In a pioneering move in the world of AI and LLMs (Large Language Models), Microsoft introduced Phi-2, a compact or small language model (SLM). Phi-2 is positioned as an upgraded version of Phi-1.5, and is currently accessible through the Azure AI Studio model catalog.
Microsoft confirms that this new model can outperform larger counterparts such as Llama-2, Mistral, and Gemini-2 in several generative AI benchmark tests.
Phi-2, which was introduced earlier this week following Satya Nadella’s announcement at Ignite 2023, is the result of the efforts of Microsoft’s research team.
The generative AI model is described as having attributes such as “common sense,” “language understanding,” and “logical reasoning.” Microsoft claims the Phi-2 can outperform models 25 times its size on specific tasks.
Trained using “textbook quality” data, including synthetic datasets, general knowledge, theory of mind, daily activities, and more, Phi-2 is a transformer-based model that features capabilities such as next word prediction target.
Microsoft points out that training Phi-2 is more straightforward and cost-effective than larger models like GPT-4, which reportedly take about 90-100 days to train using tens of thousands of A100 Tensor Core GPUs.
Phi-2’s capabilities extend beyond language processing, as it can solve complex mathematical equations and physics problems, as well as identify errors in students’ calculations. On standardized tests covering logical reasoning, language comprehension, mathematics and coding, the Phi-2 outperformed models such as the 13B Llama-2 and 7B Mistral.
Notably, it also beats the 70B Llama-2 LLM by a large margin, and even beats the GoogleGemini Nano 2, a 3.25B model designed to work natively on the Google Pixel 8 Pro.
In the rapidly evolving field of natural language processing, small language models are emerging as strong contenders, offering a range of advantages that meet specific use cases and contextual needs, over the more common MA or large language models. These advantages are reshaping the landscape of language processing technologies. Here are some of the major advantages of embedded language models:
Computational efficiency: Small language models require less computational power for both training and inference, making them a more feasible option for users with limited resources or on devices with low computational capabilities.
Quick reasoning: Smaller models have faster inference times, making them well-suited for real-time applications where low latency is critical to success.
Resource Friendly: Embedded language models, by design, use less memory, making them ideal for deployment on devices with limited resources, such as smartphones or terminals.
Energy efficiency: Due to their small size and complexity, small models consume less power during both training and inference, catering to applications where energy efficiency is a critical concern.
Reduce training time: Training smaller models is a time-efficient process compared to their larger counterparts, providing a significant advantage in scenarios where rapid model iteration and deployment is essential.
Enhancing the ability to interpret: Smaller models are often clearer to interpret and understand. This is especially critical in applications where model interpretability and transparency are of paramount importance, as seen in medical or legal contexts.
Cost effective solutions: Training and deploying small models is less expensive in terms of computational resources and time. This accessibility makes it a viable option for individuals or organizations with budget constraints.
Designed for specific fields: In some specialized or domain-specific applications, a smaller model may be sufficient and more appropriate than a large general-purpose language model.
It is important to emphasize that the decision between small and large language models depends on the specific requirements of each task. While large models excel at capturing complex patterns in diverse data, small models prove invaluable in scenarios where efficiency, speed, and resource constraints take precedence.
(With inputs from agencies)
Sequential Labeling Models (SLMs) and Language Model Models (LLMs) like ChatGPT are both natural language processing (NLP) models, but they differ in their approach and function. SLMs, such as Conditional Random Fields and Hidden Markov Models, are designed to perform sequence labeling tasks, such as named entity recognition and part-of-speech tagging. On the other hand, LLMs like ChatGPT are designed to generate human-like text based on input prompts or conversations. While both types of models operate within the realm of NLP, their specific functionalities and applications set them apart in the field of artificial intelligence.