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Awesome GitHub RepositoriesDeclarative Machine Learning Frameworks
Frameworks that enable the specification of model architectures and training pipelines via configuration files instead of code.
Distinct from Machine Learning Training: Existing candidates focus on training loops or generic training, not the specific declarative paradigm of avoiding manual code.
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Awesome Declarative Machine Learning Frameworks GitHub Repositories
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- uber/ludwig
uber/ludwig
11,718عرض على GitHubLudwig is a declarative machine learning framework designed for training neural networks and large language models using configuration files instead of manual coding. It functions as a multimodal model builder and a low-code tool for supervised fine-tuning, allowing users to build models that process mixed inputs of text, images, audio, and tabular data. The project distinguishes itself through an automated hyperparameter optimizer and a system for large language model fine-tuning using parameter-efficient adapters. It features a multimodal data pipeline and the ability to automatically gener
Provides a framework for building and training neural networks using declarative configuration files instead of manual coding.
Python
