content.raw: You gotta love what Aapple’s mlx team cooked:
content.type: text
content.value: You gotta love what Aapple’s mlx team cooked:
content.type: new_line
content.type: new_line
content.raw: - A unified memory model that literally does compute-magic: parallel operations with automatic dependency insertions.
content.type: text
content.value: - A unified memory model that literally does compute-magic: parallel operations with automatic dependency insertions.
content.type: new_line
content.raw: - Supports off-the-shelf use of all the fun stuff in composable func transformations (differentiation,  vectorization, computation graph optimization).
content.type: text
content.value: - Supports off-the-shelf use of all the fun stuff in composable func transformations (differentiation,  vectorization, computation graph optimization).
content.type: new_line
content.raw: - Houses simplified forms of all the APIs we love and in the languages we adore (python, C++, C) sorry Swift :)
content.type: text
content.value: - Houses simplified forms of all the APIs we love and in the languages we adore (python, C++, C) sorry Swift :)
content.type: new_line
content.raw: - mlx.nn is a stallion 🔥 simple to use.
content.type: text
content.value: - mlx.nn is a stallion 🔥 simple to use.
content.type: new_line
content.raw: - Open-source friendly (who would have thought lol).
content.type: text
content.value: - Open-source friendly (who would have thought lol).
content.type: new_line
content.raw: - Dynamic graph construction👍🏼
content.type: text
content.value: - Dynamic graph construction👍🏼
content.type: new_line
content.raw: - Supports both CPU and GPU🤖
content.type: text
content.value: - Supports both CPU and GPU🤖
content.type: new_line
content.raw: - Beginner Friendly 👌🏼
content.type: text
content.value: - Beginner Friendly 👌🏼
content.type: new_line
content.raw: - Great examples (clean code💯)
content.type: text
content.value: - Great examples (clean code💯)
content.type: new_line
content.raw: - Good documentation
content.type: text
content.value: - Good documentation
content.type: new_line
content.type: new_line
content.raw: Well done Awni Hannun et al 👏🏼
content.type: text
content.value: Well done Awni Hannun et al 👏🏼
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content.type: new_line
content.raw: Could this be The Transformer of ml frameworks? Well at least for us mac users 😂
content.type: text
content.value: Could this be The Transformer of ml frameworks? Well at least for us mac users 😂
content.type: new_line
content.type: new_line
content.raw: Repo: 
content.type: text
content.value: Repo: 
content.href: https://github.com/ml-explore/mlx
content.raw: https://github.com/ml-explore/mlx
content.type: link
content.type: new_line
content.raw: Examples: 
content.type: text
content.value: Examples: 
content.href: https://github.com/ml-explore/mlx-examples
content.raw: https://github.com/ml-explore/mlx-examples
content.type: link
content.type: new_line
content.raw: Documentation: 
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content.value: Documentation: 
content.href: https://ml-explore.github.io/mlx/build/html/python/nn.html
content.raw: https://ml-explore.github.io/mlx/build/html/python/nn.html
content.type: link
rawContent: You gotta love what Aapple’s mlx team cooked:
  
  - A unified memory model that literally does compute-magic: parallel operations with automatic dependency insertions.
  - Supports off-the-shelf use of all the fun stuff in composable func transformations (differentiation,  vectorization, computation graph optimization).
  - Houses simplified forms of all the APIs we love and in the languages we adore (python, C++, C) sorry Swift :)
  - mlx.nn is a stallion 🔥 simple to use.
  - Open-source friendly (who would have thought lol).
  - Dynamic graph construction👍🏼
  - Supports both CPU and GPU🤖
  - Beginner Friendly 👌🏼
  - Great examples (clean code💯)
  - Good documentation
  
  Well done Awni Hannun et al 👏🏼
  
  Could this be The Transformer of ml frameworks? Well at least for us mac users 😂
  
  Repo: https://github.com/ml-explore/mlx
  Examples: https://github.com/ml-explore/mlx-examples
  Documentation: https://ml-explore.github.io/mlx/build/html/python/nn.html
reactions.reaction: ❤️
reactions.users: osanseviero victor awni merve Federic
commentators._id: 6032802e1f993496bc14d9e3
commentators.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6032802e1f993496bc14d9e3/w6hr-DEQot4VVkoyRIBiy.png
commentators.fullname: Omar Sanseviero
commentators.name: osanseviero
commentators.type: user