Summary Let's build GPT: from scratch, in code, spelled out. - YouTube (Youtube) www.youtube.com
20,949 words - YouTube video - View YouTube video
One Line
The text covers the process of building a transformer model from scratch, including topics such as fine-tuning, model architecture, hyperparameters, self-attention, token embedding, and training methods.
Slides
Slide Presentation (9 slides)
Key Points
- Building GPT from scratch and the importance of fine-tuning stages for tasks beyond language modeling
- Training process, model size, and challenges of training large models
- Transformer model trained on Shakespearean text and its capabilities
- Impact of hyperparameters on model performance and changes made to improve performance
- Structure of the transformer model, including heads, blocks, and communication channels
- Self-attention mechanism, position encoding, and token embeddings in language modeling
- Weighted aggregation and interaction strengths between tokens in self-attention blocks
- Overfitting in transformer training and the use of validation data to assess it
Summary
1114 word summary
The video covers the process of building GPT from scratch. The lecturer mentions that the lecture is coming to an end and briefly mentions the importance of fine-tuning stages for tasks beyond language modeling. The lecturer also mentions that the code and notebook will be released. The lecturer discusses the training process and the size of the models, mentioning that GPT is much larger. The lecturer explains the different stages of fine-tuning and how they align the model to be an assistant. The lecturer also discusses the challenges of training such large models and the hyperparameters used. The lecturer compares the model they built to GPT and explains the differences in architecture. In this video, the presenter demonstrates the capabilities of a transformer model trained on Shakespearean text. The model generates nonsensical but recognizable outputs. The presenter also discusses the impact of different hyperparameters on the model's performance. They mention that the training process takes about 15 minutes on a powerful GPU. The use of dropout and layer normalization techniques is also highlighted. The presenter makes changes to the model architecture to improve performance, including increasing the number of layers and changing the block size. They compare the validation loss of the modified model to the original one. The use of layer normalization and residual connections is explained as techniques to optimize deep neural networks. The transformer structure consists of multiple heads and blocks for intercommunication and computation. The head size is set to eight, and there are four communication channels. Each block uses multi-head self-attention for communication and a feed-forward network for computation. The goal is to improve the validation loss, which has decreased from 2.28 to 2.24. The network incorporates multiple independent channels of communication to gather different types of data. The multi-head attention is implemented by running self-attention heads in parallel. The network is trained with a decreased learning rate and increased number of iterations. The model also includes position embedding and cropping the context to avoid exceeding the block size. The self-attention component is plugged into the network by creating a head module. Scaling is used in the attention mechanism to control variance. The attention module also involves dividing by the square root of the head size. Attention can be self-attention or cross-attention, depending on whether the keys, queries, and values come from the same source or different sources. Decoder blocks are used for decoding language and have a triangular structure to prevent nodes from the future communicating with nodes from the past. Encoder blocks allow all nodes to fully communicate. In language modeling, there are 32 nodes that are being processed, divided into four separate pools of eight. The structure of the directed graph allows for communication between nodes, but this constraint can be changed in other cases. Attention is a communication mechanism between nodes that allows for information aggregation. Nodes do not have a notion of space by default, so position encoding is necessary. Attention can be applied to any directed graph. Each node emits a query and a key vector, which are used to calculate the affinity between tokens. The affinity matrix is masked and normalized to control the flow of information. The self-attention mechanism allows for data-dependent information aggregation. The input tokens are embedded with token and position embeddings. Position embeddings encode the position of each token. A linear layer is used to interact with the embeddings. The text excerpt discusses the process of building GPT from scratch, focusing on token embedding, linear layers, and self-attention blocks. It explains the concept of weighted aggregation and how it is used to calculate interaction strengths or affinity between tokens. Additionally, it mentions the use of softmax and matrix multiplication to efficiently perform weighted aggregation. The text also discusses the idea of averaging vectors to create a bag of words representation. Finally, it mentions the limitations of this approach and the inability to communicate with future tokens. In this text excerpt, the author discusses the need for tokens to communicate with each other in a specific way. They mention the desire to create a self-attention block and introduce a mathematical trick used in self-attention. The author also mentions the script output, the importance of memory efficiency in Python, and the use of GPUs. They explain the training loop, the use of optimization algorithms, and the process of generating text. The author concludes by discussing the goal of training the model and improving its performance. The excerpt discusses generating and evaluating a model, specifically focusing on the loss function and reshaping the data. The author mentions the expectation for the loss to be around 4.1217, but it is currently 4.87. They also discuss reshaping the data to match the desired dimensions for the model. The excerpt further explains the use of negative log likelihood loss and cross entropy in evaluating the model's predictions. The author goes on to discuss the implementation of a language model using a transformer and the process of feeding input into the neural network. They explain the concept of batching and the importance of context length in making predictions. The author concludes by mentioning the sampling of chunks from the training set and training on multiple examples simultaneously. We will use spear text to understand overfitting. We will input text sequences into the transformer for training and pattern learning. The validation data will be kept separate to assess overfitting. The text will be encoded into a tensor for training. We will split the dataset into training and validation sets. We will use character-level tokenization for simplicity. Different encoding methods exist, such as subword encoding and word-level encoding. We will focus on character-level language modeling. The code will be shared in a Google Colab notebook. The transformer model will be trained on the Tiny Shakespeare dataset. The code is available on GitHub. The transformer will generate Shakespeare-like text based on the trained model. The transformer models the sequence of characters and predicts the next character based on the given context. The transformer architecture is based on the paper "Attention is All You Need". ChatGPT is a language model that generates responses to prompts. It can provide multiple answers to a single prompt. Cha generated different outcomes when given the same prompt on two occasions. One prompt asked Cha to write a small hi coop about how people understand Ai and how it can improve the world. The response mentioned that Ai brings prosperity and its power should be embraced. The speaker explains that chat has become popular in the Ai community and allows interaction. The document discusses building a Generatively Pretrained Transformer (GPT) based on the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3.