The GPT-2 model works by processing input tokens through a series of transformer blocks, each of which applies self-attention and then passes the result through a neural network layer. The model retains key and value vectors for each token, which are used in subsequent iterations. The model also uses a fully-connected neural network with two layers, the first of which is four times the size of the model. The second layer projects the result back into the model dimension. The author also explains the concept of "masked self-attention", which prevents the model from peaking at future words. The article concludes with a discussion of the various applications of the GPT-2 model, demonstrating its versatility and potential.
Key takeaways:
- The OpenAI GPT-2 model is a transformer-based language model that uses a decoder-only architecture, similar to the original transformer model, but trained on a larger dataset.
- The GPT-2 model uses self-attention to process each token in a sequence, taking into account the context of the token in the sequence. This is done by creating query, key, and value vectors for each token, scoring each token against all other tokens, and summing up the value vectors weighted by their scores.
- The GPT-2 model can be used for various applications beyond language modeling, such as machine translation, summarization, transfer learning, and music generation.
- The GPT-2 model has 124M parameters, including weight matrices for each transformer block, a token embedding matrix, and a positional encoding matrix.