Sep 29, 2023 · We introduce StreamingLLM, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence lengths ...
Nov 28, 2023 · The paper focuses on the challenge of efficiently scaling and generalizing beyond the training sequence length for large language models.
We show that StreamingLLM can enable Llama-2, MPT, Falcon, and Pythia to perform stable and efficient language modeling with up to 4 million tokens and more. In ...
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What are attention sinks?
What is streaming llm?
We show that StreamingLLM can enable Llama-2, MPT, Falcon, and Pythia to perform stable and efficient language modeling with up to 4 million tokens and more. In ...
It's my understanding that in regular non-sliding window context models the llm is able to pay attention to any part of the input when generating the output.
In our paper, the “attention sinks" are initial tokens that disproportionately attract attention from subsequent tokens. Introducing a dedicated sink token ...
Oct 2, 2023 · We introduce StreamingLLM, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence lengths ...
Duration: 32:27
Posted: Oct 14, 2023
Posted: Oct 14, 2023
New paper with example code claims huge context with minimal changes. https://github.com/mit-han-lab/streaming-llm
StreamingLLM is introduced, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence ...