Skip to main content

A toolkit for large scale distributed training

Project description

LASER (a toolkit for Large scAle diStributEd tRaining)

A toolkit for large scale distributed training

With LARSER we succeeded to train DeBERTa 1.5B model without model parallelism. The DeBERTa 1.5B model is the SOAT model on GLUE and SuperGLUE leaderboard. And it's the first model that surpass T5 11B model and human performance on SuperGLUE leaderboard.

TODOs

  • Add documentation and usage examples

git version: 57143200814583410acdd0c5ac0a0f8bab8a1f7e date: 2021-02-04 09:55:12.622124

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

LASER-0.0.5-py3-none-any.whl (22.2 kB view details)

Uploaded Python 3

File details

Details for the file LASER-0.0.5-py3-none-any.whl.

File metadata

  • Download URL: LASER-0.0.5-py3-none-any.whl
  • Upload date:
  • Size: 22.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.25.0 CPython/3.6.9

File hashes

Hashes for LASER-0.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 523847d32ccd26ce703077309e5eddf140cada2290daa12e54d89b6c3ab6f9e4
MD5 3b91d382d87f04453fbab3cdcc3d8ef5
BLAKE2b-256 f94afaebd70e479880c507c11d7318dd670416d53b5d6b6b973fd70ce97d72a7

See more details on using hashes here.

Supported by

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