semvec/pypi-packages huggingface dataset:
hc_mul 0.2.0
Default template for PDM package
License: MIT
Requires Python: >=3.9

# hc_calculation

## Description

Python package `hc_mul`, which is a dependency for the package `simpleCalc_cmd`. 

## Installation

```
$ pip install hc_mul
```

Alternatively, it is automatically installed when you install `simpleCalc_cmd`, which can be done by
```
$ pip install simpleCalc_cmd
```
For more information, refer [simpleCalc_cmd github](https://github.com/hamsunwoo/simpleCalc_cmd)

## What does it do?
This package holds a simple function `mul` which take 2 integer in-line variables and calculates the corresponding multiplication operation. Handles errors if in-line variables are in the wrong format or number.

For additional usages, look at `simpleCalc_cmd` github or read below.

### Package Import example

```
from hc_mul.hc_mul import mul
mul()
```
mijoon_cmd 0.3.0
Default template for PDM package
License: MIT
Requires Python: >=3.9

# mijoon_cmd
## install
```
$ pip install mijoon_cmd
```

## Usage
### zsh
```
$ t_cmd p 5 3

8

$ t_cmd m 5 3

15

$ t_cmd d 10 5

2.0

```

### Python
```py
from mijoon_cmd.cmd import p # 더하기
from mijoon_cmd.cmd import m # 곱하기
from mijoon_cmd.cmd import d # 나누기
p(a,b)
# result = a+b

m(a,b)
# result = a*b

d(a,b)
# result = a/b

```

## Dependencies
```
team_plus
team_mul
team_divide
```
onnxllm 0.0.1
OnnxLLM: A collection of Onnx Large Language Models.
License: MIT
Requires Python: >=3.6

# OnnxLLM

<p align="center">
    <a href="https://pypi.org/project/onnxllm">
        <img src="https://badgen.net/pypi/v/onnxllm?color=blue" />
    </a>
</p>

This is an example project for learning purpose

# Installation

## Using Prebuilt

```bash
pip install onnxllm
```

## Install From Source

```bash
pip install git+https://github.com/inisis/OnnxLLM@main
```

## Install From Local

```bash
git clone https://github.com/inisis/OnnxLLM && cd OnnxLLM/
pip install .
```

# How to use

```
from transformers import AutoTokenizer
from onnxllm import AutoModelForCausalLM

# you should download onnx models from https://huggingface.co/inisis-me first
tokenizer = AutoTokenizer.from_pretrained("/data/llm/llm-export/onnx-standard/", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("/data/llm/llm-export/onnx-standard/", trust_remote_code=True)

prompt = '蒙古国的首都是乌兰巴托（Ulaanbaatar）\n冰岛的首都是雷克雅未克（Reykjavik）\n埃塞俄比亚的首都是'

inputs = tokenizer(prompt, return_tensors='pt')

output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

# References

> - [mnn-llm](https://github.com/wangzhaode/mnn-llm)
> - [onnxruntime](https://github.com/microsoft/onnxruntime)
> - [huggingface](https://github.com/huggingface)


<details>
  <summary>referenced models</summary>

- [chatglm-6b](https://modelscope.cn/models/ZhipuAI/chatglm-6b/summary)
- [chatglm2-6b](https://modelscope.cn/models/ZhipuAI/chatglm2-6b/summary)
- [chatglm3-6b](https://modelscope.cn/models/ZhipuAI/chatglm3-6b/summary)
- [codegeex2-6b](https://modelscope.cn/models/ZhipuAI/codegeex2-6b/summary)
- [Baichuan2-7B-Chat](https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary)
- [Qwen-7B-Chat](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)
- [Qwen-VL-Chat](https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary)
- [Qwen-1.8B-Chat](https://modelscope.cn/models/qwen/Qwen-1_8B-Chat/summary)
- [Llama-2-7b-chat-ms](https://modelscope.cn/models/modelscope/Llama-2-7b-chat-ms/summary)
- [internlm-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-chat-7b/summary)
- [phi-2](https://modelscope.cn/models/AI-ModelScope/phi-2/summary)
- [bge-large-zh](https://modelscope.cn/models/AI-ModelScope/bge-large-zh/summary)
- [TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6)
- [Yi-6B-Chat](https://modelscope.cn/models/01ai/Yi-6B-Chat/summary)
- [Qwen1.5-0.5B-Chat](https://modelscope.cn/models/qwen/Qwen1.5-0.5B-Chat/summary)
- [Qwen1.5-1.8B-Chat](https://modelscope.cn/models/qwen/Qwen1.5-1.8B-Chat/summary)
- [Qwen1.5-4B-Chat](https://modelscope.cn/models/qwen/Qwen1.5-4B-Chat/summary)
- [Qwen1.5-7B-Chat](https://modelscope.cn/models/qwen/Qwen1.5-7B-Chat/summary)

</details>
mo_cmd 0.1.4
PDM add, sub, div calculate package
License: MIT
Requires Python: >=3.9

## 설치
```
pip install mo_cmd
```
## 실행
```
$ mo-plus <정수 입력1> <정수입력2>
mo-plus 5 6
11

$ mo-minus <정수 입력1> <정수입력2>
mo-minus 9 23
-14

$ mo-divide <정수 입력1> <정수입력2>
mo-minus 10 4
2.5
```

## 실행결과
![image](https://github.com/user-attachments/assets/9ff4bcbd-03d8-42be-8455-34ac4f6a6977)

## 코드
```
import sys
from mo_plus.plus import add
from mo_minus.minus import substract
from mo_divide.divide import divide

def plus():
    a=int(sys.argv[1])
    b=int(sys.argv[2])
    print('',add(a,b))

def minus():
    a=int(sys.argv[1])
    b=int(sys.argv[2])
    print('',substract(a,b))

def div():
    a=int(sys.argv[1])
    b=int(sys.argv[2])
    print('',divide(a,b))
```

## Dependencies

    "mo-plus>=0.1.0",
    "mo-minus>=0.1.0",
    "mo-divide>=0.1.0",
power01_cmd 0.2.0
Default template for PDM package
License: MIT
Requires Python: >=3.9

# power01_cmd


# 설치 방법
```
$ pip install power01_cmd
```

# 코드
```
import sys
from power01_plus.plus import plus
from power01_min.min import minus
from power01_mul.mul import mul
from power01_div.div import div


def call():
    a = int(sys.argv[1])
    b = int(sys.argv[2])
    print('a+b= ',end="")
    plus(a,b)
    print()
    print('a-b= ',end="")
    minus(a,b)
    print()
    print('a*b= ',end="")
    mul(a,b)
    print()
    print('a/b= ',end="")
    div(a,b)

    print('success!!')
```

# 실행
```
$ cmd 5 10 
```

# 결과
```
a+b= 15

a-b= -5

a*b= 50

a/b= 0.5
success!!
```

# 참조
- https://github.com/baechu805/power01_plus
- https://github.com/baechu805/power01_min
- https://github.com/sooj1n/power01_mul
- https://github.com/sooj1n/power01_div
hc_add 0.2.4
Default template for PDM package
License: MIT
Requires Python: >=3.9

# hc_calculation

## Description

Python package `hc_add`, which is a dependency for the package `simpleCalc_cmd`. 

## Installation

```
$ pip install hc_add
```

Alternatively, it is automatically installed when you install `simpleCalc_cmd`, which can be done by
```
$ pip install simpleCalc_cmd
```
For more information, refer [simpleCalc_cmd github](https://github.com/hamsunwoo/simpleCalc_cmd)

## What does it do?
This package holds a simple function `add` which take 2 integer in-line variables and calculates the corresponding addition operation. Handles error when in-line arguments are not given, in the wrong number, or wrong format.

For additional usages, look at `simpleCalc_cmd` github or read below.

### Package Import example

```
from hc_add.hc_add import add
add()
```
seokbong_cmd 2.0.1
Default template for PDM package
License: MIT
Requires Python: >=3.9

## PLUS, MUL, DIV CMD

### team project

### member : tbongkim03, Seokxkyu

### github url : github.com/tbongkim03
### github url : github.com/Seokxkyu

## **USAGE**

   
   `$ pip install seokbong_cmd`

   ![pip install](https://github.com/tbongkim03/seokbong_cmd/blob/main/img/install_pip.png?raw=true)

   `$ seokbong-cmd-plus <first num> <second num>`

   ![add](https://github.com/tbongkim03/seokbong_cmd/blob/main/img/add.png?raw=true)

   `$ seokbong-cmd-mul <first num> <second num>`

   ![mul](https://github.com/tbongkim03/seokbong_cmd/blob/main/img/mul.png?raw=true)

   `$ seokbong-cmd-div <first num> <second num>`

   ![div](https://github.com/tbongkim03/seokbong_cmd/blob/main/img/div.png?raw=true)


## **Thanks**

   dMario24, tbongkim03, Seokxkyu

## **License**

   The MIT

## **Packaged by pypi**

   [URL](https://pypi.org/project/seokbong_cmd/)
mask_cmd 1.0.0
Default template for PDM package
License: MIT
Requires Python: >=3.9

# mask_cmd

This project is practice project for python, co-work process, repository dependence.
This project is made by team_mask.
This project implements a simple calculator.

## Install

You can install this project write commandline like this.
```
$ pip install mask_cmd
```

## Use Guide

This project can be run by entering the following commands

### PLUS
```
$ mask_call_plus 3 5
```

```
8
```

### Divide
```
$ mask_call_cut 10 5
```

```
2
```

### Duplicate
```
$ mask_call_dup 7 8
```

```
56
```