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Fine-tuning Models with Matryoshka Embeddings
The chapter explores the process of fine-tuning models using Matryoshka embeddings, emphasizing equal weights for optimal performance. It delves into dataset curation, discussing the importance of data work and the challenges of limited data in achieving optimal model performance. The conversation transitions to the benefits and challenges of using synthetic data in training models, highlighting the incorporation of synthetic queries for dataset enhancement.