English(EN)DynaMiCS: Fine-Tuning LLMs with Performance Constraints Using Dynamic Mixtures
LoRA技术赋能大型AI模型高效微调
作者PulseAugur 编辑部·[7 个来源]·
多篇文章讨论了大型语言模型的微调,特别关注LoRA(低秩适配)技术。LoRA通过仅训练一小部分参数来实现大型模型的有效适配,使其在性能较低的硬件上也能实现。该方法与需要大量计算资源的全量微调形成对比。文章还涉及了Adam等优化算法,这对于这些大型模型的实际训练至关重要,并探讨了机器学习模型的更广泛发展历程。
AI
Multi-domain fine-tuning of large language models requires improving performance on target domains while preserving performance on constrained domains, such as general knowledge, instruction following, or safety evaluations. Existing data mixing strategies rely on fixed heuristic…
Medium — fine-tuning tag
TIER_1English(EN)·Mohsen Kheirandishfard·
<p><em>Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. <a href="https://github.com/HexmosTech/git-lrc" rel="noopener noreferrer">Star Us</a> to help devs discover the project. Do give it a try and share your feedback for impr…
dev.to — LLM tag
TIER_1English(EN)·Hiroki Kameyama·
<h2> Introduction </h2> <p>In <a href="https://dev.to/hiroki-kameyama/mlops-llmops-cicd-pipelines-for-continuous-quality-assurance-46d1">Chapter 5 (MLOps)</a>, we built a CI/CD pipeline. This chapter explores a different approach: fine-tuning — training the model itself on your o…
dev.to — LLM tag
TIER_1English(EN)·Devanshu Biswas·
<p>Fine-tuning a large model used to mean one painful thing: update every weight in it, keep a full copy per task, and pay for the GPUs to do it. A 7-billion-parameter model has 7 billion knobs. The Adam optimizer keeps two extra numbers per knob, so you're suddenly holding rough…