tensorflow / model-optimization
A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
AI Architecture Analysis
This repository is indexed by RepoMind. By analyzing tensorflow/model-optimization in our AI interface, you can instantly generate complete architecture diagrams, visualize control flows, and perform automated security audits across the entire codebase.
Our Agentic Context Augmented Generation (Agentic CAG) engine loads full source files into context on-demand, avoiding the fragmentation of traditional RAG systems. Ask questions about the architecture, dependencies, or specific features to see it in action.
Embed this Badge
Showcase RepoMind's analysis directly in your repository's README.
Repository Overview (README excerpt)
Crawler viewTensorFlow Model Optimization Toolkit The **TensorFlow Model Optimization Toolkit** is a suite of tools that users, both novice and advanced, can use to optimize machine learning models for deployment and execution. Supported techniques include quantization and pruning for sparse weights. There are APIs built specifically for Keras. For an overview of this project and individual tools, the optimization gains, and our roadmap refer to tensorflow.org/model_optimization. The website also provides various tutorials and API docs. The toolkit provides stable Python APIs. Installation For installation instructions, see tensorflow.org/model_optimization/guide/install. Contribution guidelines **If you want to contribute to TensorFlow Model Optimization, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.** **We use GitHub issues for tracking requests and bugs.** Maintainers Subpackage Maintainers tfmot.clustering Arm ML Tooling tfmot.quantization TensorFlow Model Optimization tfmot.sparsity TensorFlow Model Optimization Community As part of TensorFlow, we're committed to fostering an open and welcoming environment. • TensorFlow Blog: Stay up to date on content from the TensorFlow team and best articles from the community.