  While Large Language Models show remarkable performance in natural language
understanding, their resource-intensive nature makes them less accessible. In
contrast, smaller language models such as MiniCPM offer more sustainable
scalability, but often underperform without specialized optimization. In this
paper, we explore the enhancement of smaller language models through the
improvement of their text embeddings. We select three language models, MiniCPM,
Phi-2, and Gemma, to conduct contrastive fine-tuning on the NLI dataset. Our
results demonstrate that this fine-tuning method enhances the quality of text
embeddings for all three models across various benchmarks, with MiniCPM showing
the most significant improvements of an average 56.33% performance gain. The
contrastive fine-tuning code is publicly available at
https://github.com/trapoom555/Language-Model-STS-CFT.
