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Workflows for optimizing model behavior using preference data and instruction tuning.
Distinguishing note: Distinct from general training: focuses specifically on post-training alignment and preference optimization.
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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
A structured workflow for optimizing model behavior through preference data, automated feedback, and multi-turn instruction tuning.