semvec/pypi-packages huggingface dataset:
real-llama-cpp-python 0.1.6
A simple custom LLM wrapper for llama.cpp with LangChain compatibility.
Requires Python: >=3.10

# real-llama-cpp-python
A simple custom wrapper for llama.cpp models in Python, support seamlessly interaction with LangChain. As the name suggests, it is truly a wrapper for llama.cpp, you should have installed llama.cpp into your machine prior to this.

## Background

While it might seem intuitive that llama-cpp-python would seamlessly integrate with llama.cpp, the reality is that installing llama-cpp-python is a pain in the bum. It is NOT simple as described `pip install llama-cpp-python`.  

Langchain, by default, supports [llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp/). If you have a pipeline that works with Langchain, it would be very difficult to run the latest quantized model (gguf files downloaded from HuggingFace) from llama.cpp. The real-llama-cpp-python is a simplified and an alternative library designed to seamlessly integrate with LangChain while avoiding the pain of installing the library llama-cpp-python. 

llama.cpp can be installed or built easily. (If you are facing the challenge by the time you install llama.cpp, the llama.cpp community is also very active that any issue can be resolved in a few days (unlike llama-cpp-python).  

## Installation
As the name said, it is truly a wrapper for llama.cpp, you should first install llama.cpp into your machine. 

### 1 Install llama.cpp first

- **Highly recommend this method** Clone [llama.cpp](https://github.com/ggerganov/llama.cpp/tree/master) repository and build locally, see [how to build](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
- On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](https://github.com/ggerganov/llama.cpp/blob/master/docs/install.md). Noted that, this brew install may not support GPU.
- Use a ``llama.cpp`` Docker image, see [documentation for Docker](https://github.com/ggerganov/llama.cpp/blob/master/docs/docker.md). 
- **Have not tested yet** Download pre-built binaries from [releases](https://github.com/ggerganov/llama.cpp/releases).

After successfully built/installed llama.cpp. You want to add llama.cpp directory to your `PATH` permanently by editing your shell configuration file:
```
vim ~/.bashrc 
```
or
```
source ~/.zshrc
```
Add the following line: 
```
export PATH=$PATH:/path/to/your/llama.cpp/build/bin
```

Save and run `source ~/.bashrc`  or `source ~/.zshrc`
You should be able to run `llama-cli` and `llama-server` from any directory. Verify the accessibility, run the following commands in any directory 

```
llama-cli --help
llama-server --help
```

### 2 Install llama.cpp
Now, you can install real-llama-cpp-python by either install the stable version through `pip install`
```bash
pip install real-llama-cpp-python
```
or clone the github repository for the developing features.

```
git clone https://github.com/minhtran1309/real-llama-cpp-python.git
cd real-llama-cpp-python
pip install -e .
```

### 3 Running the tests
weatherpy-apiwrapper 1.0.0
Un wrapper para la API de OpenWeatherMap que obtiene el clima actual y el pronóstico del tiempo.
Requires Python: >=3.6

![WeatherPy Logo](docs/media/Weatherpy.png)

WeatherPy is a wrapper for the OpenWeatherMap API that simplifies obtaining current weather data and forecasting weather. It is designed to be easy to use, with a command line interface (CLI) and support for automatic location detection.

## Main Features

- **Current Weather**: Get the current temperature, humidity, and weather description for a specific city or your automatic location.
- **Weather Forecast**: Get the weather forecast for the next few days.
- **Automatic Location Detection**: Detects the user's location using their IP.
- **Visualization Friendly**: Use the `rich` library to display data in formatted and colored tables.
- **Easy Configuration**: Configure your OpenWeatherMap API Key easily.

## Requirements

- Python 3.6 or higher.
- An API Key of [OpenWeatherMap](https://openweathermap.org/api).

## Facility

Install WeatherPy using pip:

```bash

pip install weatherpy

```

## Basic Use

### Configure your API Key:

The first time you use WeatherPy, you will be asked to enter your OpenWeatherMap API Key.

#### Get the Current Weather:

**Current Weather:**

```bash
┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓
┃ Ciudad   ┃ Temperatura (°C) ┃ Humedad (%)┃ Descripción  ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩
│ Madrid   │ 15.0             │ 60         │ Cielo claro  │
└──────────┴──────────────────┴────────────┴──────────────┘
```

**Weather forecast:**

```bash
┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓
┃ Fecha                ┃ Temperatura (°C) ┃ Humedad (%)┃ Descripción  ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩
│ 2023-10-01 12:00:00 │ 16.0             │ 55         │ Cielo claro  │
│ 2023-10-01 15:00:00 │ 18.0             │ 50         │ Sol          │
└──────────────────────┴──────────────────┴────────────┴──────────────┘
```


## Contribution

Contributions are welcome! If you want to improve WeatherPy, open an issue or submit a pull request.

## License

This project is under the MIT license. See the LICENSE file for more details.
lipidoz 1.3.3
Python utility for determination of lipid double bond position location from OzID data
Requires Python: >=3.12

# lipidoz
Python utility for determination of lipid double bond position location from OzID data

#### Contributors:
- Dylan Ross (dylan.ross@pnnl.gov)
- Aivett Bilbao (aivett.bilbao@pnnl.gov)
- Joon-Yong Lee (junyoni@gmail.com)
- Xueyun Zheng (xueyun.zheng@pnnl.gov)

<hr>


Documentation is available at https://lipidoz.readthedocs.io/


<hr>

Beta Release (with GUI executables) available at: https://github.com/PNNL-m-q/lipidoz/releases/tag/v1.0.0

<hr>

Demonstration of GUI application with data for Ultimate SPLASH lipid standards available here: https://zenodo.org/record/7636522
image-processing-rafael-dias-campos 0.0.1
Image processing Package using Skimage
Requires Python: >=3.8

# package_name

Description. 
The package package_name is used to:
	Processing:
		- Histogram matching
		- Structural similarity
		- Resize image
	Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```


## Author
Rafael Dias Campos

## License
[MIT](https://choosealicense.com/licenses/mit/)
li-memcached-client 0.2.3
Package that helps to get memcached iteractions
Requires Python: >=3.11
Keywords: li, memcached, stats

# li-memcached-client

[![CircleCI](https://dl.circleci.com/status-badge/img/gh/liniribeiro/li-memcached-client/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/gh/liniribeiro/li-memcached-client/tree/main)

li-memcached-client is a Python library for connecting into memcached, list, add, get and delete keys.

## Installation
To install the li-memcached-client package, use the following command:

```
pip3 install li-memcached-client
```

## Usage 
```
from li_memcached_client import LiMemcachedClient
client = LiMemcachedClient()
```
By default, it connects to localhost on port 11211. If you need to specify a host and/or port:

```
client = LiMemcachedClient(host='1.2.3.4', port='11211')
```

## Local Development
For local development, follow these steps:

1. Build the project:
```
poetry build
```
2. Install the project:
```
poetry install
```
3. Install twine for publishing:
```
python3 -m pip install --upgrade twine
```

## Contributing
Contributions are welcome! Please open an issue or submit a pull request on GitHub.  

## Contact
For any questions or issues, please contact Alini Ribeiro at aliniribeiroo@gmail.com. 


This documentation provides an overview of the li-memcached-stats library, including installation, configuration, usage, and development instructions.
genetigraph 0.1.0
A novel graph-based implementation for probabilistic pedigree analysis.
Requires Python: >=3.8

# Pedigree Charts and Linked Lists

This project implements a linked-list based data structure and algorithms for an active memory representation of a pedigree chart. It is designed to handle incomplete pedigrees effectively.

## Idea

The concept centers on probabilistic expectations for pedigree analysis. While inherited pedigrees can be calculated easily with known parental genotypes, challenges arise when parental information is missing. This program models the inheritance process mathematically, offering an implementation of that theory along with a user-friendly interface suitable for use by healthcare professionals.

## Features

- **Linked List Structure**: Efficiently represents complex pedigree relationships.
- **Incomplete Data Handling**: Supports analysis even when parental genotypes are unknown.
- **Probabilistic Inheritance Modeling**: Uses mathematical formulas to predict inherited traits.
- **User-Friendly Interface**: Designed with healthcare professionals in mind for ease of use.

## Installation

To install the project, follow these steps:

1. Clone the repository:

   ```bash
   git clone https://github.com/nakulbhat/PedigreePredict.git

2. Build the project:

   ```bash
   make

3. Run Unit Tests:

   ```bash
   make test

4. Clean after make

   ```bash
   make clean

5. Run the Program

   ```bash
   ./PedigreePredict 

## Authorship

This project is being built and maintained by Nakul Bhat.

**Affiliation**: Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India - 576104

**Contact**:
Please Reach out to me for queries and contributions.

- <nakul1.mitmpl2023@learner.manipal.edu>

## Contribution Guidelines

Contributions are the lifeblood of the open source community. Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature/YourFeature`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add your feature'`).
5. Push to the branch (`git push origin feature/YourFeature`).
6. Open a pull request.

## FAQs / Troubleshooting

**Q: I encountered an error while running the tests. What should I do?**
A: Ensure all dependencies are installed and try running `make clean` before rebuilding. If the error still persists, then raise an issue with instructions to recreate the issue.

## Project Roadmap

- Develop a custom pedigree format and implement a file handler.