From 3d5b36bb6d684bd1941de03a41ac1e9925d1fe49 Mon Sep 17 00:00:00 2001 From: Elizabeth Davis Date: Mon, 27 Jul 2026 12:39:20 -0400 Subject: [PATCH] Editorial pass: remove second person, fix Apple silicon capitalization --- docs/src/landing_page.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/src/landing_page.md b/docs/src/landing_page.md index 9416f2ac..3961556b 100644 --- a/docs/src/landing_page.md +++ b/docs/src/landing_page.md @@ -2,9 +2,9 @@ ## What is `coreai-opt`? -`coreai-opt` is a Python library for compressing PyTorch models for deployment on Apple Silicon. It allows you to apply compression-based optimizations (such as quantization or palettization) to any PyTorch model, producing a transformed PyTorch model that can be converted to a Core AI model and run with the [Core AI](https://developer.apple.com/documentation/coreai) framework. +`coreai-opt` is a Python library for compressing PyTorch models for deployment on Apple silicon. It applies compression-based optimizations (such as quantization or palettization) to any PyTorch model, producing a transformed PyTorch model that can be converted to a Core AI model and run with the [Core AI](https://developer.apple.com/documentation/coreai) framework. -Model compression can help reduce the memory footprint of your model (disk size and at runtime), reduce inference latency, reduce power consumption, or optimize them all at once. +Model compression can help reduce the memory footprint of a model (disk size and at runtime), reduce inference latency, reduce power consumption, or optimize them all at once. ```{mermaid} flowchart LR @@ -20,11 +20,11 @@ flowchart LR `coreai-opt` is built around the following ideas: -- **PyTorch native.** All APIs operate on PyTorch models. Compression is another transformation in your PyTorch workflow. The output of every compressor is itself a PyTorch model that can be validated, fine-tuned, and exported like any other model. +- **PyTorch native.** All APIs operate on PyTorch models. Compression is another transformation in a PyTorch workflow. The output of every compressor is itself a PyTorch model that can be validated, fine-tuned, and exported like any other model. - **Integrates with existing PyTorch code.** Adding post-training compression, calibration-based, or compression-aware training to an existing PyTorch pipeline takes a few additional lines of code. All three use the same compressor object. -- **Aligned with Apple Silicon.** Default configurations and the majority of the available optimization options align with what the [Core AI](https://developer.apple.com/documentation/coreai) runtime executes efficiently, on one or many of the Apple Silicon platforms. Compressed PyTorch models can be seamlessly converted to `.aimodel` for deployment via Core AI. +- **Aligned with Apple silicon.** Default configurations and the majority of the available optimization options align with what the [Core AI](https://developer.apple.com/documentation/coreai) runtime executes efficiently, on one or many of the Apple silicon platforms. Compressed PyTorch models can be seamlessly converted to `.aimodel` for deployment via Core AI. ## Types of compression @@ -44,9 +44,9 @@ The process of applying compression to a model typically involves the following - **Calibration-based compression**: Post-training compression with calibration data. Often used when quantizing activations. A small amount of representative data (e.g. ~128 samples) lets compressors observe activation ranges and weight sensitivities. -- **Fine-tuning-based compression**: Compression-aware fine-tuning (e.g. quantization-aware training) with full training data. The compressor is integrated into your training loop so the model adapts to compression error as it trains. The most time-intensive workflow, but typically the only way to recover accuracy at the most aggressive compression ratios for weights (4 bits and below), and/or for models that are sensitive to activation quantization. +- **Fine-tuning-based compression**: Compression-aware fine-tuning (e.g. quantization-aware training) with full training data. The compressor is integrated into the training loop so the model adapts to compression error as it trains. The most time-intensive workflow, but typically the only way to recover accuracy at the most aggressive compression ratios for weights (4 bits and below), and/or for models that are sensitive to activation quantization. -`coreai-opt`'s APIs allow you to easily move from one stage to the next while evaluating accuracy after each stage and escalating to a more expensive workflow only when needed. +`coreai-opt`'s APIs make it straightforward to move from one stage to the next while evaluating accuracy after each stage and escalating to a more expensive workflow only when needed. ## Getting started