semvec/pypi-packages huggingface dataset:
benchmarkLLM 0.1.0
A benchmarking tool for LM Studio inference
Requires Python: >=3.8

# LM Studio Inference Benchmark

This project provides tools to benchmark inference performance for LM Studio models. It measures various metrics including:
- Inference latency
- Tokens per second
- Memory usage
- Response generation time

## Requirements
- Python 3.8+
- LM Studio running locally
- Required Python packages (see requirements.txt)

## Installation
```bash
pip install -r requirements.txt
```

## Usage
```bash
python main.py --model "your_model_name" --prompt "your_test_prompt"
```

## Features
- Measures inference latency across different prompt lengths
- Supports multiple model comparisons
- Generates detailed performance reports
- Memory usage tracking
- Configurable test parameters

## License
MIT License
needto 0.1.9
Ask AI for help you in your terminal.
Requires Python: >=3.8

# Needto

[![PyPI - Version](https://img.shields.io/pypi/v/needto.svg)](https://pypi.org/project/needto)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/needto.svg)](https://pypi.org/project/needto)


Needto have an AI assistant in your command line?

`needto` is an AI-powered assistant for the command line, designed to help you with various tasks such as **finding and running commands**, **answering questions by analyzing command outputs**, and **writing code snippets**. It suggests solutions and makes it easy to apply them in your environment. With `needto`, you can select and run recommended commands safely.

## Installation

To install `needto`, use `pipx`:

```sh
pipx install needto
```

## Configuration

After installation, configure `needto` by running:

```sh
needto config
```

Needto leverages `Groq` to run Meta's Llama3 language models. To get started, you'll need a Groq API key ([Groq](console.groq.com)).

## Usage

### 1. Find and Run a Command

Use `needto do` to find and run commands effortlessly. Simply describe what you need to do, and `needto` will provide a list of CLI commands for you to choose from. You can then edit the command further if necessary before executing it.

**Example:**

```sh
needto do "kill process on port 8000"
```

### 2. Ask Questions

Ask `needto ask` questions about your system. It will help you find answers by suggesting commands to explore and analyzing the outputs of those commands. Note that, in this case, `needto` will prioritize safety and avoid recommending commands that could modify your system state. You always remain in full control of what gets executed on your computer.


**Example:**

```sh
needto ask "how many programs I have installed on my system?"
```

### 3. Write Code

Use `needto write` to generate code snippets. Describe the code you need, and `needto` will provide it along with necessary clarifications.

**Example:**

```sh
needto write "a python script that gets my public IP"
```

## License

`needto` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
TopDR 0.4
Topological Dimension Reduction
Requires Python: >=3.6

# TDR: Topological Dimensional Reduction
This method allows for topological dimensionality reduction on the data. In this method, the columns of the reduced data set are from the original data set.

## Transform 
This function convert data into categories (1,2,3,4,...) based on standard deviation.

## Core
This function finds the core according to topological methods. This core show us how important which column is.

## TDR
This version is primal version, not effective with big amounts data.
It outputs the important column numbers (with index +1) and their importance levels as a list.


## TDR_V2
This version is much faster.
It outputs the important column numbers (with index +1) and their importance levels as a list.

The developer of the code is Kenan Evren BOYABATLI, e-mail: kbybtli@gmail.com
Developer of the system is Dr. Uğur YİĞİT, e-mail: ugur.yigit@medeniyet.edu.tr

## TDR_V3 (available)
This version is much faster.
Rewrote the "transform" function for more stability.

## Citation Importance:

Although our algorithm is open source, it is important that our work is recognized and documented academically. In this way, we can trace the origin of the algorithm and those who contributed to its development. Please refer to the following article when using the algorithm:

Yiğit, Uğur. "The Rough Topology for Numerical Data." arXiv preprint arXiv:2206.05776 (2022). https://doi.org/10.48550/arXiv.2206.05776

Gökhan Kazar, Uğur Yiğit, Kenan Evren Boyabatlı,
Predicting maintenance cost overruns in public school buildings using a rough topological approach,
Automation in Construction,
Volume 168, Part A,
2024,
105810,
ISSN 0926-5805,
https://doi.org/10.1016/j.autcon.2024.105810.

If you have any questions or feedback, please contact us.
errcode 0.1.0
Error Detecting/Correcting Codes

errcode: A Toolbox for Error Correcting/Detecting Codes
=======================

`errcode` is a package for classical error-correcting codes and error-detecting codes.

### API

`max_size(arity:int|list[int]=2, repeat:int=1, direction:int|list[int]=1, correct:int=0, detect:Optional[int]=None) -> int|range`

## Usage Examples

```
from errcode import max_size
print(max_size(arity=2, repeat=6, correct=4))
print(max_size(arity=[5, -3, 4], detect=1))
```
pyliveserver 0.1.3
Un servidor local con recarga automática para desarrollo de sitios estáticos.

# PyLiveServer

1.[description](#description) 2.[dependencies](#dependencies) 3.[how to use](#use)

## description

This is a small script written in python aimed at those who use nvim, helix or the zed code editor on Linux OS.
This will allow you to serve files in the web development process and see the changes in your preferred browser in real time. This file includes the languages ​​commonly used in web development (html, css, js, json, images extensions).

## dependencies

Linux systems already have python3 installed by default.
You just need to use the venv module to create a virtual environment, activate it and install pyliveserver.

## use example

open a terminal in the root directory of your project.

1.[create and activate the virtual environment]

```
python3 -m venv venv
#activate virtual environment
source venv/bin/activate
# you should see (venv) in your terminal
```

2.[install pyliveserver]

```
pip install pyliveserver
#now you can use pyliveserver
pyls
```

3.[try it]

This should open filedialog in the home folder of your system. Select the folder you want to serve and your browser will open. Now you can edit and see the changes in real time!

4.[deactivate virtual environment]

```
#terminal
deactivate
```

don`t forget to add venv in your .gitignore file before commiting!