semvec/pypi-packages huggingface dataset:
bluecon 0.5.0a1
Library for connecting to Fermax Blue supported doorbells
Requires Python: >=3.7

# BlueCon

Library for connecting to Fermax Blue supported doorbells.

### Tested Devices:

* Fermax Veo XS WiFi (9449)

## Installation

```sh
pip install bluecon
```

## Acknowledgements

I'd like to thank all contributors of [push_receiver](https://github.com/olijeffers0n/push_receiver) and its many forks for provinding a library that can receive push notifications from Firebase. This is the essencial part of how Fermax Blue works in terms of receiving the doorbell rings and without it this project would be close to pointless. This library is included with some minor changes in this repo.
sorsa 1.0.1
"SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models" implementation intergrated with Hugging Face transformers
Requires Python: >=3.8

# SORSA Python Package

## Initialize SORSA model:
```python
from sorsa import SORSAConfig, SORSAModel, SORSATrainer, SORSATrainingArguments
config = SORSAConfig(
    base_model_name_or_path="meta-llama/Llama-2-7b-hf",
    target_modules=[
        "q_proj",
        "o_proj",
        "k_proj",
        "v_proj",
        "gate_proj",
        "up_proj",
        "down_proj",
    ],
    rank=16,
    dropout=0,
)
sorsaModel = SORSAModel(config)
self.model.to("cuda")
self.model.sorsa_init() # Initialize SORSA adapters.
```

## Train SORSA model:
```python
trainingArguments = SORSATrainingArguments(
    # ...
    gamma=4e-4,
)
trainer = SORSATrainer(
        model=sorsaModel,
        args=trainingArguments,
        train_dataset=train_dataset,
)
trainer.train()
```
ideo 0.2.1
High quality image generation by ideogram.ai. Reverse engineered API.

# IdeoImageCreator
About High quality image generation by ideogram. Reverse engineered API.

## How to
- Login to https://ideogram.ai and generate some images.
- Use `Chrome` or other browsers to inspect the network requests (F12 -> XHR).
- Clone this REPO -> `git clone https://github.com/yihong0618/IdeoImageCreator.git`
- Copy the cookie.
- Copy the Bearer token.
- Copy your user name.
- Export IDEO_COOKIE='xxxxx'.
- Export IDEO_AUTH_TOKEN='xxxxx' (found after `Bearer` in the HEADERS of the requests).
- Export IDEO_USER_ID='xxxxx'.

## Usage

```
python -m ideo --prompt 'a big red cat'
```

or
```
pip install -U ideo
```

```python
from ideo import ImageGen
i = ImageGen('cookie', 'user_id', 'auth_token') # Replace 'cookie', 'user_id', and 'auth_token' with your own values
print(i.get_limit_left())
i.save_images("a blue cyber dream", './output')
```

## Customization

Customization params: 
- aspect_ratio - default "1:1"
- model_version - default "V_0_3" ("V_1_0" and lower take 1 credit)
- style - default "AUTO"
- is_auto_prompt - default "AUTO"

Example:

```python
i.save_images(prompt = "woman with son turned away walking in a park",
              aspect_ratio="16:10",
              model_version="V_1_5",
              style="PHOTO",
              output_dir='./output',)
```


## Thanks

- [chatgpt-telegram-bot](https://github.com/brainboost/chatgpt-telegram-bot)
onnxdumper 0.7
A package for ONNX intermediate data dumping
Requires Python: >=3.6

# onnxdumper

这是一个用于 ONNX 推理会话的 Python 包，通过额外创建一个 ONNX 推理会话加载修改了输出接口的 ONNX 模型，以便在导出 ONNX 推理中间层的输出。

## 安装

```sh
pip install onnxdumper
```
## 使用

```python
import onnxruntime
import onnxdumper

# 将 onnxruntime.InferenceSession 替换为 onnxdumper.InferenceSession 即可
# session = onnxdumper.InferenceSession("path/to/your/model.onnx")
session = onnxdumper.InferenceSession("path/to/your/model.onnx")

# 准备输入数据
input_feed = {
    "input_name": input_data
}

# 运行推理并导出中间层输出
output_names = ["output_name"]
results = session.run(output_names, input_feed, dump_path="path/to/save/outputs")
```

## 导出格式
导出格式为 `npz`，使用 `numpy` 进行加载，data 的 key 为输出节点的名称，value 为输出节点的数据。
```python
import numpy as np
data = np.load("path/to/save/outputs.npz")
for key in data.keys():
    print(key, data[key], data[key].shape, data[key].dtype)
```

## 节点输出的key查看方式
### 1. 使用 onnx 加载模型查看
```python
model = onnx.load(path_or_bytes)
for node in model.graph.node:
    print(node.output)
```
### 2. 使用 netron 查看

# License
MIT
aws-cryptography-internal-standard-library 100.0.0
aws-cryptography-internal-standard-library



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
token-analyzer 0.1.2
A Python library for analyzing and calculating token usage costs for various LLM models like OpenAI and Anthropic, with support for real-time currency conversion.
Requires Python: >=3.6

# Token Analyzer Library

`TokenAnalyzer` is a Python library to analyze and calculate token usage costs for various LLM models such as OpenAI, Anthropic, and others. The library supports live currency conversion using the FreeCurrencyAPI or uses default exchange rates if the API key is not provided.

## Features

- Calculate costs for input/output tokens across various LLM models.
- Supports multiple currencies (INR, USD, EUR, etc.).
- Can write token usage information to a CSV file.
- Logging options for detailed analysis of costs.
- Use FreeCurrencyAPI for real-time currency conversion.

## Installation

You can install the library directly from PyPI (after publishing):

```bash
pip install token-analyzer
```

## Usage

Here is a basic usage example:

```
from analyzer.token_analysis import TokenAnalysis

# Response data from a model
response = {
    'usage': {'prompt_tokens': 1000, 'completion_tokens': 500},
    'model': 'claude-2.1'
}

# Initialize the analyzer
analyzer = TokenAnalysis(output_currency="INR", output_csv="output/token_costs.csv", create_csv=True, log_info=True)

# Calculate token cost
total_cost = analyzer.token_analysis(response, "ANTHROPIC")
print(f"Total Cost: {total_cost}")

```

## Example without CSV output and logging disabled:

```
analyzer = TokenAnalysis(output_currency="USD", create_csv=False, log_info=False)
total_cost = analyzer.token_analysis(response, "OPENAI")
print(f"Total Cost: {total_cost}")

```

## License

This project is licensed under the MIT License - see the [LICENSE]() file for details.

## Links

* [GitHub Repository](https://github.com/krtarunsingh/token_analyzer)
Shack4u 0.5.0
Una biblioteca para consultar cursos de hack4u.

# Hack4u Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4u.

## Cursos disponibles:

- Introducción a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introducción al Hacking [53 horas]

## Instalación

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```

## Uso básico

### Listar todos los cursos

```python
from hack4u import list_courses

for course in list_courses():
    print(course)
```

### Obtener un curso por nombre

```python
from hack4u import get_course_by_name

course = get_course_by_name("Introducción a Linux")
print(course)
```

### Calcular duración total de los cursos

```python3
from hack4u.utils import total_duration

print(f"Duración total: {total_duration()} horas")