Extending the Context Length of LLMs

The post explains techniques to extend the context length of large language models (LLMs), highlighting methods like sparse attention and flash attention. These techniques help manage the computational complexity associated with processing longer context windows, making it feasible to handle extensive tokens without a drastic increase in cost. The importance of optimizing positional embeddings, particularly rotary positional embeddings (RoPE), is also discussed to maintain the relative position and relation of tokens.