In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
"When you look at the date on the bottom of the 16oz bottles, some were manufactured in the 1960s and 70s. It's really cool to see them still going through our washer today. We'd love to keep the tradition alive as long as we can."
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需求遍历弹出条件栈类型下一个更大元素的索引倒序栈顶 ≤ 当前 → 弹出严格单调递减栈下一个更大或相等元素的索引倒序栈顶 < 当前 → 弹出非严格单调递减栈下一个更小元素的索引倒序栈顶 ≥ 当前 → 弹出严格单调递增栈下一个更小或相等元素的索引倒序栈顶 当前 → 弹出非严格单调递增栈上一个更大元素的索引正序栈顶 ≤ 当前 → 弹出严格单调递减栈上一个更大或相等元素的索引正序栈顶 < 当前 → 弹出非严格单调递减栈上一个更小元素的索引正序栈顶 ≥ 当前 → 弹出严格单调递增栈上一个更小或相等元素的索引正序栈顶 当前 → 弹出非严格单调递增栈
，更多细节参见91视频
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./build/parakeet model.safetensors audio.wav --vocab vocab.txt --gpu