Future of Generative AI for Enterprises: The Game-Changing Potential of Small Language Models

In 15 months, Large Language Models like GPT-4 have surged in prominence, boasting parameter counts that exceed a trillion. However, amid the staggering scale of LLMs, Small Language Models (SLMs) present a contrasting approach. With SLMs numbering only in the tens compared to the 729,318 LLMs, these specialised models are demonstrating the potential of precision and targeted application in reshaping enterprise AI solutions.

Is bigger necessarily better for enterprise applications?

Small Language Models (SLMs) are characterized by their compact architecture and reduced computational power, SLMs are engineered to efficiently perform specific language tasks. This efficiency and specificity distinguish them from their Large Language Model (LLM) counterparts, like GPT-4, which are trained on vast and diverse datasets.

1. SLMs are designed for specific, often niche purposes within an enterprise. For example, a domain-specific model for the legal industry can navigate intricate legal jargon and concepts more adeptly than a general-purpose LLM, providing more accurate and relevant outputs for legal professionals.
2. The smaller size of SLMs translates directly into lower computational and financial costs. Training, deploying, and maintaining an SLM is considerably less resource-intensive, making it a viable option for smaller enterprises or specific departments within larger organizations.
3. SLMs can be deployed on-premises or in private cloud environments, reducing the risk of data leaks and ensuring that sensitive information remains under the control of the organization. This aspect is particularly appealing for industries dealing with highly confidential data, such as finance and healthcare.
4. SLMs offer adaptability and responsiveness crucial for real-time applications. Their smaller size allows for lower latency in processing requests, making them ideal for AI customer service and real-time data analysis.

Microsoft has unveiled the Phi-3 family of small language models (SLMs), these models, designed to be highly capable yet cost-effective, outperform both models of the same size and even larger ones in various benchmarks, including language, coding, and math.

Phi-3-mini (3.8B parameters)

• Platforms: Available on Microsoft Azure AI Studio, Hugging Face, and Ollama.
• Variants: Two context-length options (4K and 128K tokens).
• Features: Supports up to 128K tokens with minimal quality impact, instruction-tuned, optimized for ONNX Runtime, and compatible across GPU, CPU, and mobile hardware. Also available as an NVIDIA NIM microservice with a standard API interface.
• Example: ITC’s Krishi Mitra app for farmers in India enhances efficiency and accuracy using Phi-3.

• Legacy:Building on the success of Phi-2, which saw over 2 million downloads, Phi-3 represents a leap forward in SLM capabilities

Gemma 2B and Gemma 7B

• Model Sizes: Gemma is available in two sizes, Gemma 2B and Gemma 7B, with pre-trained and instruction-tuned variants.
• Tools and Resources: A new Responsible Generative AI Toolkit for creating safer AI applications.Toolchains for inference and supervised fine-tuning across major frameworks: JAX, PyTorch, and TensorFlow (Keras 3.0).Ready-to-use Colab and Kaggle notebooks and integration with tools like Hugging Face, MaxText, NVIDIA NeMo, and TensorRT-LLM.
• Deployment: Models can run on laptops, workstations, or Google Cloud, with easy deployment on Vertex AI and Google Kubernetes Engine (GKE).
• Commercial Usage: Permitted for responsible commercial use and distribution for all organizations.

These models may not perform well outside their specific domain of training, lacking the broad knowledge base that allows large language models (LLMs) to generate relevant content across a wide range of topics. However, as enterprises incorporate GenAI-driven solutions into their specialized workflows, tailored models promise not only to deliver superior accuracy and relevance but also to amplify human expertise in ways that generic models cannot match. By focusing on specific domains, these specialized models can provide enhanced performance and insights, ultimately leading to more effective and efficient AI-driven solutions in enterprise environments.