tuning
13 stories and discussions about tuning, aggregated from every source we track.
To answer the challenges of tuning a database
Optimizing code starts with measuring it, and a measurement is only useful if it is repeatable: a 2% improvement is invisible under 5% of noise. Yet on an …
Optimizing code starts with measuring it, and a measurement is only useful if it is repeatable: a 2% improvement is invisible under 5% of noise. Yet on an …
Automatic database management system tuning through large-scale machine learning Aken et al. , SIGMOD’17 Achieving good performance in DBMSs is non-trivial as they are complex systems with ma…
Optimizing code starts with measuring it, and a measurement is only useful if it is repeatable: a 2% improvement is invisible under 5% of noise. Yet on an …
On real multi-year Comcast support traffic, a fine-tuned verifier's gray-zone positive rate drifted 5x and the model went from helping to actively hurting. Here's the failure, and the three-detector monitor we built…
Online OS tuning can improve long-running services, but existing controllers are poorly matched to live hosts. They treat scheduler, power, memory, and I/O controls as black-box variables and optimize a scalar reward.…
Originally published at AI Frontier Post. Every production LLM feature eventually hits the same...
Contribute to Hantlowt/laya-studio development by creating an account on GitHub.
Empirical notes on fine-tuning π0.5 for a real manufacturing task: training duration, LoRA vs full fine-tuning, batch size, data quantity and quality, and how much your evaluation can actually resolve.
Empirical notes on fine-tuning π0.5 for a real manufacturing task: training duration, LoRA vs full fine-tuning, batch size, data quantity and quality, and how much your evaluation can actually resolve.