Here are my thoughts on why I believe Small Language Models might be the next big thing.
Large Language Models have been in the headlines for almost two years now, and now I see a shift where Small Language Models (SLMs) are stepping into this spotlight. One of the biggest bottlenecks with LLMs has been that they are computed and cost-heavy, and that's where SLMs are becoming more popular in the applied AI community:
⏭ 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: SLMs due to their significantly smaller size compared to LLMs have much reduced computational needs. With reduced computing, SLMs can be easily embedded in edge devices, where running them locally is the need. This also proves to be a better ROI for resource-sensitive apps.
⏭ 𝐂𝐨𝐬𝐭-𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐧𝐞𝐬𝐬: SLMs being cheaper to operate makes them an easier choice for startups to quickly iterate over their solution. This also opens up new opportunities for developers to run their experiments locally.
⏭ 𝐃𝐨𝐦𝐚𝐢𝐧 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Thanks to their size, it is much easier to fine-tune them with proprietary data, and make them niche-efficient.
⏭ 𝐒𝐩𝐞𝐞𝐝 𝐚𝐧𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧: SLMs not only speed up the training process but also enhances their potential for accuracy, as they are trained on high-quality data.
⏭ 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: SLMs have a very simple architecture that facilitates better security measures and easier compliance with privacy regulations.
Some popular SLMs you should check out: Llama 2 7B, Phi2 , Orca, Stable Beluga 7B, X Gen, Alibaba’s Qwen, Alpaca 7B, MPT, Falcon 7B, Zephyr