Skip to main content

Pure-TensorFlow lightweight Mamba with multi-dilated causal conv front-end

Project description

lite-mamba

Publish Python Package Tests Documentation

A minimal, pure-TensorFlow implementation of Mamba with a multi-dilated causal depthwise conv front-end. No custom C++ or Triton kernels needed; works seamlessly on CPU, GPU, or TPU with standard TensorFlow ops.


📚 READ THE FULL DOCUMENTATION HERE 📚

Contains architecture details, API references, streaming inference guides, and overviews of the multi-branch variants (TFPTCNMamba, TFSTCNMamba, TFDPWCMamba).


Quick Start

Install

pip install lite-mamba

Basic Usage

from lite_mamba import TFPTCNMamba
import tensorflow as tf

x = tf.random.normal((2, 128, 512))  # (batch, seq, d_model)
m = TFPTCNMamba(d_model=512, d_conv=3, conv_dilations=(1, 2, 4, 8))

y = m(x)
print(y.shape)  # (2, 128, 512)

License

Apache-2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

lite_mamba-1.1.1.tar.gz (6.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

lite_mamba-1.1.1-py3-none-any.whl (6.1 kB view details)

Uploaded Python 3

File details

Details for the file lite_mamba-1.1.1.tar.gz.

File metadata

  • Download URL: lite_mamba-1.1.1.tar.gz
  • Upload date:
  • Size: 6.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.14

File hashes

Hashes for lite_mamba-1.1.1.tar.gz
Algorithm Hash digest
SHA256 1dc7e08d2ea3905a604466c857e794fbcbbfad4ecf475bd84b265c654ba1853e
MD5 46ef4064dac7167d3c87bd5cbb4fded1
BLAKE2b-256 e2fc7fa89be21044cd9803f21faa0d12faea2eb2e75a98d71d6c768d77dff98e

See more details on using hashes here.

File details

Details for the file lite_mamba-1.1.1-py3-none-any.whl.

File metadata

  • Download URL: lite_mamba-1.1.1-py3-none-any.whl
  • Upload date:
  • Size: 6.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.14

File hashes

Hashes for lite_mamba-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 6b5fca2006984520ca9879726568ab0d9c8481d4e2a58c46d3aebd5ff932b49f
MD5 c633afd7aa0a72c25ea67efa356ec555
BLAKE2b-256 360528a62979167401d46cf12284c2ace59b98cd593d750fdc1c656dc734e1ab

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page