idea: Support Apple CoreML

[qdrddr]

Problem

Please consider adding Core ML model package format support to utilize Apple Silicone Nural Engine + GPU.

Success Criteria
Utilize both ANE & GPU, not just GPU on Apple Silicon

Additional context
List of Core ML package format models

https://github.com/likedan/Awesome-CoreML-Models

[qdrddr]

This is about running LLMs locally on Apple Silicone. Core ML is a framework that can redistribute workload across CPU, GPU & Nural Engine (ANE). ANE is available on all modern Apple Devices: iPhones & Macs (A14 or newer and M1 or newer). Ideally, we want to run LLMs on ANE only as it has optimizations for running ML tasks compared to GPU. Apple claims "deploying your Transformer models on Apple devices with an A14 or newer and M1 or newer chip to achieve up to 10 times faster and 14 times lower peak memory consumption compared to baseline implementations".

1. To utilize Core ML first, you need to convert a model from TensorFlow, PyTorch to Core ML model package format using coremltools (or simply utilize existing models in Core ML package format ).
2. Second, you must now use that converted package with an implementation designed for Apple Devices. Here is the Apple XCode reference PyTorch implementation.

https://machinelearning.apple.com/research/neural-engine-transformers

[qdrddr]

Work in progress on CoreML implementation for [whisper.cpp]. They see x3 performance improvements for some models. (ggerganov/whisper.cpp#548) you might be interested in.
You might also be interested in another implementation Swift Transformers. Example of CoreML application
https://github.com/huggingface/swift-chat

[freelerobot]

Updated title to better reflect ask

[qdrddr]

Would that be of any help the LM Studio has implemented MLX. And here is Anemll ANE library to work with MLX it is MIT Licensed. And there's FastMLX with an Apache 2.0 license. @freelerobot

[qdrddr]

there's Apple MPS Metal GPU flash attention in swift by the way