semvec/pypi-packages huggingface dataset:
nbcc_loading 0.0.2
NBCC Loadings
Requires Python: >=3.9

<!--- Heading --->
<div align="center">
  <h1>NBCC Loadings</h1>
  <p>
    Library to obtain NBCC Loadings
  </p>
<h4>
    <a href="https://github.com/rpakishore/nbcc-loadings/">View Demo</a>
  <span> · </span>
    <a href="https://github.com/rpakishore/nbcc-loadings">Documentation</a>
  <span> · </span>
    <a href="https://github.com/rpakishore/nbcc-loadings/issues/">Report Bug</a>
  <span> · </span>
    <a href="https://github.com/rpakishore/nbcc-loadings/issues/">Request Feature</a>
  </h4>
</div>
<br />

[![tests](https://github.com/rpakishore/nbcc-loadings/actions/workflows/test.yml/badge.svg)](https://github.com/rpakishore/nbcc-loadings/actions/workflows/test.yml)
![GitHub commit activity](https://img.shields.io/github/commit-activity/m/rpakishore/nbcc-loadings)
![GitHub last commit](https://img.shields.io/github/last-commit/rpakishore/nbcc-loadings)

![PyPI - Format](https://img.shields.io/pypi/format/nbcc_loading) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nbcc_loading)

<!-- Table of Contents -->
<h2>Table of Contents</h2>

- [1. About the Project](#1-about-the-project)
- [2. Getting Started](#2-getting-started)
  - [2.1. Prerequisites](#21-prerequisites)
  - [2.2. Dependencies](#22-dependencies)
  - [2.3. Installation](#23-installation)
- [3. Usage](#3-usage)
- [4. Roadmap](#4-roadmap)
- [5. License](#5-license)
- [6. Contact](#6-contact)
- [7. Acknowledgements](#7-acknowledgements)

<!-- About the Project -->
## 1. About the Project

A library to supply NBCC loadings, to be incorporated into other calculations

<!-- Getting Started -->
## 2. Getting Started

<!-- Prerequisites -->
### 2.1. Prerequisites

### 2.2. Dependencies

The repo comes pre-compiled with all dependencies.

<!-- Installation -->
### 2.3. Installation

Install from pypi

```bash
python -m venv .venv

.venv\Scripts\activate

pip install nbcc_loading
```
<!-- Usage -->
## 3. Usage

```python

```

<!-- Roadmap -->
## 4. Roadmap

- [ ] Snow Loadings
  - [x] NBCC 2020
  - [ ] NBCC 2015
  - [ ] NBCC 2010
- [ ] Wind Loadings
  - [x] NBCC 2020
  - [ ] NBCC 2015
  - [ ] NBCC 2010
- [ ] Seismic Loadings
  - [ ] NBCC 2020
  - [ ] NBCC 2015
  - [ ] NBCC 2010

<!-- License -->
## 5. License

See `LICENSE.txt` for more information.

<!-- Contact -->
## 6. Contact

Arun Kishore - [@rpakishore](mailto:pypi@rpakishore.co.in)

Project Link: [https://github.com/rpakishore/nbcc-loadings](https://github.com/rpakishore/nbcc-loadings)

<!-- Acknowledgments -->
## 7. Acknowledgements

- [Awesome README Template](https://github.com/Louis3797/awesome-readme-template/blob/main/README-WITHOUT-EMOJI.md)
- [Banner Maker](https://banner.godori.dev/)
- [Shields.io](https://shields.io/)
- [Carbon](https://carbon.now.sh/)
SimpleOUCH 0.0.2
A simple OUCH 5.0 parser
Requires Python: >=3.7

# OUCH_Parser
A simple parser for OUCH messages

## Introduction
OUCH 5.0 is a text-based protocol for interaction between parties in financial trading. Banks, brokers, clearing firms, exchanges, and other general market participants use OUCH protocol for all phases of electronic trading.

This package provides a simple parser to interpret and store OUCH messages after they have been used for electronic trading purposes. It does not create OUCH messages or receive them in real time. It supports the decoding and formatting of OUCH messages.

## Licence
The module is licensed under the MIT license.


## Parsing Messages

To parse an OUCH message, first create an instance of the Parser class.

parser = Parser.Parser()

parser.parse(data) will parse the encoded OUCH messages that you pass in. You should enter data of type bytes, str, List[bytes], List[str], or List[int]. Results are stored inside the parser object

parser.getMessages() will return a Python dictionary containing all the data stored inside your parser object

parser.reset() will clear the data stored inside your parser

## Contributing
Comments, suggestions, bug reports, bug fixes – all contributions to this project are welcomed. Please use this project’s GitHub page for access to the latest source code, and please open an issue for comments, suggestions, and bugs.
offloading 2.0.0
Offloading tasks in threads or processes
Requires Python: >=3.7

# Offloading

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

Offloading tasks using processes

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)
- [Usage](#usage)

## Installation

```console
pip install offloading
```

## License

`offloading` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.

## Usage

```python
from offloading import offload, Task


def get_result(x):
    return x * 2


future = Task.run(get_result, 2)
res = future.result(timeout=1)
assert res == 4


@offload
def heavy_processing(x):
    return x * 2


# blocking operation
res = heavy_processing(10)
assert res == 20
```

Check out `tests` for more.
bbplot 0.0.2
For generating residual plots
Requires Python: >=3.7

# bbplot

Generate diagnostics and residual plots.

## Instructions

1. Install:

```
pip install bbplot
```

2. Import:

```
from bbplot import bijan
```

2. Call:

```
bijan.eplot(error)
bijan.resid(error)
bijan.pval(error)
```
lancraft 0.1.0
Customizing and deploying your own large language models.
Requires Python: >=3.7

# LanCraft
LanCraft is a unified package for customizing and deploying large language models such as
[LLaMa](https://arxiv.org/abs/2302.13971v1), [Flan-T5](https://arxiv.org/abs/2210.11416),
[OPT](https://arxiv.org/abs/2205.01068), [GLM](https://github.com/THUDM/GLM-130B),
and [CodeGen](https://arxiv.org/abs/2203.13474).

The key features of LanCraft include:

🌱 **Portable**.  
LanCraft can seamlessly run on a single commodity GPU, CPU, and Apple Silicon by utilizing offloading and compression.

⚡ **Fast**.  
LanCraft incorporates state-of-the-art techniques to increase the training and inference efficiency.

🎯 **End-to-end**.  
LanCraft provides end-to-end application examples from training on custom datasets to serving with web UIs.

## Install
Requirements:  
 - PyTorch >= 1.13 [(Help)](https://pytorch.org/get-started/locally/)

### Method 1: With pip
```
pip install lancraft
```

### Method 2: From source
```
git clone git@github.com:lm-sys/LanCraft.git
cd LanCraft
pip install -e .
```

## Examples

### Question Answering

### Summarization

### Chatbot

## Inference

## Serving

### Launch a Server

### Launch a Web UI

## Fine-Tuning

### Instruction Fine-Tuning

### Fine-Tuning on Custom Datasets