guide

Run the documented SSM sequence example

Instantiate the SSM class, pass it a batch of token-index sequences, and inspect the logits shape.

Before you start

  • Python environment with PyTorch available.
  • Install the package with pip install simple-mamba.
  • Use the documented values: vocab_size 10000, embed_dim 256, state_dim 512, and num_layers 2.

Steps

  1. Install SimpleMamba with the command below.

    Documented command
    pip install simple-mamba

  2. Import torch and SSM from simple_mamba, then instantiate SSM(vocab_size, embed_dim, state_dim, num_layers) with the documented values.

  3. Create input_seq with torch.randint(0, vocab_size, (32, 10)).

  4. Call logits = model(input_seq) and print logits.shape.

Expected outcome

The example expects logits shaped `[32, 10, vocab_size]`.

Guide limitations

  • The expected shape is stated in the README but no successful test result is provided.
  • The example does not specify additional environment or version requirements.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
16 October 2026
Original website title
kyegomez/SimpleMamba · Official homepage title · retained in its source language
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