fine-tuning
fine-tuning on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on fine-tuning in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around fine-tuning, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Presentation: Engineering AI for Creativity and Curiosity on Mobile
Join us for a compelling presentation by Bhavuk Jain, exploring the engineering behind bringing powerful AI to mobile devices. Jain details the challenges and solutions in translating foundational AI into scalable products like AI Wallpapers and Circle to Search, focusing on runtime guardrails, fine-tuning, and OS integration. This session offers critical insights for engineering leaders navigating the balance between user experience, model latency, and infrastructure costs—essential for delivering safe and reliable AI experiences.
The qlora 2e-4 default is wrong under 10k samples and nobody talks about it [D]
Fine-tuning QLoRA models on smaller datasets—less than 10,000 samples—often leads to unexpected results. The pervasive default learning rate of 2e-4, widely promoted across tutorials and documentation, can actually trigger overfitting. Extensive experimentation reveals that a starting learning rate of 1e-4 or lower, combined with increased epochs, consistently yields significantly improved evaluation metrics. This adjustment, easily implemented, can save practitioners considerable time and frustration, as detailed in a recent discussion about ECCV expenses.