The second part of the blog delves into financial topics such as the MGM hack, negative equity risk premium estimates, the EV market, and rising insurance costs. It also touches on miscellaneous topics like charging EVs at home, the increase in the price of bananas at Trader Joe's, and the cost of tuition at some US universities. The post concludes with a study on the impact of layer pruning on Large Language Models (LLMs), suggesting that shallow layers may be crucial for LLMs and that there is potential for more efficient LLM designs.
Key takeaways:
- A simple layer-pruning strategy tested on Large Language Models (LLMs) shows minimal performance loss until a significant portion of the model is pruned.
- Current LLMs might not be fully utilizing deeper layers, and techniques like pruning and quantization can greatly improve efficiency.
- Layers are pruned based on similarity, with minimal finetuning afterwards.
- Removing deep layers has little effect on model performance, suggesting potential for more efficient LLM designs.