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Awesome GitHub RepositoriesPreference Alignment Datasets
Utilities for preparing datasets of chosen and rejected response pairs to align models with human preferences.
Distinguishing note: Specifically targets RLHF or DPO-style preference data preparation, distinct from general supervised fine-tuning.
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Awesome Preference Alignment Datasets GitHub Repositories
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- jingyaogong/minimind
jingyaogong/minimind
39,663View on GitHubThis project is a comprehensive framework for the entire lifecycle of transformer-based language models, supporting everything from foundational pretraining to specialized deployment. It provides a modular toolkit for defining neural network architectures, managing data preparation pipelines, and executing training routines across various scales. The framework is designed to handle the full model development process, including supervised fine-tuning, behavioral alignment, and the integration of agentic capabilities. What distinguishes this framework is its focus on efficient training and adva
The framework facilitates the creation of preference datasets by structuring pairs of chosen and rejected responses to align model outputs with human expectations.
Pythonartificial-intelligencelarge-language-model
