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STREAMINGLLM
Run LLMs on streaming text with millions of tokens
MIT
ABOUT
Standard LLMs have a fixed context window — tokens beyond the window cause OOM errors or force expensive re-computation. StreamingLLM enables LLMs to process streaming, infinitely-long text by keeping only a sliding window of recent tokens plus the initial "attention sink" tokens. It leverages the discovery that the first few tokens naturally attract disproportionate attention (the attention sink phenomenon), allowing the model to maintain stable generation quality over millions of tokens without recomputing the full history. This works with Llama 2, MPT, Falcon, Pythia, and other open models without retraining.
INTEGRATION GUIDE
1. Process a 100K-token codebase diff in a single LLM pass without hitting context limits
2. Run a live transcription system where the LLM maintains state across hours of streaming audio
3. Analyze large log files by streaming through millions of tokens with consistent performance
4. Build a chat application with infinite conversation history that stays responsive
5. Enable long-document question answering on entire books without chunking or retrieval
TAGS
inferencelong-contextstreamingattentionllmoptimizationmit