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Awesome GitHub RepositoriesComputationally Efficient Search
Optimization strategies designed to minimize the total number of trials and total compute time spent searching.
Distinct from Hyperparameter Optimizers: Distinct from general hyperparameter optimizers: focuses specifically on the trade-off between search budget and performance.
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Awesome Computationally Efficient Search GitHub Repositories
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- microsoft/flaml
microsoft/FLAML
4,365GitHub पर देखेंFLAML is an automated machine learning framework, hyperparameter optimization tool, and large language model agent orchestrator. It provides a system for model selection and tuning across various learners and datasets, while also offering a toolkit for optimizing the inference parameters and fine-tuning settings of large language models. The project features a meta-learning tuning system that analyzes historical task data to generate data-dependent default configurations, accelerating model convergence. It further enables the design of collaborative multi-agent systems through conversational
Implements search strategies that minimize total compute time and trial counts to find optimal configurations.
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