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Resources for fine-tuning, compressing, and aligning machine learning models.
Distinguishing note: Focuses on the technical optimization and training of models rather than application development or prompt engineering.
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This project serves as a comprehensive, static directory of external resources dedicated to the study and application of large language models. It functions as a centralized discovery point for developers and researchers, aggregating foundational academic papers, technical documentation, and specialized tools within a structured, version-controlled knowledge base. The repository distinguishes itself through a multi-level classification system that organizes diverse technical domains, ranging from model training frameworks and inference optimization to AI safety and hallucination detection. By
Identifying resources for fine-tuning, compressing, and aligning large language models to improve performance while managing computational and data requirements.