1. Pruning
28 |Pruning is a technique used to remove less important connections in a neural network, reducing its size and complexity. This can lead to faster training and inference times, as well as lower memory requirements.
29 |Optimizations in Language Models
26 |2. Quantization
32 |Quantization is the process of converting continuous values into discrete values, often to reduce the memory and computational requirements of a model. This can lead to faster training and inference times, as well as lower memory requirements.
33 |Capabilities of New Language Models
37 |1. Text Generation
39 |New language models can generate coherent and contextually relevant text, making them useful for tasks such as content creation, summarization, and translation.
40 |2. Question Answering
43 |Advanced language models can understand and answer questions based on a given context, making them valuable for applications like chatbots and customer support.
44 |Explaining Transformer Architecture to a 10-Year-Old
48 |1. Transformers are like LEGO blocks
50 |Imagine you have a bunch of LEGO blocks. You can stack them together in different ways to create various shapes and structures. Transformers work similarly, using building blocks called "layers" to process and understand text.
51 |2. Transformers are good listeners
54 |Transformers are great at paying attention to different parts of a sentence. They can figure out which words are important and how they relate to each other, helping them understand the meaning of the text.
55 |