llama 3.3 70b q4_k_m perplexity on log files with timestamps vs without

tested this yesterday because our log ingestion pipeline was hitting weird coherence issues around 20k tokens. ran the same 50 log files through the model twice - once with timestamps intact, once with timestamps stripped. results on llama 3.3 70b q4_k_m: - logs WITH timestamps: perplexity starts degrading around 19.4k tokens, breaks hard at 21.2k - logs WITHOUT timestamps: holds coherence to 24.7k tokens, gradual degradation after that - perplexity delta at the 20k mark: 3.8 points think the issue is that timestamps create a ton of tokens that carry almost zero semantic information (like `2024-01-15T08:23:47.291Z` is 12+ tokens depending on the format) but the model still has to track them in the attention window. so you're burning through your context budget on noise. also tested with different timestamp formats: - ISO 8601 with milliseconds: breaks at 19.4k (worst) - Unix epoch timestamps: breaks at 20.1k - Stripped to just HH:MM:SS: holds to 22.8k - No timestamps: holds to 24.7k (best) for anyone doing log analysis with llms - strip the timestamps before you hit the context window, or at least simplify them to just time-of-day. you'll get way better coherence on the actual error messages and stack traces.