Tiny But Mighty: Unleashing the Power of Small Language Models đ
While Large Language Models (LLMs) like GPT-4 dominate headlines with their extensive capabilities, they often come at the cost of high computational requirements and complexity. For developers and organizations looking to implement AI solutions on edge devices or with limited resources, Small Language Models (SLMs) are emerging as a practical alternative. SLMs are not just "smaller" versions of their larger counterpartsâthey're designed to be faster, more efficient, and adaptable for specific tasks. With fewer parameters and lower computational needs, SLMs open the door to deploying AI on mobile devices, IoT systems, and edge environments without compromising performance. What You Stand to Learn đ§ Introduction to Microsoft's AI Ecosystem Discover Microsoft's end-to-end AI development tools, from Azure AI Services to ONNX Runtime, enabling efficient and secure deployment of AI models across cloud and edge environments. The Advantages of SLMs over LLMs SLMs are game-changers for edge AI applications, providing faster training and inference times, reduced energy costs, and scalability across diverse devices. Hands-On with Phi-3 and ONNX Runtime Experience live demonstrations of SLMs in action with tools like Phi-3 and ONNX Runtime, showcasing how to fine-tune and deploy models on mobile devices, IoT, and hybrid cloud environments. Responsible AI Practices Understand how to safeguard your AI applications with Microsoft's Responsible AI toolkit, ensuring ethical and trustworthy deployments. Watch the Full Session đ¨âđť đ Date: December 12, 2024 â° Time: 4 PM GMT | 5 PM CEST | 8 AM PT | 11 AM ET | 7 PM EAT A session packed with live demos, practical examples, and Q&A opportunities. Register NOW | Events | Microsoft Reactor Agenda đ Introduction (5 min) A brief overview of the session and its focus on SLMs and LLMs. Microsoft AI Tooling (5 min) Explore the latest tools like Azure AI Services, Azure Machine Learning, and Responsible AI Tooling. How to Choose the Right Model (10 min) Key considerations such as performance, customizability, and ethical implications. Comparing SLMs vs LLMs (10 min) The strengths, weaknesses, and best use cases for both Small and Large Language Models. Deploying Models at the Edge (10 min) Insights into optimizing AI for mobile, IoT, and edge devices. Q&A Addressing participant questions about AI development and deployment.How to build a social media assistant with Prompty and PromptFlow
Large Language Models were trained by data from all over the internet with billions of parameters. To generate an output from LLMs, you need a prompt. For day-to-day questions for example generating a LinkedIn Post from a blog post, you may need additional instructions for your LLM, for example: the word count, tone of the message, the format and the call to action at the end. Using prompty, you can easily standardize your prompt and execute it into a single assetWhy Should Business Adopt RAG and migrate from LLMs?
In this blog we are going to discuss the importance of migrating your product or startup project from LLMS to RAG. Adopting RAG empowers businesses to leverage external knowledge, enhance accuracy, and create more robust AI applications. Itâs a strategic move toward building intelligent systems that bridge the gap between generative capabilities and authoritative information. Below are topics in this blog. Brief History of AI What are Large Language Models (LLMS). Limitation of LLMS. How can we incorporate domain knowledge. What is Retrieval Augmented Generation (RAG). What is Robust retrieval for RAG Apps. Once we are done with these concepts, I hope to convince you to adopt RAG in your project.
ChatGPT- What? Why? And How?
This blog discusses ChatGPT, a pre-trained language model that has garnered significant attention in the AI community due to its innovative capabilities. It explores the technology behind ChatGPT, its purpose, function, and utilization, as well as its potential applications and impact on the field of artificial intelligence. This blog also covers the limitations of ChatGPT and its architecture and working, including the use of advanced machine learning techniques such as Transformer and fine-tuning.Unlock the Potential of AI in Your Apps with Semantic Kernel: A Lightweight SDK for LLMs
Semantic Kernel (SK) is a lightweight SDK enabling integration of AI Large Language Models (LLMs) with conventional programming languages. The SK extensible programming model combines natural language semantic functions, traditional code native functions, and embeddings-based memory unlocking new potential and adding value to applications with AI. SK supports prompt templating, function chaining, vectorized memory, and intelligent planning capabilities out of the box
