If you've ever used Generative AI tools, there's a good chance you've interacted with a Large Language Model like GPT-4.
Large Language Models (LLMs), including GPT-4, Claude 3, and Gemini 1.5, often steal the spotlight due to the massive capabilities and easy access. However, they come with their own set of challenges.
These models require substantial computational power and so cannot be run on your regular desktop computers.
They require massive datacenters to run and are typically gatekept by major corporations through APIs or platforms such as ChatGPT.
This dependency not only leads to hefty costs but also binds organizations to the terms and capabilities of external providers like OpenAI, Microsoft, Amazon, and Google, affecting both data security and performance.
Enter small language models.
What are Small Language Models?
Small Language Models can be described as compact Generative AI models, typically ranging from a few million to just under ten billion parameters.
Unlike their larger counterparts that often require GPUs for processing, these smaller models are capable of running on the CPUs found in most modern computing devices. This makes it possible to use such models directly on local machines, including mobile phones, PCs, or laptops, for common business applications like market segmentation and sentiment analysis.
In contrast to large language models that operate on cloud-based systems—often incurring usage fees and involving data transmission to the cloud—small language models can be made to run on your own device, ensuring that your data remains private and secure.
Recently, Microsoft, Meta, and Google introduced their own compact yet powerful language models—Phi-3, Llama-3 8B, and Gemma respectively. These smaller models pack a punch, offering capabilities that can be tailored to individual needs without the hefty price tag and resource demands of their larger counterparts.
You might wonder why you'd consider a smaller model when you could have a "one-size-fits-all" solution like an LLM. The answer lies in efficiency and control.
Unlike their larger siblings, small language models can be operated on existing local infrastructure, negating the need for large amounts of compute. They can be fine-tuned quickly and cost-effectively to handle specialized tasks—be it sentiment analysis, content creation, or customer interaction—making them highly versatile for a range of business applications.
Why should your organization consider using Small Language Models over Large Language Models?
When considering the integration of AI into your business, it's crucial to weigh the benefits of Small Language Models (SLMs) against the commonly favored Large Language Models (LLMs).
SLMs offer several distinct advantages over their larger counterparts.
- Lower Cost
For starters, Small Language Models bring a notable reduction in computing or API costs as compared to Large Language Models.
Because they require less computational power, SLMs can run on devices with limited resources, such as laptops, desktops and mobile phones. This makes them particularly advantageous for small and medium-sized businesses that may not have access to extensive IT infrastructure.
Additionally, If you run it in your local infrastructure, you will not need to pay API fees that you will have to pay if you integrate LLMs such as GPT-3.5 or Claude 2.
Imagine being able to deploy an advanced Small Language Model on a standard work laptop, transforming it into a powerful tool for data analysis or customer service without the need for expensive hardware.
- Easy & Fast Deployment
Deployment is another area where SLMs shine. Their smaller size simplifies packaging and deployment in production environments, a task that often poses significant challenges with LLMs.
This ease of integration allows businesses to implement AI solutions faster and with fewer headaches, enabling them to stay agile and keep pace with technological advancements.
- Customization & Fine-Tuning
Furthermore, SLMs excel in customization. Fine-tuning these models is more efficient, requiring less time and data. This rapid customization is crucial for businesses needing to adapt quickly to market changes.
For example, a marketing firm could quickly fine-tune an SLM for sentiment analysis to gauge public reaction to a new product launch in real-time, providing valuable insights that inform strategy quickly.
- Data Privacy & Security
Data privacy is another critical consideration. SLMs can operate entirely on-device, which enhances user privacy by negating the need to transfer sensitive information to the cloud. This is especially beneficial for sectors like finance and healthcare, where data security is paramount.
- Low Environmental Impact
And let’s not overlook the environmental impact. With reduced computational demands, SLMs consume less energy, resulting in a lower carbon footprint. This not only underscores a company’s commitment to sustainability but also translates to long-term cost savings. What are some known limitations of Small Language Models?
What are some disadvantages of using Small Language Models
Despite their promising advantages, Small Language Models (SLMs) aren't without their limitations. Currently, they can't quite match the output quality of Large Language Models (LLMs).
One specific challenge is that SLMs, trained on smaller datasets, often struggle with producing fact-based content and may lack comprehensive information on many topics. This deficiency can be mitigated by integrating capabilities to search the internet or by using tools and techniques such as RAG to supplement the model's knowledge base.
Another observation I’ve made while using SLMs is that models like Phi-3 are more prone to generating hallucinations compared to their larger counterparts. For instance, in recent tests, Phi-3's content had over 30% more inaccuracies than outputs from GPT-3.5 and Llama-3.
Fortunately, with precise prompting, effective guardrails, and ongoing evaluations, these issues can be minimized, paving the way for more reliable outputs.
As companies continue to innovate, these gaps are likely to close.
How can your organization start leveraging Small Language Models?
Ready to embrace small language models but unsure where to start? Here’s how your company can start leveraging SLMs effectively.
First, identify clear use cases within your organization where SLMs can provide immediate value. These could be single tasks that have a single well-defined goal, like sentiment analysis for youtube comments, classifying product features by priority, or generating short summaries of emails. The lightweight nature of SLMs makes them ideal for creating small, language-dependent tools implementations with limited computing needs.
Next, consider the infrastructure and platforms your company currently uses. Adapting your legacy systems to integrate SLMs may require some adjustments, but the long-term benefits can far outweigh these initial efforts.
Begin by piloting SLMs in specific departments or use cases to demonstrate their potential. This gradual integration can help uncover best practices and fine-tune the models for broader deployment.
Customization is another critical step. SLMs like Phi-3 and Llama-3 8B can be tailored to your company's unique needs. By training these models on your industry-specific data, you can enhance their accuracy and relevance. This customization is less resource-intensive than for larger models, providing a nimble response to evolving market demands.
Don't forget about continuous evaluation.
While SLMs are efficient and cost-effective, they may require regular updates and precise prompting to maintain performance. You can choose to establish a feedback loop where the output is constantly reviewed and improved, ensuring the SLMs remain accurate and useful.
Lastly, devise a strategy for staying agile with technology changes. As SLMs and related platforms evolve, your systems should be flexible enough to incorporate the latest advancements seamlessly. This adaptability will keep your company competitive and ready to leverage new AI capabilities as they emerge.
Conclusion: Future of Small Language Models
The largest tech giants like Meta and Apple are investing heavily in Small Language Models (SLMs).
Mark Zuckerberg recently shared that Meta is fully committed to developing smaller models like Llama-3 8B, alongside their larger counterparts. Microsoft and Google, too, are eyeing small and open models such as Phi and Gemma with the potential to introduce agents running on these models.
Imagine having AI Assistants for various tasks in your organization that are not only aligned with your goals and values but also keeps your data secure and private. The trends show a promising future for SLMs.
SLMs are set to become increasingly powerful while remaining open-source, democratizing access to advanced Generative AI for all businesses. This puts the onus on decision-makers to harness this technology strategically, ensuring long-term benefits and competitive edge in the rapidly evolving landscape. Now is the perfect time for you to consider how SLMs can transform your business processes, driving efficiency, and innovation.
