Chemical language interfaced predictions using large language models.
With ChemLIFT you can use large language models to make predictions on chemical data. You can use two different approaches:
- Few-shot learning: Provide a few examples in the prompt along with the points you want to predict and the model will learn to predict the property of interest.
- Fine-tuning: Fine-tune a large language model on a dataset of your choice and use it to make predictions.
Fine-tuning updates the weights of the model, while few-shot learning does not.
from chemlift.icl.fewshotclassifier import FewShotClassifier
from langchain.llms import OpenAI
llm = OpenAI()
fsc = FewShotClassifier(llm, property_name='bandgap')
# Train on a few examples
fsc.fit(['ethane', 'propane', 'butane'], [0,1,0])
# Predict on a few more
fsc.predict(['pentane', 'hexane', 'heptane'])
from chemlift.finetuning.classifier import ChemLIFTClassifierFactory
model = ChemLIFTClassifierFactory('property name',
model_name='EleutherAI/pythia-1b-deduped').create_model()
model.fit(X, y)
model.predict(X)
The most recent code and data can be installed directly from GitHub with:
$ pip install git+https://github.com/lamalab-org/chemlift.git
Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.
The code in this package is licensed under the MIT License.
Citation goes here!
@article{Jablonka_2023,
doi = {10.26434/chemrxiv-2023-fw8n4},
url = {https://doi.org/10.26434%2Fchemrxiv-2023-fw8n4},
year = 2023,
month = {feb},
publisher = {American Chemical Society ({ACS})},
author = {Kevin Maik Jablonka and Philippe Schwaller and Andres Ortega-Guerrero and Berend Smit},
title = {Is {GPT}-3 all you need for low-data discovery in chemistry?}
}
The work of the LAMALab is supported by the Carl-Zeiss foundation.
In addition, the work was supported by the MARVEL National Centre for Competence in Research funded by the Swiss National Science Foundation (grant agreement ID 51NF40-182892). In addition, we acknoweledge support by the USorb-DAC Project, which is funded by a grant from The Grantham Foundation for the Protection of the Environment to RMI’s climate tech accelerator program, Third Derivative.
See developer instructions
The final section of the README is for if you want to get involved by making a code contribution.
To install in development mode, use the following:
$ git clone git+https://github.com/lamalab-org/chemlift.git
$ cd chemlift
$ pip install -e .
After cloning the repository and installing tox
with pip install tox
, the unit tests in the tests/
folder can be
run reproducibly with:
$ tox
Additionally, these tests are automatically re-run with each commit in a GitHub Action.
The documentation can be built locally using the following:
$ git clone git+https://github.com/lamalab-org/chemlift.git
$ cd chemlift
$ tox -e docs
$ open docs/build/html/index.html
The documentation automatically installs the package as well as the docs
extra specified in the setup.cfg
. sphinx
plugins
like texext
can be added there. Additionally, they need to be added to the
extensions
list in docs/source/conf.py
.
After installing the package in development mode and installing
tox
with pip install tox
, the commands for making a new release are contained within the finish
environment
in tox.ini
. Run the following from the shell:
$ tox -e finish
This script does the following:
- Uses Bump2Version to switch the version number in the
setup.cfg
,src/chemlift/version.py
, anddocs/source/conf.py
to not have the-dev
suffix - Packages the code in both a tar archive and a wheel using
build
- Uploads to PyPI using
twine
. Be sure to have a.pypirc
file configured to avoid the need for manual input at this step - Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
- Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can
use
tox -e bumpversion -- minor
after.