Context packing solves LLM context window limits by using a secondary model to summarize conversation history when token counts approach the limit. The technique replaces lengthy message histories with compressed summaries, achieving up to 96% token reduction while preserving essential information. Implementation involves configuring Docker Model Runner with multiple models, setting token thresholds, and automatically triggering summarization when limits are reached. This approach enables longer conversations with small local models without performance degradation.
Table of contents
Understanding context windows and their limits in local LLMsUsing context packing to fit more information into limited context windowsApplying context packing to the assistant’s codeSummary381 Impressions