This article explains how to train large language models efficiently using LLaMA‑Factory, covering low‑resource training challenges, memory‑saving optimizations for parameters, gradients and activations, framework features, quick‑start guidance, performance tuning, real‑world case studies, and a detailed Q&A.
This article presents a low‑resource NLP pretraining approach that combines transformer‑based language modeling with contrastive vector learning, details the unsupervised sample‑pair construction, introduces a camel‑shaped masking distribution, and demonstrates through extensive experiments that the resulting model achieves strong zero‑shot NLU, NLG, and retrieval performance while requiring minimal compute and data.
This article reviews recent advances in low‑resource text‑to‑speech synthesis, covering the background of TTS, challenges in data‑ and compute‑limited scenarios, and detailed descriptions of FastSpeech, LightTTS, LightBERT, and related lightweight vocoder techniques, along with experimental results and future research directions.
