• pretraining — The first, biggest phase of building a model: training it on enormous amounts of text so it learns language, facts, and reasoning in general.

Merging during pre-training rather than after it reduced loss spikes and stabilized GradNorm, and a constant learning rate plus merging matched decay schedules while making final performance predictable earlier in a run.

• articleThe Hidden Obstacles Of Domain Adaptation In Llms
• articleWhy Methods Like Qlora Fall Short In Domain Knowledge Injection 2
• articleOptimizing Arcee Foundation Models On Intel Cpus
• articleVirtuoso Lite Virtuoso Medium V2 Distilling Deepseek V3 Into 10b 32b Small Language Models Slms

• articleMeet Mergekit V0 1 Arcee Fusion Expanded Model Support Multi Gpu AccelerationArcee AI editorial sitemap
• articleTutorial Tutorial How To Get Started With Evolutionary Model MergingArcee AI editorial sitemap
• articleMix Data Or Merge Models Optimizing For Diverse Multi Task Learning 2024 10 15Cohere editorial sitemap

Checking sign-in…

Loading comments…