Spaces:
Running
Running
File size: 2,968 Bytes
d7e0d35 5bb6864 cb420aa 2a85533 5bb6864 2a85533 7884a68 2a85533 4b200dc 2a85533 7884a68 2a85533 4b200dc 2a85533 4b200dc 2a85533 7884a68 2a85533 53ebbb5 2a85533 cb420aa 2a85533 33ccf9b 2a85533 33ccf9b 2a85533 cb420aa 2a85533 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
---
title: README
emoji: 📉
colorFrom: gray
colorTo: purple
sdk: static
pinned: false
---
Welcome to the official Hugging Face organisation for Apple!
# Apple Core ML – Build intelligence into your apps with Core ML
[Core ML](https://developer.apple.com/machine-learning/core-ml/) is optimized for on-device performance of a broad variety of model types by leveraging Apple Silicon and minimizing memory footprint and power consumption.
## Core ML Models
- [FastViT](https://huggingface.co/collections/coreml-projects/coreml-fastvit-666b0053e54816747071d755): Image Classification
- [Depth Anything](https://huggingface.co/coreml-projects/coreml-depth-anything-small): Depth estimation.
- [DETR Resnet50](https://huggingface.co/coreml-projects/coreml-detr-semantic-segmentation): Semantic Segmentation
- [Stable Diffusion Core ML models](https://huggingface.co/collections/apple/core-ml-stable-diffusion-666b3b0f4b5f3d33c67c6bbe).
- [Hugging Face Core ML Examples](https://github.com/huggingface/coreml-examples).
# Apple Machine Learning Research
Open research to enable the community to deliver amazing experiences that improve the lives of millions of people every day.
## Models
- OpenELM: open, Transformer-based language model. [Base](https://huggingface.co/collections/apple/openelm-pretrained-models-6619ac6ca12a10bd0d0df89e) | [Instruct](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
- [MobileCLIP](https://huggingface.co/collections/apple/mobileclip-models-datacompdr-data-665789776e1aa2b59f35f7c8): Mobile-friendly image-text models.
## Datasets
- [FLAIR](https://huggingface.co/datasets/apple/flair): A large image dataset for federated learning.
- [DataCompDR](https://huggingface.co/collections/apple/mobileclip-models-datacompdr-data-665789776e1aa2b59f35f7c8): Improved datasets for training image-text models.
## Benchmarks
- [TiC-CLIP](https://huggingface.co/collections/apple/tic-clip-666097407ed2edff959276e0): Benchmark for the design of efficient continual learning of image-text models over years
## Select Highlights and Other Resources
- [Hugging Face CoreML Examples](https://github.com/huggingface/coreml-examples) – Run Core ML models with two lines of code!
- [Apple Model Gallery](https://developer.apple.com/machine-learning/models/)
- [New features](https://apple.github.io/coremltools/docs-guides/source/new-features.html) in Core ML Tools 8
- [Apple Core ML Stable Diffusion](https://github.com/apple/ml-stable-diffusion) – Library to run Stable Diffusion on Apple Silicon with Core ML.
- Hugging Face Blog Posts
- [Releasing Swift Transformers: Run On-Device LLMs in Apple Devices (Aug, 2023)](https://huggingface.co/blog/swift-coreml-llm)
- [Faster Stable Diffusion with Core ML on iPhone, iPad, and Mac](https://huggingface.co/blog/fast-diffusers-coreml)
- [Using Stable Diffusion with Core ML on Apple Silicon](https://huggingface.co/blog/diffusers-coreml) |