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Awesome GitHub RepositoriesDecoder Architectures
Transformer-based model structures utilizing causal attention mechanisms for autoregressive sequence generation.
Distinguishing note: Specifically targets decoder-only transformer blocks used for generative tasks, distinct from encoder-only or encoder-decoder architectures.
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Awesome Decoder Architectures 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
Models are constructed using stacked transformer blocks with causal attention mechanisms to predict subsequent tokens in a sequence.
Pythonartificial-intelligencelarge-language-model
