Summary
A user investigated how attention implementation (dense vs. sparse) affects DeepSeek V3.2's reasoning performance, particularly when run with dense attention like in llama.cpp. While earlier tests on smaller graphs showed no difference, new benchmarks using much larger lineage graphs revealed that dense attention slightly degrades the model's reasoning capabilities. This indicates that specific implementation choices can subtly impact the performance of local large language models.
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