<ul><li>Large language models (LLMs) face increasing computational and memory demands.</li><li>Sparse Transformers introduce sparsity in attention mechanisms to improve efficiency.</li><li>Key concepts in Sparse Transformers include local attention, strided attention, block sparse patterns, and dilated attention.</li><li>Sparse Transformers offer advantages such as reduced complexity, efficiency for long sequences, and improved scalability.</li></ul>

Day 29: Sparse Transformers: Efficient Scaling for Large Language Models

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