semvec/pypi-packages huggingface dataset:
HOPYBOX 2.0.1
An open-source and practical command box.
Requires Python: >=3.8
Keywords: hopybox, box, ho, py, command, tool

# HOPYBOX
## Introduction
An open-source and practical command box, it was made in python language, so I named it `HOPYBOX`.
## Installion
Install with `pip` or your favorite PyPI package manager.
```sh
$ python -m pip install hopybox
```
## Run
Run the following command to run `HOPYBOX` in the terminal:
```sh
$ python -m hopybox
```
or like these
```sh
$ hopybox
```
```python
>>> from hopybox import __main__
>>> __main__.start()
```
## Import all methods
```python
>>> from hopybox import *
```
## Usage
See built-in help in the program
```sh
HOPYBOX/Program:help
```
## Update
Enter in the `HOPYBOX` console:
```sh
HOPYBOX/Program:update
```
or like this
```sh
$ pip install -U hopybox
```
## License
```
Copyright (c) 2022-2025 HOStudio123 (hostudio.hopybox@foxmail.com)

HOPYBOX is an open-source command box released under the GPL Version 3 license. 

This means that you are free to use, distribute, and modify the software, but you must comply with the terms and conditions of the GPL Version 3 license. 

The software is provided free of charge, but WITHOUT ANY WARRANTY, including the implied warranties of MERCHANTABILITY and FITNESS FOR A PARTICULAR PURPOSE. 

You can visit <https://www.gnu.org/licenses/> to view the details of the GPL Version 3 license.
```

## Docs
The [HOPYBOX Documentation](https://hopybox.readthedocs.io) can give you detailed more instructions on how to use it.  

## Stargazers over time
[![Stargazers over time](https://starchart.cc/HOStudio123/HOPYBOX.svg?variant=adaptive)](https://starchart.cc/HOStudio123/HOPYBOX)
saev 0.1.0
Sparse autoencoders for vision transformers in PyTorch
Requires Python: >=3.11

# saev - Sparse Auto-Encoders for Vision

Implementation of sparse autoencoders (SAEs) for vision transformers (ViTs) in PyTorch.

This is the codebase used for our preprint "Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision Models"

* [arXiv preprint](https://arxiv.org/abs/2502.06755)
* [Huggingface Models](https://huggingface.co/collections/osunlp/sae-v-67ab8c4fdf179d117db28195)
* [API Docs](https://osu-nlp-group.github.io/SAE-V/saev)
* [Demos](https://osu-nlp-group.github.io/SAE-V/#demos)

## About

saev is a package for training sparse autoencoders (SAEs) on vision transformers (ViTs) in PyTorch.
It also includes an interactive webapp for looking through a trained SAE's features.

Originally forked from [HugoFry](https://github.com/HugoFry/mats_sae_training_for_ViTs) who forked it from [Joseph Bloom](https://github.com/jbloomAus/SAELens).

Read [logbook.md](logbook.md) for a detailed log of my thought process.

See [related-work.md](saev/related-work.md) for a list of works training SAEs on vision models.
Please open an issue or a PR if there is missing work.

## Installation

Installation is supported with [uv](https://docs.astral.sh/uv/).
saev will likely work with pure pip, conda, etc. but I will not formally support it.

To install, clone this repository (maybe fork it first if you want).

In the project root directory, run `uv run python -m saev --help`.
The first invocation should create a virtual environment and show a help message.

## Using `saev`

See the [docs](https://osu-nlp-group.github.io/SAE-V/saev) for an overview.

I recommend using the [llms.txt](https://osu-nlp-group.github.io/SAE-V/llms.txt) file as a way to use any LLM provider to ask questions.
For example, you can run `curl https://osu-nlp-group.github.io/SAE-V/llms.txt | pbcopy` on macOS to copy the text, then paste it into [https://claude.ai](https://claude.ai) and ask any question you have.