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แบ่งปัน
Introducing a novel transformer architecture, Differential Transformer, designed to improve the performance of large language models. The key innovation lies in its differential attention mechanism, which calculates attention scores as the difference between two separate softmax attention maps. This subtraction effectively cancels out irrelevant context (attention noise), enabling the model to focus on crucial information. The authors demonstrate that Differential Transformer outperforms traditional transformers in various tasks, including long-context modeling, key information retrieval, and hallucination mitigation. Furthermore, Differential Transformer exhibits greater robustness to order permutations in in-context learning and reduces activation outliers, paving the way for more efficient quantization. These advantages position Differential Transformer as a promising foundation architecture for future large language model development. Read the research here: https://arxiv.org/pdf/2410.05258…
Prune This! PyTorch and Efficient AI
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เนื้อหาจัดทำโดย Brian Carter เนื้อหาพอดแคสต์ทั้งหมด รวมถึงตอน กราฟิก และคำอธิบายพอดแคสต์ได้รับการอัปโหลดและจัดหาให้โดยตรงจาก Brian Carter หรือพันธมิตรแพลตฟอร์มพอดแคสต์ของพวกเขา หากคุณเชื่อว่ามีบุคคลอื่นใช้งานที่มีลิขสิทธิ์ของคุณโดยไม่ได้รับอนุญาต คุณสามารถปฏิบัติตามขั้นตอนที่แสดงไว้ที่นี่ https://th.player.fm/legal
Both sources explain neural network pruning techniques in PyTorch. The first source, "How to Prune Neural Networks with PyTorch," provides a general overview of the pruning concept and its various methods, along with practical examples of how to implement different pruning techniques using PyTorch's built-in functions. The second source, "Pruning Tutorial," focuses on a more in-depth explanation of pruning functionalities within PyTorch, demonstrating how to prune individual modules, apply iterative pruning, serialize pruned models, and even extend PyTorch with custom pruning methods.
Read this: https://towardsdatascience.com/how-to-prune-neural-networks-with-pytorch-ebef60316b91
And the PyTorch tutorial: https://pytorch.org/tutorials/intermediate/pruning_tutorial.html
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