We know that small, large language models (LLMs) have fewer parameters than general-purpose LLMs and that lightweight models can help reduce the time and cost of machine learning. Even if you don’t need a supercomputer, a small language model can still reduce your data center footprint. For these reasons, sLLMs are likely to have lower carbon emissions compared to LLMs. BHSN’s sLLM utilizes high-quality legal-specific data, fine-tuning, and a proprietary pre-training methodology* to enhance accuracy, competing with LLMs. A small, lightweight large language model may be small in size, but it can have a big impact.