semvec/pypi-packages huggingface dataset:
mlwheels 0.1.0
Auto-detect and install pre-built wheels for Flash Attention & vLLM
License: MIT
Requires Python: >=3.8
Keywords: flash-attention, vllm, cuda, pytorch, wheels

# Pre-Built Wheels

Pre-built wheels for Flash Attention & vLLM. Skip the compilation.

## Quick Install

Auto-detect your environment and install the right wheel:

```bash
pip install mlwheels

# Install Flash Attention
mlwheels flash-attn

# Install vLLM
mlwheels vllm

# Just detect environment (no install)
mlwheels --detect
```

## Features

- Search and filter by CUDA, Python, PyTorch, and Platform
- One-click copy for `pip` and `uv` install commands
- Direct download links
- Flash Attention 2 & 3 support
- vLLM wheels for multiple CUDA versions

## Supported Configurations

### Flash Attention 2
- CUDA: 11.8, 12.1, 12.2, 12.3, 12.4, 12.6
- PyTorch: 2.0 - 2.10
- Python: 3.8 - 3.12
- Platforms: Linux x86_64, Linux ARM64, Windows

### Flash Attention 3
- CUDA: 12.6, 12.8, 12.9, 13.0
- PyTorch: 2.8 - 2.10
- Python: 3.10 - 3.12
- Platforms: Linux x86_64, Linux ARM64, Windows

### vLLM
- CUDA: 11.8, 12.1, 12.4, 12.6, 12.8, 12.9, 13.0, CPU
- Python: 3.8+
- Platforms: Linux x86_64, Linux ARM64

## Sources

**Flash Attention**
- [flashattn.dev](https://flashattn.dev/)
- [Flash Attention 3 Wheels](https://windreamer.github.io/flash-attention3-wheels/)
- [mjun0812/flash-attention-prebuild-wheels](https://github.com/mjun0812/flash-attention-prebuild-wheels)
- [Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention)

**vLLM**
- [vLLM GitHub Releases](https://github.com/vllm-project/vllm/releases)
- [vLLM Documentation](https://docs.vllm.ai/)

## License

MIT
option-prices 0.1.1
Option pricing using the Black Scholes Model
License: MIT
Requires Python: >=3.8

# Option-prices

This package is to be used for option price calculation using the Black Scholes Model and binomial model

## Description

This package provides a simple and easy-to-use implementation of the Black Scholes Model and binomial model for calculating European call and put option prices. It is designed for students, traders, and developers who want a lightweight and reliable tool for option pricing without the complexity of larger libraries.

## Getting Started

### Dependencies

Numpy,Scipy

### Installing

pip install option-prices

### Executing program

import option_pricings
from option_pricings import bsm
from option_pricings import binomial

## example when time scale is in year
price=bsm(S=50,K=50,T=1,r=6,sigma=25,option_type = "call",time_scale="Year")
print(price)

price = binomial(S=30,K=40,r=6,T=2,time_step = 1,u=1.2,d=0.8)
print(price)

# using sigma parameter instead of u and d
price = binomial(S=30,K=40,r=6,T=2,time_step = 1,sigma = 25)
print(price)

# put option
price = binomial(S=30,K=40,r=6,T=2,time_step = 1,u=1.2,d=0.8,option_type="put")
print(price)

# changing time scale to month
price = binomial(S=30,K=40,r=6,T=24,time_step = 12,u=1.2,d=0.8,time_scale = "month")
print(price)

## example when time scale is in month
price = bsm(S=50,K=50,T=6,r=6,sigma=25,option_type = "call",time_scale="Month")
print(price)

Note:
When time_scale="Year", enter T in years.
When time_scale="Month", enter T in months.
same thing applies for binomial
## Authors

Shreenivas Dani

shreenivasdani@gmail.com
## Version History

* 0.1
    * Initial Release

## License

This project is licensed under the MIT License - see the LICENSE.md file for details