https://github.com/lamalab-org/chemlift
Language-interfaced fine-tuning for chemistry
https://github.com/lamalab-org/chemlift
chemistry few-shot-learning fine-tuning hacktoberfest llm materials
Last synced: 7 days ago
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Language-interfaced fine-tuning for chemistry
- Host: GitHub
- URL: https://github.com/lamalab-org/chemlift
- Owner: lamalab-org
- License: mit
- Created: 2023-07-10T06:54:07.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-30T10:47:50.000Z (over 1 year ago)
- Last Synced: 2025-04-13T13:14:04.750Z (7 days ago)
- Topics: chemistry, few-shot-learning, fine-tuning, hacktoberfest, llm, materials
- Language: Jupyter Notebook
- Homepage:
- Size: 229 KB
- Stars: 38
- Watchers: 1
- Forks: 6
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Citation: CITATION.cff
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README
chemlift
Chemical language interfaced predictions using large language models.
## 💪 Getting Started
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.
### Few-shot learning
```python
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'])
```
### Fine-tuning
```python
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)
```
## 🚀 Installation
The most recent code and data can be installed directly from GitHub with:
```bash
$ pip install git+https://github.com/lamalab-org/chemlift.git
```
## 👐 Contributing
Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See
[CONTRIBUTING.md](https://github.com/lamalab-org/chemlift/blob/master/.github/CONTRIBUTING.md) for more information on getting involved.
## 👋 Attribution
### ⚖️ License
The code in this package is licensed under the MIT License.
### 📖 Citation
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?}
}
```
### 🎁 Support
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.
## 🛠️ For Developers
See developer instructions
The final section of the README is for if you want to get involved by making a code contribution.
### Development Installation
To install in development mode, use the following:
```bash
$ git clone git+https://github.com/lamalab-org/chemlift.git
$ cd chemlift
$ pip install -e .
```
### 🥼 Testing
After cloning the repository and installing `tox` with `pip install tox`, the unit tests in the `tests/` folder can be
run reproducibly with:
```shell
$ tox
```
Additionally, these tests are automatically re-run with each commit in a [GitHub Action](https://github.com/lamalab-org/chemlift/actions?query=workflow%3ATests).
### 📖 Building the Documentation
The documentation can be built locally using the following:
```shell
$ 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`](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`](docs/source/conf.py).
### 📦 Making a Release
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:
```shell
$ tox -e finish
```
This script does the following:
1. Uses [Bump2Version](https://github.com/c4urself/bump2version) to switch the version number in the `setup.cfg`,
`src/chemlift/version.py`, and [`docs/source/conf.py`](docs/source/conf.py) to not have the `-dev` suffix
2. Packages the code in both a tar archive and a wheel using [`build`](https://github.com/pypa/build)
3. Uploads to PyPI using [`twine`](https://github.com/pypa/twine). Be sure to have a `.pypirc` file configured to avoid the need for manual input at this
step
4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
5. 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.