**The Rise of Small Language Models (SLMs) in Enterprise Applications**
Small Language Models (SLMs) are gaining traction among enterprises as a preferable alternative to Large Language Models (LLMs). Their appeal lies in their ability to offer enhanced control, simplified fine-tuning for specific domains, and robust data security—all at lower operational costs.
"Early adoption of SLMs is evident within enterprises, particularly as major cloud providers like AWS and Azure offer these models through hosted APIs,” explains Pushpraj Shukla, senior vice president of engineering and head of AI/ML at SymphonyAI. “We leverage these models for natural language understanding (NLU) tasks across various sectors including retail, financial services, and industrial applications. Interestingly, many users are often unaware that they are utilizing SLMs.”
**What Are Small Language Models?**
Typically, SLMs boast a size that is five to ten times smaller than their LLM counterparts and are often open-source projects. This compact size leads to significantly reduced energy consumption and allows for deployment on a single GPU. Given the ongoing shortage of these chipsets and the high costs associated with computational resources, this efficiency is particularly beneficial.
Despite their smaller footprint, SLMs can deliver performance levels that closely align with those of LLMs across a variety of NLU tasks. Their effectiveness greatly improves when they undergo fine-tuning for specialized applications, such as healthcare or software development. This fine-tuning process is significantly faster, often taking only minutes to several hours, compared to the tens of hours or days typically required for LLMs. Realistically, to achieve optimal results, an SLM often necessitates a dataset containing several hundred thousand examples.
**Advantages of SLMs**
SLMs offer faster training and inference times, resulting in lower latency. This characteristic makes them particularly suitable for resource-constrained environments. Gustavo Soares, global product manager at Dell Technologies, notes, “SLMs are especially advantageous for highly regulated industries like healthcare, where handling sensitive personal data requires strict compliance and data privacy. Their reduced complexity makes them an ideal choice for on-premises deployment.”
Several leading SLMs include:
- **Llama-2-13b** and **CodeLlama-7b** from Meta
- **Mistral-7b** and **Mixtral 8x7b** from Mistral
- **Phi-2** and **Orca-2** from Microsoft
“The Llama 2 SLMs have quickly become favorites in the open-source community since their launch in August 2023, showing outstanding performance across numerous NLU benchmarks,” Shukla adds. “However, the Mistral-7b model is gaining significant momentum, outperforming both Llama-13b and Llama-70b on several tasks.”
Mixtral, a mixture-of-experts model from Mistral, is currently generating considerable excitement. It utilizes eight underlying 7-billion parameter models and a routing mechanism, achieving performance levels that match or exceed that of GPT-3.5 across nearly all tasks. Furthermore, the Phi and Orca models from Microsoft excel in reasoning tasks and can be fine-tuned for domain-specific applications swiftly.
There is also a growing array of SLMs featuring fewer than one billion parameters, such as **DistilBERT**, **TinyBERT**, and **T5-Small**. While these models are optimized for limited applications—such as summarization—they are well-suited for environments with constrained computational power.
**Challenges with Adoption**
While the benefits of SLMs are significant, enterprises face notable challenges when integrating these models. The technology is still evolving, which can introduce unexpected changes and management complexities. A recommended strategy is designing systems that facilitate the swapping of different SLMs smoothly.
Additionally, leveraging this technology often demands specialized expertise, particularly in machine learning operations, a talent pool that is both limited and costly. Integrating SLMs with legacy systems can also prove challenging, necessitating the management of complex workflows for pre- and post-processing tasks to refine and adapt data effectively.
Enterprises must remain cognizant of the distinctions between SLMs and LLMs. Developers and enterprise users frequently express concerns about potential quality trade-offs when opting for SLMs instead of established closed-source models like GPT-4, which is widely regarded as a benchmark for NLU tasks.
To ensure that quality is not significantly compromised in the pursuit of speed and cost-effectiveness, enterprises need to implement robust evaluation methods to compare the performance of SLMs against that of LLMs. Such assessments typically rely on human judgment, making them complex yet necessary.
In response to these challenges, many companies are seeking the expertise of consultants or utilizing in-house specialists. Fortunately, startups are emerging to simplify this transition; for example, OctoAI is developing automations that streamline the hosting of fine-tuned models, while Databricks' acquisition of MosaicML aims to simplify the fine-tuning process, making SLM deployment more accessible for enterprises.
The trend towards SLMs indicates that as the technology matures, enterprises will increasingly harness these models to enhance operational efficiency and unlock new capabilities in natural language understanding.
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