Language models get unlimited context with StreamingLLM
One of the big challenges of LLMs is their limited context. When you want to work on a very long text, such as books or long articles.
There are several different techniques that can help address this limitation, but they have shortcomings such as low precision and high compute and memory requirements.
StreamingLLM, a technique developed by researchers at Meta AI, MIT, and Carnegie Mellon University, can impressively extend the context of LLMs to millions of tokens without the need for changes to the model or high memory and compute costs.
StreamingLLM leverages “attention sinks,” a phenomenon in language models that gives a lot of attention value to the first few tokens. By preserving attention sinks when moving the context window of the LLM, StreamingLLM maintains the quality of the model without the need to discard and recompute the KV value of preserved tokens.
For more on StreamingLLM and its implementation, read the full article on TechTalks.
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