Tanat Tonguthaisri, CISSP®’s Post

Arcee AI Releases SuperNova-Medius: A 14B Small Language Model Built on the Qwen2.5-14B-Instruct Architecture In the ever-evolving world of artificial intelligence

The current wave of generative #AI models is built on the Transformer architecture, which has been popularized by the emergence of large language models (LLMs). Despite their prominence, LLMs have inherent drawbacks and constraints. To address these issues, researchers are now focusing on developing smaller language models that could potentially revolutionize the field of generative AI. Read more. #GenAI #GenerativeAI #LanguageModels

Love seeing NRI leading the way in Market Research! Great read and it got me thinking! Here's my take: AI is booming, and language models are leading the charge. But with Small Language Models (SLMs) and Large Language Models (LLMs) on the scene, choosing the right one can be tricky. LLMs are trained on massive amounts of data, allowing them to handle a wider range of tasks and generate creative text formats. LLMs excel at complex tasks requiring deep understanding of context, like summarizing research papers or writing different creative content styles. SLMs offer Efficiency & Scalability. Their smaller size makes them faster to train, run, and deploy, especially for businesses with limited resources. Since they're trained on specific data sets, they offer greater accuracy and precision in their domain. This is ideal for specialized tasks like legal document analysis or medical report interpretation. The key? Matching the model to your needs

Just a moment...: The initial prototype was successful, but subsequently faced a decline in performance. For further details, please refer to Towards AI.

🚀 Enhancing AI Responses with Advanced RAG Techniques! 🚀 Are you exploring ways to level up your AI-driven applications? Retrieval-Augmented Generation (RAG) is transforming how we approach context, accuracy, and relevance in AI responses. From smart metadata tagging to reranking and multiple data sources, there’s so much more to RAG than meets the eye. 🧠💡 In my latest Medium blog, I break down practical ways to boost your RAG systems for better outcomes and smarter insights. Whether you’re dealing with complex data or want a deeper level of response accuracy, these techniques are for you. 👉 Ready to dive in? Check it out and see how you can supercharge your LLM’s responses! #AI #MachineLearning #DataScience #LLM #RAG #TechInnovation #AIInsights #Medium

Generative AI models like GPT-4o process text using tokenization due to transformer architecture limitations, often resulting in confusing outputs. This introduces challenges across different languages and numerical data, highlighting inefficiencies. Alternative models like MambaByte could be a solution but are still in early research stages. #Tokenization #GenerativeAi #TransformerModels https://lnkd.in/gEgkDuhM

🌟 Exciting Announcement: Check out our latest blog post providing a comprehensive review of Multi-Modal Large Language and Vision Models. The post explores the evolution of Large Language Models (LLMs) and the emergence of multi-modal large language models (MM-LLMs), extending capabilities to process image, video, audio, and text data. Learn about the historical development of LLMs, major advancements enabled by transformer-based architectures, and ethical considerations in AI development. Dive into the transformative potential of MM-LLMs in various applications! Read the full post here: https://bit.ly/4cANmRH #AI #MachineLearning #MM-LLMs #LanguageModels

Where using #artificialintelligence / #machinelearning / #largelanguagemodels in applications, what #threatmodeling has your team considered? Open to setting up a further chat on the topic. Here are some approaches our research team took when building appropriate threat models for these scenarios, with a focus on models-as-threat-actors - https://bit.ly/3Oyuzf9 #secdevops #devopssecurity #ai #ml #llm

Just finished the course “Generative AI: Introduction to Large Language Models” by Frederick Nwanganga! Covered fundamentals and topics such as: Self-Attention Transformer Architecture CNN RNN Check it out: https://lnkd.in/de4Kim4P #generativeai #largelanguagemodels.#genai #llmops #langchain

Optimization techniques for LLM inference are gaining more attention in organizations as the benefits become more well-known. With inference becoming increasingly expensive, it's clear that more optimization techniques are needed. Fortunately, attention operators can be performed on cheaper, memory-optimized devices, making compute-intensive and expensive chips unnecessary for every stage of inference. According to the authors, using these optimization techniques can result in estimated throughputs per dollar that are 1.48x-12.1x higher than homogeneous GPU solutions. Learn more about these techniques here: https://lnkd.in/dvtZBRkz. #optimization #inference #LLM #AI #machinelearning