Bamba: An open-source LLM that crosses a transformer with an SSM

Join the first vLLM meetup in NYC on May 7 at the IBM Innovation Studio at 1 Madison Avenue in Manhattan! Hosted by IBM and Red Hat, the event will feature technical talks and discussion on how to optimize LLM inference for performance and efficiency. The transformer architecture behind today’s large language models has shown an uncanny ability to generate human-like text. Part of its effectiveness comes from its self-attention mechanism, which allows the model to weigh all the words in an input sequence when generating a response. The problem comes as conversations get longer. Because the model holds the running sequence in memory as it responds, the cumulative cost of generation grows quadratically. If the size of the context window doubles, the cost of processing the context and generating a response doesn’t just double — it quadruples. This “quadratic bottleneck” is often behind that frustrating lag between asking the model a question and getting an answer. It also creates a lot ... Read full article.