Skip to main content

XLM Framework

Project description

XLM is a unified framework for developing and comparing small non-autoregressive language models. It uses PyTorch as the deep learning framework, PyTorch Lightning for training utilities, and Hydra for configuration management. XLM provides core components for flexible data handling and training, useful architectural implementations for non-autoregressive workflows, and support for arbitrary runtime code injection. Custom model implementations that leverage the core components of xlm can be found in the xlm-models package. The package also includes a few preconfigured synthetic planning and language-modeling datasets.

Usage:

pip install xlm-core

Command usage:

xlm job_type=[JOB_TYPE] job_name=[JOB_NAME] experiment=[CONFIG_PATH]

The job_type argument can be one of train ,eval and generate. The experiment argument should point to the root hydra config file.

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

xlm_core-0.1.4a0.tar.gz (140.3 kB view details)

Uploaded Source

Built Distribution

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

xlm_core-0.1.4a0-py3-none-any.whl (200.4 kB view details)

Uploaded Python 3

File details

Details for the file xlm_core-0.1.4a0.tar.gz.

File metadata

  • Download URL: xlm_core-0.1.4a0.tar.gz
  • Upload date:
  • Size: 140.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.2

File hashes

Hashes for xlm_core-0.1.4a0.tar.gz
Algorithm Hash digest
SHA256 9ee9e5960456ac66c24501af3d27057e16f737cf1e5d27184270c1c2c30ee2f9
MD5 f4322ca1ccdd2d0528e8943773be02e8
BLAKE2b-256 ac9619707e6325c5374dc55ed4db49fe075cac492caa36a31761a16dce00027c

See more details on using hashes here.

File details

Details for the file xlm_core-0.1.4a0-py3-none-any.whl.

File metadata

  • Download URL: xlm_core-0.1.4a0-py3-none-any.whl
  • Upload date:
  • Size: 200.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.2

File hashes

Hashes for xlm_core-0.1.4a0-py3-none-any.whl
Algorithm Hash digest
SHA256 b8e40fd43e976a731f294f2a33e934bdd26b0b899ce3b11221a4f66b3ba1ead4
MD5 d5959898021de6f593a66bf634a149e0
BLAKE2b-256 6ca21989d7ab3ed832b8668c030c612565c81c9270fc93584c6c5a68368d3cb7

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