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An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning
Fine-tuning is an important step in adapting foundation models such as large language models to downstream tasks. To make this step more accessible to users with limited computational budgets, it is crucial to develop fine-tuning methods that are memory and computationally efficient. Sparse Fine-tun
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