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Utilities for configuring model output streams to enforce explicit reasoning steps and chain-of-thought generation.
Distinguishing note: Distinct from general prompt engineering; specifically targets the structural injection of reasoning steps during model training or inference.
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This 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 enables reasoning configuration by injecting specific formatting tags into the model output stream, forcing the generation of explicit reasoning steps before final answers.