semvec/pypi-packages huggingface dataset:
lmcats 0.2.0
lmcats: Advanced metaheuristic optimization with reporting
Requires Python: >=3.6

# lmcats: Advanced Matrix Optimization Library

## Key Features

### 1. Multi-Algorithm Benchmarking
Compare CATS against Genetic Algorithms, Random Search, and Nearest Neighbor:

```python
comparison_results = compare_algorithms(
    objective_function=your_cost_function,
    products=your_products,
)
simple-calc-for-api-and-other 0.1.0
A simple TDD-based calculator core with precision settings
Requires Python: >=3.7

# Calculator Core Package
This is a study project implementing TDD and Layered Architecture.
## Installation
```bash
pip install -i [https://test.pypi.org/simple/](https://test.pypi.org/simple/) simple_calc_for_api_and_other
multi-workspace 3.0.5
Multi
Requires Python: >=3.9

# multi

`multi` is the best way to work with VS Code/Cursor on multiple Git repos at once. It is an alternative to [multi-root workspaces](https://code.visualstudio.com/docs/editing/workspaces/multi-root-workspaces) that offers more flexibility and control. With `multi`, you can gain control over how tasks, debug runnables, and various IDE and linter settings are combined from multiple project repos ("sub-repos") located in the same folder.

Features:

- Generates files in your root `.vscode` folder from sub-repo `launch.json`, `tasks.json`, and `settings.json` files.
- Generates `CLAUDE.md` files from Cursor rules.

## Installation

### Using `pipx`:

- Install [pipx](https://github.com/pypa/pipx)
- Run `pipx install multi-workspace`

### Using `uv`

- Install [uv](https://docs.astral.sh/uv/getting-started/installation/)
- Run `uv tool install multi-workspace`

## Getting started

To get started, create a new workspace directory that will house all your related repos and run:

```
multi init
```

When prompted, paste in the URLs of all the repositories you want to have in your workspace. You can optionally specify descriptions of what they do, which will be used to create a new repo-directories.mdc Cursor/Claude rule.

It is recommended you also install the [VS Code Extension](https://marketplace.visualstudio.com/items?itemName=montaguegabe.multi-sync) that automatically keeps your project synced when edits are made to synced files. To manually sync, you can run `multi sync`.
byond-topic 0.1.2
Sending Topic packets to BYOND servers, and receive responses
Requires Python: >=3.8

# byond.topic

A python module for sending packets to BYOND servers, for calling
`/world/Topic()` and receiving a response. In the same vein as
[BYOND.TopicSender](https://github.com/Cyberboss/BYOND.TopicSender),
but in python!

## Installation

Open the directory, and `pip install .`!

## Exports
### `send(address, port, query)`

Sends a Topic() packet to the specified server, and (ideally) returns
the response from the server.
* `address`: The address (IP, or DNS) of the target DreamDaemon
  instance to send the Topic() packet to.
* `port`: Port that the DreamDaemon instance is serving the world on
* `query`: Query string to be sent. This is an urlencoded series of
  keys (and, where required, values.)
  (...Yeah. it really is just an url query string.)

Returns: A tuple containing the response-type, and the response from
the server.

If the response-type is `TOPIC_RESPONSE_STRING`, the response will be
a dict of key-value pairs, parsed out from the URL query-format string
returned from the server.

The actual data returned from the server depends on the codebase that
the server is running, and the query that was sent.

If the response-type is `TOPIC_RESPONSE_FLOAT`, the response will be a
floating point numeric value.

(`send` automatically opens a socket, transmits the packet, receives
the reply, and then closes the socket again.)

### `queryStatus(address, port)`

Sends a Topic() packet to the specified server, querying the `?status`
endpoint. This returns a string of JSON, or `None` for an invalid
response.

### `queryPlayerCount(address, port)`

Sends a Topic() packet to the specified server, querying the
`?playing` endpoint. This should return the number of
currently-connected players, or `None` for an invalid response.

### Constants

* `TOPIC_PACKET_ID`: The signature that identifies a Topic() packet.
* `TOPIC_RESPONSE_STRING`: Response-type: string
* `TOPIC_RESPONSE_FLOAT`: Response-type: float/numeric
* `MALF_PACKET_ID`: Packet-id that identifies that the server is
  unhappy with the request you just sent.

## Examples

### Requesting World Status

```python

import byond.topic as Topic

responseType, responseData = Topic.send("localhost", 26200, '?status')
if(responseType == Topic.TOPIC_RESPONSE_STRING):
    print(f"We're currently playing a {responseData['mode'][0]} round on {responseData['map_name'][0]}, with {responseData['players'][0]} players!")
```

(The values available in your status response data depend on the
codebase you're querying. Try it and see, first!)
cmatlib 0.1.1
Синхронная библиотека для работы с ChatGPT с историей сессии

# chatinpylib

Простая библиотека для работы с ChatGPT.

## Установка 
```bash
pip install cmatlib==0.1.0
```
## Пример использования
```bash
from chatinpylib import Chat
chat = Chat(token="YourToken")
condition = True
while condition:
    user_request = input('Введите свой запрос:')
    print(chat.ask(user_request))
    choice = input('Вы хотите продолжить? y/n:')
    if choice == 'y':
        condition = True
    elif choice == 'n':
        condition = False
qwen-mt-tool 0.2.0
A CLI tool for translating markdown files using Qwen-MT
Requires Python: >=3.10

# Qwen-MT Tool

A CLI tool for translating markdown files using Qwen-MT (via OpenAI compatible API).

## Features

- **Recursive Scanning**: Automatically finds all `.md` files in a directory.
- **Dependency Awareness**: Skips files that have already been translated (ending in `_zh_CN.md`).
- **Concurrency**: Translates multiple files in parallel for faster processing.
- **Smart Splitting**: Splits long documents into chunks to avoid token limits while preserving code blocks.
- **Usage Tracking**: Logs token usage to `usage.log`.

## Installation

```bash
pip install qwen-mt-tool
```

## Usage

### Prerequisites

You need a DashScope API Key. You can set it as an environment variable or pass it via command line.

```bash
export DASHSCOPE_API_KEY="sk-..."
```

### Basic Usage

Translate a single file:

```bash
qwen-mt path/to/file.md
```

Translate all markdown files in a directory (recursive):

```bash
qwen-mt path/to/directory
```

### Options

- `--workers <N>`: Number of concurrent workers (default: 1).
- `--api-key <KEY>`: Explicitly provide the API key.

```bash
qwen-mt . --workers 4 --api-key "sk-..."
```

## Output

Translated files will be created in the same directory with a `_zh_CN.md` suffix.
For example, `README.md` -> `README_zh_CN.md`.
zarx 0.2.4
Zero-to-AGI Deep Learning Framework
Requires Python: >=3.8

# ZARX - Zero-to-AGI Deep Learning Framework

ZARX provides a clean, production-ready interface for deep learning, focusing on advanced architectures like IGRIS.

## Features

- Explicit model selection (IGRIS_277M, IGRIS_7B, etc.)
- Efficient data pipeline with binary formats
- Comprehensive tokenizer system
- Resume/continue training support
- Production-grade error handling

## Installation

```bash
pip install zarx
```

## Quick Start

```python
import zarx

# 1. Load model
model = zarx.IGRIS_277M()

# 2. Load tokenizer
tokenizer = zarx.load_pretrained('zarx_32k')

# 3. Convert data
zarx.txt_to_bin('train.txt', 'train.bin', tokenizer, max_length=2048)

# 4. Load data
data = zarx.load_from_bin('train.bin', batch_size=32)

# 5. Train
trainer = zarx.train(model, data, epochs=10)
trainer.train()
```

## Documentation

For detailed documentation, visit the [GitHub repository](https://github.com/Akik-Forazi/zarx.git).

## License

This project is licensed under the MIT License.