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| title: README | |
| emoji: 🤖 | |
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| Welcome to the official Hugging Face organization for Apple! | |
| ## Apple Core ML – Build intelligence into your apps | |
| [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. | |
| * Models | |
| - [Depth Anything V2 Core ML](https://huggingface.co/collections/apple/core-ml-depth-anything-66727e780bc71c005763baf9): State-of-the-art depth estimation | |
| - [DETR Resnet50 Core ML](https://huggingface.co/apple/coreml-detr-semantic-segmentation): Semantic Segmentation | |
| - [FastViT Core ML](https://huggingface.co/collections/apple/core-ml-fastvit-666b782d98d6421a15237897): Image Classification | |
| - [Stable Diffusion Core ML](https://huggingface.co/collections/apple/core-ml-stable-diffusion-666b3b0f4b5f3d33c67c6bbe) | |
| - [Additional Core ML Model Gallery Models](https://huggingface.co/collections/apple/core-ml-gallery-models-666b66ca4e6657b7d179bc42) | |
| # 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 [Base](https://huggingface.co/collections/apple/openelm-pretrained-models-6619ac6ca12a10bd0d0df89e) | [Instruct](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca): open, Transformer-based language model. | |
| - [MobileCLIP](https://huggingface.co/collections/apple/mobileclip-models-datacompdr-data-665789776e1aa2b59f35f7c8): Mobile-friendly image-text models. | |
| - [DCLM](https://huggingface.co/collections/apple/dclm-66960ebf2400d314ff19018f): State-of-the-art open data language models via dataset curation. | |
| - [DFN](https://huggingface.co/collections/apple/dfn-models-data-659ecf85cebd98088a9d9a3b): State-of-the-art open data CLIP models via dataset curation. | |
| * 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 in Core ML Tools](https://apple.github.io/coremltools/docs-guides/source/new-features.html) | |
| - [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 | |
| - [WWDC 24: Running Mistral 7B with Core ML](https://huggingface.co/blog/mistral-coreml) | |
| - [Releasing Swift Transformers: Run On-Device LLMs in Apple Devices](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) | |