corenet

Thanks, seems you're talking about the OpenELM family of models: https://github.com/apple/corenet/tree/main/projects/openelm

Here you go - https://github.com/apple/corenet

Some likely candidates are the existing open source tools their ML team has released, most of which either use PyTorch or are usable via PyTorch: - CoreNet, a training toolchain developed by their ML teams: https://github.com/apple/corenet. It's built on PyTorch, but uses MLX - MLX, Apples internal array processing framework which is C/C++/Swift (https://pypi.org/project/mlx/). - CoreML tools, their python package for accessing the lower-level CoreML APIs (https://github.com/apple/coremltools)

This sounds like it is fully reproducible: "it also included reproducible training recipes that allow the weights (neural network files) to be replicated, which is unusual for a major tech company so far" Digging into the code, is this the best definition of the datasets used? https://github.com/apple/corenet/blob/0333b1fbb29c31809663c4... And the paper says the instruction tuning is based on UltraFeedback, this config seems to say exactly in what form: https://github.com/apple/corenet/blob/0333b1fbb29c31809663c4...

The interface looks very Apple as well. Looks like you create a config file, and you already have a model in mind with the hyperparameters and it provides a simple interface. How useful is this to researchers trying to hack the model architecture? One example: https://github.com/apple/corenet/tree/main/projects/clip#tra...

The link should go here I think: https://github.com/apple/corenet/tree/main/projects/catlip

It looks like MLX is a part of this initiative. https://github.com/apple/corenet lists "MLX examples" as one of the components being released in April.

> Relationship with CVNets > CoreNet evolved from CVNets, to encompass a broader range of applications beyond computer vision. Its expansion facilitated the training of foundational models, including LLMs. We can expect it to have grown from here: https://apple.github.io/ml-cvnets/index.html It looks like a mid-level implementations of training and inference. You can see in their "default_trainer.py"[1] that the engine uses Tensors from torch but implements its own training method. They implement their own LR scheduler and optimizer; the caller can optionally use Adam from torch. It's an interesting (maybe very Apple) choice to build from the ground up instead of partnering with existing frameworks to provide first class support in them. The MLX examples seem to be inference only at this point. It does look like this might be a landing gr