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The main functional layer of a transformer is anattentionmechanism. When you enter an input, the model tends to most important parts of the input and studies it contextually. A transformer can traverse long queues of input to access the first part or the first word and produce contextual outpu...
If we analyze human cognition, we will notice that all human cognitive ability sums down to one crucial feature called ‘Attention‘. Not only is attention important for efficient use of resources, but it is also the main ingredient of semantic understanding of raw sensory...
models no longer have to dedicate the same attention to all inputs and can focus on the parts of the input that actually matter. This representation of what parts of the input the neural network needs to pay attention to is learnt over time as the model sifts and analyzes mountains of dat...
Let’s take the encoder-decoder framework as an example since it is within such a framework that the attention mechanism was first introduced. If we are processing an input sequence of words, then this will first be fed into an encoder, which will output a vector for every element in the...
which is usually an advanced multihead self-attention mechanism. This mechanism enables the model to process and determine or monitor the importance of each data element.Multiheadmeans several iterations of the mechanism operate in parallel, enabling the model to examine different relationships between ...
VAE is another type of Gen AI model that consists of two components: an encoder and a decoder. Here’s how they work together: Encodercompresses input data into a simplified representation. Decoderreconstructs data from this simplified representation and adds details to produce a new output. ...
LLMs are a product of machine learning technology, utilizing neural networks whose operations are facilitated bytransformers: attention-layer-based encoder-decoder architectures. Transformers were invented in 2017 by deep-learning visionaryAshish Vaswaniet al,as introduced in a paper calledAttention Is All...
Understanding the mathematical concept of attention, and more specifically self-attention, is essential to understanding the success of transformer models in so many fields. Attention mechanisms are, in essence, algorithms designed to determine which parts of a data sequence an AI model should “pay ...
作者观察到的现象和Efficient Streaming Language Models with Attention Sinks里展示的一样,对于Decoder Only的OpenLLaMA 1.4B 的模型,第一个token被attention到的概率是异常的,但是加了作者提出的StableMask以后attention的分布就正常了。仔细想想,和BERT的Encoder Only结构不同,之所以在Decoder Only中第一个token更容易成...