<p align="center">
<picture>
<source srcset="assets/hypster_white_text.png" media="(prefers-color-scheme: dark)">
<img src="assets/hypster_with_text.png" alt="Hypster Logo" width="600"/>
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<p align="center">
<span style="font-size: 18px;">
<a href="https://gilad-rubin.gitbook.io/hypster">[Documentation]</a> |
<a href="https://gilad-rubin.gitbook.io/hypster/getting-started/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:
```bash
pip install hypster
```
## Quick Start
Here's a simple example of how to use Hypster:
```python
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(0),
'max_tokens': hp.number(64)}
system_prompt = hp.text('You are a helpful assistant. Answer with one word only')
```
Now we can instantiate the configs with our values:
```python
results = my_config(final_vars=["chunking_strategy", "llm_config", "llm_model"],
values={"llm_model" : "haiku", "llm_config.temperature" : 0.5})
```
## Inspiration
Hypster draws inspiration from Meta's [hydra](https://github.com/facebookresearch/hydra) and [hydra-zen](https://github.com/mit-ll-responsible-ai/hydra-zen) framework.
The API design is influenced by [Optuna's](https://github.com/optuna/optuna) "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](LICENSE) file for details.
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