Explain Transformers and deploy an LLM safely

1) Transformer basics Problem solved compared with RNNs: RNNs process tokens sequentially, which prevents parallelization and makes modeling long-range dependencies difficult due to vanishing gradients. Transformers address this by using self-attention, which computes interactions between all token pairs in parallel and allows direct modeling of long-range dependencies. Main components: Token embeddings and positional information: Token embeddings map each discrete token to…