The rise of small language model (SLMs)
Here is the translation:
Summary:
- Small language models are trained using small-scale, high-quality synthetic data and do not rely on large language models.
- There are many application scenarios for small language models, primarily in the Edge AI field, such as smart home devices, IoT devices, medical devices, etc.
- The attention of manufacturers is focused on small language models mainly because of the decrease in training time and inference costs, as well as an improvement in privacy and environmental friendliness.
- In addition to Meta’s Llama series, there are also Google’s Gemma series, Microsoft Phi series, and NVIDIA’s Mistral-NeMo-MiniTron, etc., small language models available for selection.
Translation
总结如下：
- 小型语言模型是使用小规模、高质量合成数据进行训练的小型模型，不依赖于大型语言模型。
- 小型语言模型有许多应用场景，主要是在Edge AI的范畴内，例如智慧家具设备、物联网设备、医疗保健设备等。
- 小型语言模型受到厂商关注主要因为训练时间和推理成本的下降，以及隐私性和环保友善的提升。
- 除了Meta的Llama系列之外，还有Google的Gemma系列、Microsoft Phi系列、NVIDIA开发的Mistral-NeMo-MiniTron等小型语言模型可供选择。
这部影片主要介绍了小型语言模型的特点、应用场景以及优势，希望通过此影片可以了解更多关于Edge AI和小型语言模型的信息。
Reference:
https://www.itpro.com/technology/artificial-intelligence/small-language-models-set-for-take-off-next-year https://siliconangle.com/2024/12/13/microsoft-releases-phi-4-language-model-trained-mainly-synthetic-data/