Awesome
<p align="center"> <img src="assets/hypster_with_text.png" alt="Hypster Logo" width="600"/> </p> </p> <p align="center"> <span style="font-size: 18px;"> <a href="https://gilad-rubin.github.io/hypster/">[Documentation]</a> | <a href="#installation">[Installation]</a> | <a href="#quick-start">[Quick Start]</a> </span> </p>Hypster
is a lightweight configuration system for AI & Machine Learning projects.
It offers minimal, intuitive pythonic syntax, supporting hierarchical and swappable configurations.
Installation
You can install Hypster using pip:
pip install hypster
Quick Start
Here's a simple example of how to use Hypster:
from hypster import HP, config
@config
def my_config(hp: HP):
chunking_strategy = hp.select(['paragraph', 'semantic', 'fixed'], default='paragraph')
llm_model = hp.select({'haiku': 'claude-3-haiku-20240307',
'sonnet': 'claude-3-5-sonnet-20240620',
'gpt-4o-mini': 'gpt-4o-mini'}, default='gpt-4o-mini')
llm_config = {'temperature': hp.number_input(0),
'max_tokens': hp.number_input(64)}
system_prompt = hp.text_input('You are a helpful assistant. Answer with one word only')
Now we can instantiate the configs with our selections and overrides:
results = my_config(final_vars=["chunking_strategy", "llm_config", "llm_model"],
selections={"llm_model" : "haiku"},
overrides={"llm_config.temperature" : 0.5})
Inspiration
Hypster draws inspiration from Meta's hydra and hydra-zen framework. The API design is influenced by Optuna's "define-by-run" API.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the MIT License - see the LICENSE file for details.