Open-weight large language models (LLMs) such as LLaMA 2, LLaMA 3, and DeepSeek have quickly become attractive backbones for downstream NLP tasks, including sentiment analysis in both long-form reviews and short social media posts. However, full fine-tuning of these models remains computationally expensive and often impractical for academic research groups with limited hardware resources. This paper presents a comparative study of QLoRA-based sentiment adaptation for three open-weight LLM families, LLaMA 3, LLaMA 2, and DeepSeek, on two representative English benchmarks: the IMDB movie review dataset and a Twitter sentiment dataset. We apply a unified QLoRA pipeline that quantizes the backbone to 4-bit precision and trains low-rank adapters on top, enabling efficient fine-tuning on a single GPU. LLaMA 3 consistently achieves the best performance across both domains, reaching 91.2% accuracy and 0.908 F1 on IMDB and 85.6% accuracy and 0.849 F1 on Twitter. LLaMA 2 follows closely, while DeepSeek remains competitive but trails by 1–2 percentage points. Confusion matrix analysis reveals that all models struggle more with Twitter data due to its informal language and context-poor nature. Our findings provide practical guidance for practitioners choosing open LLM backbones for sentiment-related applications under compute constraints.
BAYAT et al. (Fri,) studied this question.