Skip to main content
https://github.com/NatLabRockies/elm/workflows/Documentation/badge.svg https://github.com/NatLabRockies/elm/workflows/pytests/badge.svg https://github.com/NatLabRockies/elm/workflows/Lint%20Code%20Base/badge.svg https://img.shields.io/pypi/pyversions/NLR-elm.svg https://badge.fury.io/py/NLR-elm.svg https://zenodo.org/badge/690793778.svg

The Energy Language Model (ELM) software provides interfaces to apply Large Language Models (LLMs) like ChatGPT and GPT-4 to energy research. For example, you might be interested in:

Installing ELM

NOTE: If you are installing ELM to run ordinance scraping and extraction, see the ordinance-specific installation instructions.

Option #1 (basic usage):

  1. pip install NLR-elm

Option #2 (developer install):

  1. from home dir, git clone git@github.com:NatLabRockies/elm.git

  2. Create elm environment and install package
    1. Create a conda env: conda create -n elm

    2. Run the command: conda activate elm

    3. cd into the repo cloned in 1.

    4. Prior to running pip below, make sure the branch is correct (install from main!)

    5. Install elm and its dependencies by running: pip install . (or pip install -e . if running a dev branch or working on the source code)

Acknowledgments

This work was authored by the National Laboratory of the Rockies, operated by Alliance for Energy Innovation, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the DOE Wind Energy Technologies Office (WETO), the DOE Solar Energy Technologies Office (SETO), and internal research funds at the National Laboratory of the Rockies. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Download files

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

Source Distribution

nlr_elm-0.0.48.tar.gz (125.0 kB view details)

Uploaded Source

Built Distribution

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

nlr_elm-0.0.48-py3-none-any.whl (148.1 kB view details)

Uploaded Python 3

File details

Details for the file nlr_elm-0.0.48.tar.gz.

File metadata

  • Download URL: nlr_elm-0.0.48.tar.gz
  • Upload date:
  • Size: 125.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for nlr_elm-0.0.48.tar.gz
Algorithm Hash digest
SHA256 02b787d200a3272e5803c38556ae526db2339d8a4610f4cbc94c47833c048285
MD5 698ee6fda3d7adc76b1a68892902583c
BLAKE2b-256 4eba0f2eb52a85ee5ebe329f0adf76168195a29cb7a860f5fa1eb0118149e136

See more details on using hashes here.

Provenance

The following attestation bundles were made for nlr_elm-0.0.48.tar.gz:

Publisher: publish_to_pypi.yml on NatLabRockies/elm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file nlr_elm-0.0.48-py3-none-any.whl.

File metadata

  • Download URL: nlr_elm-0.0.48-py3-none-any.whl
  • Upload date:
  • Size: 148.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for nlr_elm-0.0.48-py3-none-any.whl
Algorithm Hash digest
SHA256 d2e0c4729934960297437b76c3392ba3921aaff788224fca85f2a561782f814e
MD5 9a9c3336304884ba4967e07193f47080
BLAKE2b-256 aa997ad2c37a4d7d0e5173888ede7b08b15d6e09e896ced6d3606d11e0cf4e9f

See more details on using hashes here.

Provenance

The following attestation bundles were made for nlr_elm-0.0.48-py3-none-any.whl:

Publisher: publish_to_pypi.yml on NatLabRockies/elm

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.0.50

2 files

0.0.49

2 files

This release

0.0.48 This release

2 files

0.0.47

2 files

0.0.46

2 files

0.0.45

2 files

0.0.44

2 files

0.0.43

2 files

0.0.42

2 files

0.0.41

2 files

0.0.40

2 files

0.0.39

2 files

0.0.38

2 files

0.0.37

2 files

0.0.36

2 files

0.0.35

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page