semvec/pypi-packages huggingface dataset:
soundtools 0.2.3.4
used to work with sounds waves
Requires Python: >=3.6

made by Mohammad Erfan Karami
github: https://github.com/erfan-ops

version: 0.2.3.4

this package is used to create, play and save sound files
it has some basic sound waves although you can add your own and modify the package.

it stores the sound waves as numpy arrays and uses pyaudio for playback
and uses matplotlib to visualize the waves
seroup 0.2.6
CLI tool to manage sero servers
Requires Python: <4.0,>=3.12

# Seroup

Seroup is a CLI management tool for [sero](https://github.com/clowzed/sero) servers, allowing you to easily upload and manage site on this server.
The last version of this tool is guaranteed to work with latest stable release of sero server.

## Table of Contents

- [Seroup](#seroup)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [Usage](#usage)
  - [Commands](#commands)
  - [Configuration](#configuration)

## Installation

To install `seroup`, use pip:

```sh
pip install seroup
```

## Usage

After installation, you can use the seroup command followed by various subcommands.

```sh
seroup <command> <directory (default: current directory)>
```

## Commands

| Command        | Description                                       |
| -------------- | ------------------------------------------------- |
| `init`         | Create default `sero.toml` in specified directory |
| `enable`       | Enable site                                       |
| `disable`      | Disable site                                      |
| `download`     | Download full site as zip                         |
| `upload`       | Upload site                                       |
| `teardown`     | Teardown site                                     |
| `ucors`        | Update CORS settings without reuploading site     |
| `registration` | Register a user in sero server                    |

## Configuration

The tool uses a configuration file named `sero.toml` to manage site settings. Below is an example configuration:

```toml
[server]
url = "http://mysite.ru"
subdomain = "blog"

[cors]
origins = []

[credentials]
login = "clowzed"
# For security reasons
# you'd better keep this empty
password = ""
```

Configuration Sections

- [server]: Contains the server URL and subdomain.
  - url: The base URL of the server.
  - subdomain: The subdomain for the site.
- [cors]: Contains CORS settings.
  - origins: A list of allowed origins for CORS.
- [credentials]: Contains login credentials.
  - login: The login username.
  - password: The login password.
moment-api 1.0.0
Moment API: Доступ к LLAMA3 без нагрузки на ваше железо
Requires Python: >=3.6

# Moment API: Доступ к LLAMA3 без нагрузки на ваше железо

Moment API - это удобный способ получить доступ к мощной нейросети LLAMA3, не требующий значительных ресурсов вашего компьютера. 
Наш API ключ позволяет вам использовать возможности LLAMA3 для генерации текста и изображений, не беспокоясь о нагрузке на ваше железо.

## Что вы можете делать с Moment API

* **Генерировать текст:**  Создавайте увлекательные истории,  поэзию, сценарии и другие виды текстов.
* **Переводить текст:** Переводите  тексты  между  разными  языками.
* **Генерировать изображения:** Создавайте  удивительные  изображения  на  основе  текстовых  описаний.
* **Создавать  искусство:**  Создавайте  уникальные  произведения  искусства  с  помощью  LLAMA3.

## Преимущества Moment API

* **Легко в использовании:** наш API прост в интеграции и использовании, даже если вы не являетесь опытным разработчиком
* **Экономия ресурсов:** вы не нужно тратить ресурсы на обучение и запуск нейросети на вашем компьютере
* **Высокая производительность:** наш API обеспечивает высокую производительность и быстрый ответ на ваши запросы

## Как начать использовать Moment API

1. **Получите наш API ключ:** https://t.me/MomentAPI_bot
2. **Установите нашу библиотеку для Python:**
   ```bash
   pip install moment_api
one-line-llm-tuner 0.0.15
A Large Language Model Fine-tuning package. The package uses a single line to fine-tune an LLM by taking care of all the boilerplate in the backend.
Requires Python: >=3.10

# 🔥 One Line LLM Tuner 🔥

Fine-tune any Large Language Model (LLM) available on [Hugging Face](https://www.huggingface.co) in a single line. It is created by [Suhas Bhairav](https://www.metriccoders.com).

## Overview

`one-line-llm-tuner` is a Python package designed to simplify the process of fine-tuning large language models (LLMs) like GPT-2, Llama-2, GPT-3 and more. With just one line of code, you can fine-tune a pre-trained model to your specific dataset. Consider it as a wrapper for `transformers` library, just like how `keras` is for `tensorflow`.

## Features

- **Simple**: Fine-tune models with minimal code.
- **Supports Popular LLMs**: Works with models from the `transformers` library, including GPT, BERT, and more.
- **Customizable**: Advanced users can customize the fine-tuning process with additional parameters.

## Installation

You can install `one-line-llm-tuner` using pip:

```bash
pip install one-line-llm-tuner
```

## Usage

The PyPI package can be used in the following way after installation.

```bash
from one_line_llm_tuner.tuner import llm_tuner

fine_tune_obj = llm_tuner.FineTuneModel()

fine_tune_obj.fine_tune_model(input_file_path="train.txt")

fine_tune_obj.predict_text("Elon musk founded Spacex in ")
```

If you want to modify the default values such as type of model used, tokenizer and more, use the following code.

```bash
from one_line_llm_tuner.tuner import llm_tuner

fine_tune_obj = llm_tuner.FineTuneModel(model_name="gpt2",
                 test_size=0.3,
                 training_dataset_filename="train_dataset.txt",
                 testing_dataset_filename="test_dataset.txt",
                 tokenizer_truncate=True,
                 tokenizer_padding=True,
                 output_dir="./results",
                 num_train_epochs=2,
                 logging_steps=500,
                 save_steps=500,
                 per_device_train_batch_size=128,
                 per_device_eval_batch_size=128,
                 max_output_length=100,
                 num_return_sequences=1,
                 skip_special_tokens=True,)

fine_tune_obj.fine_tune_model(input_file_path="train.txt")

fine_tune_obj.predict_text("Elon musk founded Spacex in ")
```


## Contributing
We welcome contributions! Please see the [contributing guide](Contributing.md) for more details.

## License
This project is licensed under the terms of the MIT license. See the [LICENSE](LICENSE.txt) file for details.
bananagan 0.2.0
Python library for generating synthetic images of banana diseases using Generative Adversarial Networks (GANs)
Requires Python: <4.0,>=3.10

# BananaGan

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Description

BananaGan is a Python library for generating synthetic images of banana diseases using Generative Adversarial Networks (GANs). It provides a simple API to access the [Pix2PixHd](https://github.com/NVIDIA/pix2pixHD) pre-trained models for different banana parts and disease types.

## Installation

To install BananaGan, you can use pip:

```bash
pip install bananagan
```

## Usage

Here are some examples of how to use BananaGan:

**1. Generate an image of a pseudostem with Xanthomonas wilt:**

```python
from bananagan import *
from PIL import Image
from matplotlib import pyplot as plt

if __name__ == '__main__':
    image_path = "./../images/pseudostem-healthy.png"
    image_color = Image.open(image_path)
    model = BananaGan.get_model(PseudostemModels.xanthomonas_wilt)
    generated_image = model(input_image=image_color, block_size=13, c=1)

    fig = plt.figure(figsize=(10, 10))
    ax = fig.add_subplot(1, 2, 1)
    ax.imshow(image_color)
    ax.axis('off')
    ax.set_title('Original Image')

    ax = fig.add_subplot(1, 2, 2)
    ax.imshow(generated_image)
    ax.axis('off')
    ax.set_title('Generated Image')

    plt.tight_layout()
    plt.show()
```

![Pseudostem with Xanthomonas wilt](https://raw.githubusercontent.com/haruiz/bananagan/main/images/plot1.png)


**2. Generate images with different parameters:**

```python
from bananagan import *
from PIL import Image
from matplotlib import pyplot as plt

if __name__ == '__main__':
    image_path = "./../images/pseudostem-healthy.png"
    image_color = Image.open(image_path)
    model = BananaGan.get_model(PseudostemModels.xanthomonas_wilt)

    images = []
    block_size = [3, 5, 7, 9, 11, 13]
    for bsz in block_size:
        generated_image = model(input_image=image_color, block_size=bsz, c=1)
        images.append(generated_image)

    fig = plt.figure(figsize=(10, 5))
    for i, generated_image in enumerate(images):
        ax = fig.add_subplot(1, len(images), i+1)
        ax.imshow(generated_image)
        ax.axis('off')
    plt.tight_layout()
    plt.show()
```

![Pseudostem with Xanthomonas wilt](https://raw.githubusercontent.com/haruiz/bananagan/main/images/plot2.png)

## Available Models

**Pseudostem Models:**

* `PseudostemModels.healthy`: Generates healthy pseudostems.
* `PseudostemModels.xanthomonas_wilt`: Generates pseudostems with Xanthomonas wilt.
* `PseudostemModels.fusarium_wilt`: Generates pseudostems with Fusarium wilt.

**Rachis Models:**

* `RachisModels.healthy`: Generates healthy rachis.
* `RachisModels.banana_blood_disease`: Generates rachis with banana blood disease.

## License

This project is licensed under the MIT License.