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Auto-detect and install pre-built wheels for Flash Attention & vLLM

Project description

Pre-Built Wheels

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

Quick Install

Auto-detect your environment and install the right wheel:

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

vLLM

License

MIT

Project details


Download files

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Source Distribution

mlwheels-0.1.0.tar.gz (4.7 kB view details)

Uploaded Source

Built Distribution

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mlwheels-0.1.0-py3-none-any.whl (5.6 kB view details)

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  • Download URL: mlwheels-0.1.0.tar.gz
  • Upload date:
  • Size: 4.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.2

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  • Download URL: mlwheels-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 5.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.2

File hashes

Hashes for mlwheels-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3451f60a947df52a47978d0fbe04697da284e7a7125a5063febc0e5baab1f15a
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BLAKE2b-256 2fea4e3d6081f9e840e2d39185d473bb76764f34d48476766b40aefd34c365eb

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