The AI arms race is prompting big tech companies to deploy massive resources to keep large, power-hungry language models running, potentially reducing response times. . Microsoft, for example, is reportedly planning to invest his $100 billion in a supercomputer scheduled to go live in 2028 to support its AI ambitions.
But on the other hand, smaller language models are also gaining traction. And it's quickly proving that prompts don't always need gigawatts of processing power. Some of these models can run on personal devices such as phones and perform just as well for certain tasks. Here's everything you need to know about SLM.
What is a small language model?
Small-scale language models aim to challenge the notion that bigger is always better in natural language processing. Unlike the hundreds of billions of parameters (variables that the model learns during training) that models like GPT-4 and Gemini Advanced boast, SLM's parameters range from “only” millions to billions.
Still, it has proven to be very effective for specialized tasks and resource-constrained environments. Advances in training techniques, architectures, and optimization strategies are closing the performance gap with his LLM, making it an increasingly attractive option for a wide range of applications.
What is a small language model used for?
SLM's versatility is one of its most appealing features. These models are applied in a variety of fields, from sentiment analysis and text summarization to question answering and code generation. Its compact size and efficient computation make it suitable for deployment in edge devices, mobile applications, and resource-constrained environments.
For example, Google's Gemini Nano is a compact, high-performance device found in the latest Google Pixel smartphones that helps you reply to text messages and summarize recordings, all on the device itself without an internet connection. . Microsoft's Orca-2–7b and Orca-2–13b are also examples of his SLM.
Of course, small-scale language models are relatively new and are still being researched, so there may not be many real-world applications yet. But the promise still remains. Organizations can particularly benefit from these. By enabling on-premises deployment, these smaller models can keep sensitive information securely within an organization's infrastructure, reducing the risk of data breaches and addressing compliance concerns.
How is SLM different from LLM?
LLMs are trained on vast amounts of general data, whereas SLMs excel at specialization. Through a process called fine-tuning, these models can be tailored to specific domains or tasks to achieve high accuracy and performance in narrow contexts. This targeted training approach makes SLM highly efficient, requiring significantly less computational power and energy consumption compared to its larger counterparts.
Another difference lies in the inference speed and latency of SLM. Its compact size reduces processing time and improves responsiveness, making it suitable for real-time applications such as virtual assistants and chatbots.
Additionally, developing and deploying SLMs is often more cost-effective than LLMs, which require significant computational resources and financial investments. This accessibility factor makes SLM an attractive option for small organizations and research groups with limited budgets.
What are some of the most popular SLMs?
The landscape of small language models is rapidly evolving, with numerous research groups and companies contributing to their development. Some of the most notable examples are listed below.
1. Llama 2: Developed by Meta AI, Llama 2 is a collection of pre-trained, fine-tuned generative text models ranging in size from 7 billion to 70 billion parameters with superior performance across a range of natural language understanding. Because of this, it has received a lot of attention in the open source community. task.
2. Mistral and Mistral: Mistral AI's products, such as Mistral-7B and the expert mixed model Mixtral 8x7B, have demonstrated competitive performance compared to larger models such as GPT-3.5.
3. Microsoft's Phi and Orca: Microsoft's Phi-2 and Orca-2 models are known for their powerful inference capabilities and fine-tuning adaptability to domain-specific tasks.
4. Alpaca 7B: Developed by researchers at Stanford University, Alpaca 7B is a fine-tuned version of the LLaMA 7B model with a demonstration of following 52K instructions. In preliminary evaluation, it showed qualitatively similar behavior to OpenAI's text-davinci-003, which is based on GPT-3.
5.StableLM: Stability AI's StableLM series includes small models with as many as 3 billion parameters.
Looking to the future
As research and development in this area progresses, the future of small-scale language models looks promising. Advanced techniques such as distillation, transfer learning, and innovative training strategies can further enhance the capabilities of these models and potentially narrow the performance gap with LLMs on a variety of tasks.
