1. Prediciton in Decoder
在前面GPT summary里面对GPT的模型有一个综合的介绍，这里用一个fake example来解释一步步GPT是怎么做的，self attention是怎么计算的，KV cache是怎么回事。
GPT是decoder only的模型，根据前面的token来预测下一个token。比如有一个句子 "it is sunny today."，现在有初始输入 …
Image Generation 2: Latent Diffusion model / Stable Diffusion
In the previous blog we introduced diffusion model (DDPM) which is to learn the step (time \(t\)) and the noise function (NN model) by adding Gaussian noise to an image step by step and reversing the process by denosiing from Gaussian noise to an image. Diffusion model is the most …
Image Generation 1: Diffusion model
The previous notes introduced the text generation models (GPT family). This reading note is about image generator papers.
Similar to text generator which generate the next token, OpenAI has image-GPT which is a large transformer trained on next pixel prediction in which the pixels are concated into a vector to …
GPT-1, GPT-2, GPT-3, InstructGPT / ChatGPT and GPT-4 summary
1. GPT-1
Improving Language Understandingby Generative Pre-Training
What the problem GPT-1 solve?
Before GPT-1, NLP was usually a supervised model. For each task, there are some labeled data, and then develop a suoervised model based on these labeled data. There are several problems with this approach: First, labeled data is …
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