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-0.0.47.tar.gz (124.9 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-0.0.47-py3-none-any.whl (147.9 kB view details)

Uploaded Python 3

File details

Details for the file nlr_elm-0.0.47.tar.gz.

File metadata

  • Download URL: nlr_elm-0.0.47.tar.gz
  • Upload date:
  • Size: 124.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for nlr_elm-0.0.47.tar.gz
Algorithm Hash digest
SHA256 ee32ad42f03ff2c709fa895670890205f9af5a9921b872adca829fa02aeadc7f
MD5 d2c184c67ed91d7bdd56b2a4e8347032
BLAKE2b-256 1003014c0002f17cc16d4a3f359d1043a10e94ab8eb58a474d596ffa4ea9b15c

See more details on using hashes here.

Provenance

The following attestation bundles were made for nlr_elm-0.0.47.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-0.0.47-py3-none-any.whl.

File metadata

  • Download URL: nlr_elm-0.0.47-py3-none-any.whl
  • Upload date:
  • Size: 147.9 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-0.0.47-py3-none-any.whl
Algorithm Hash digest
SHA256 1739c00b4aa440032b2093ad63bc0f26fdd09e705e43ab3d0d52f73ec7aa0a8d
MD5 2c7182f694ccbe4fdcc6495106656fcf
BLAKE2b-256 7bcf7b9f1d202722b3e491870c8a903098bbf193038713d67de675cae6ee3c4f

See more details on using hashes here.

Provenance

The following attestation bundles were made for nlr_elm-0.0.47-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

0.0.48

2 files

This release

0.0.47 This release

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