Researchers at NVIDIA introduced a new architectural optimization technique named FFN Fusion, which addresses the sequential bottleneck in transformers by identifying FFN sequences that can be executed in parallel. This approach emerged from the observation that when attention layers are removed using a Puzzle tool, models often retain long sequences of consecutive FFNs. These sequences show minimal interdependency and, therefore, can be processed simultaneously. By analyzing the structure of LLMs such as Llama-3.1-405B-Instruct, researchers created a new model called Ultra-253B-Base by pruning and restructuring the base model through FFN Fusion. This method results in a significantly more efficient model that maintains competitive performance……
Learning and Practicing 🎖️🎖️🎖️
🚨Tutorial to Create a Data Science Agent: A Code Implementation using gemini-2.0-flash-lite model through Google API, google.generativeai, Pandas and IPython.display for Interactive Data Analysis [Colab Notebook Included]
