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In streaming settings, StreamingLLM outperforms the sliding window recomputation baseline by up to 22.2x speedup.
mit-han-lab • GitHub - mit-han-lab/streaming-llm: Efficient Streaming Language Models with Attention Sinks
StreamingLLM can enable Llama-2, MPT, Falcon, and Pythia to perform stable and efficient language modeling with up to 4 million tokens and more.
mit-han-lab • GitHub - mit-han-lab/streaming-llm: Efficient Streaming Language Models with Attention Sinks
Often several copies and
David Uhlman • Hacking Healthcare: A Guide to Standards, Workflows, and Meaningful Use
Stanford STORM
storm.genie.stanford.eduGitHub - mit-han-lab/streaming-llm: Efficient Streaming Language Models with Attention Sinks
mit-han-labgithub.comA big distinction of a MapReduce environment is the specific ability to handle unstructured text.
Bill Franks • Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley and SAS Business Series)
On Scalable Logging Platform