Dimitar Iliev ☁️’s Post

✨ ✨ Come join us for a guest lecture by Hyung Won Chung from OpenAI on "Shaping the Future of AI from the History of Transformer". The talk will be on Monday October 14 from 1:45 PM to 3:15 PM ET, as part of my course on Large Language Models at the University of Pennsylvania. Hyung Won is a research scientist at OpenAI who worked most recently on their 🍓 o1 model. His other notable works include contributions to Flan-T5, Flan-PaLM, T5X, and the PaLM language model at Google. ➡️ Registration link: https://lu.ma/q0gghdtc Title: Shaping the Future of AI from the History of Transformer Abstract: AI is developing at such an overwhelming pace that it is hard to keep up. Instead of spending all our energy catching up with the latest development, I argue that we should study the change itself. First step is to identify and understand the driving force behind the change. For AI, it is the exponentially cheaper compute and associated scaling. I will provide a highly-opinionated view on the early history of Transformer architectures, focusing on what motivated each development and how each became less relevant with more compute. This analysis will help us connect the past and present in a unified perspective, which in turn makes it more manageable to project where the field is heading. Bio: Hyung Won Chung is a research scientist at OpenAI. His recent work focuses on o1. He has worked on various aspects of Large Language Models: pre-training, instruction fine-tuning, reinforcement learning with human feedback, reasoning, multilinguality, parallelism strategies, etc. Some of the notable work includes scaling Flan paper (Flan-T5, Flan-PaLM) and T5X, the training framework used to train the PaLM language model. Before OpenAI, he was at Google Brain and before that he received a PhD from MIT. Supplementary Readings: ➡️ Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. (https://lnkd.in/eEA6MSJx). ➡️ Fast Transformer Decoding: One Write-Head is All You Need. (https://lnkd.in/eusN_AHm). #ai #machinelearning #largelanguagemodels #llm #upenn #turing

🔍 Will We Run Out of Data? 📉 Check out this paper on the Limits of LLM Scaling Based on Human-Generated Data 📚🤖 Researchers investigate the future of Large Language Models (#LLMs) and the potential constraints posed by the availability of public human-generated text data. Their study forecasts that by 2026-2032, the demand for training data could match the total stock of available public text data. What’s next? They explore solutions like synthetic data generation, transfer learning, and data efficiency improvements to ensure LLMs continue evolving. Dive into their findings for more insights! 🌐📈 Check out the paper on arXiv: https://lnkd.in/difB_ee3 #AI #ArtificialIntelligence #DataScience #ResearchInnovation #AIResearch #LLMResearch Researchers represent Epoch AI University of Aberdeen MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) Centre for the Governance of AI (GovAI) and University of Tuebingen

BurstAttention: A Groundbreaking Machine Learning Framework that Transforms Efficiency in Large Language Models with Advanced Distributed Attention Mechanism for Extremely Long Sequences https://lnkd.in/g9UmYAjD Large language models (LLMs) have revolutionized how computers understand and generate human language in machine learning and natural language processing. Central to this revolution is the Transformer architecture, known for its remarkable ability to handle complex textual data. We must overcome significant challenges as we explore the full potential of these models, particularly in processing exceptionally lengthy sequences. Despite their effectiveness, traditional attention mechanisms suffer from a quadratic increase in computational and memory costs concerning sequence length, making the processing of long sequences inefficient and...

In the evolving #AI and #ML landscape, choosing between LLMOps and MLOps is crucial for maximizing the benefits of AI technology. 🔷 LLMOps focuses on automating AI development with large language models like #gpt3, emphasizing seamless integration and management for creative applications such as text generation. 🔶 MLOps offers a broader approach, enhancing AI operations across machine learning models by building robust infrastructures and automating workflows for reliable process management. Key Difference: LLMOps is specialized for large language models, requiring significant computational resources, while MLOps caters to a wider range of ML applications, emphasizing scalability and reliability. ⚖ Which to choose? The choice between LLMOps and MLOps hinges on your goals, the nature of your projects, and available resources. LLMOps is ideal for natural language processing projects, whereas MLOps suits broader ML applications. Consider a hybrid approach for the best of both worlds.

🚀 Excited to share our latest blog post on "KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation". Large Language Model (LLM) inference is a critical component of modern natural language processing. Our work introduces KV-Runahead, an innovative parallelization scheme to accelerate the prompt phase, minimizing the time-to-first-token (TTFT) and offering significant speedups. Check out the full details and experimental results at https://bit.ly/3WEgotZ. #NaturalLanguageProcessing #LLMInference #Parallelization #Innovation

Excited to share that I've completed the Microsoft Azure Al Fundamentals course! During this course, I delved into the world of Artificial Intelligence and learned how Azure Al services can be leveraged to build Al solutions. From understanding the fundamentals of Al to exploring computer vision, natural language processing, and conversational Al, this journey has been incredibly insightful. I'm grateful for the opportunity to deepen my knowledge in Al and look forward to applying these learnings in my future projects. #MicrosoftAzure #AlFundamentals #ArtificialIntelligence #Al #AzureAI

Large Language Models (LLMs) are a type of deep learning model designed to understand and generate human language. Foundation Models: LLMs are trained on vast amounts of data, enabling them to perform a wide range of tasks such as text generation, translation, summarization, and more. Transformer Architecture: Most LLMs, like GPT-4, are based on transformer architecture, which allows them to handle long-range dependencies in text and generate coherent and contextually relevant responses. Applications: They are used in various applications, including chatbots, virtual assistants, content creation, and even in scientific research to analyze large datasets. Challenges: Despite their capabilities, LLMs require significant computational resources and can sometimes produce biased or incorrect outputs due to the data they are trained on. LLMs are revolutionizing how we interact with technology and process information. #llms #largelanguagemodels

"#LLM Inference on #RISCV #Embedded #CPU" By Yueh Feng Lee, Andes Technology Corporation https://lnkd.in/eajbhFr7 The advancement of large language models (LLMs) has significantly enhanced natural language processing capabilities, enabling complex text understanding and generation tasks. This presentation focuses on optimizing the open-source LLaMA CPP project for the RISC-V P extension. By running the TinyLLaMA 1.1B model on the Andes Voyager development board using a quad-core #CPU supporting the RISC-V P extension, performance results show that the model can achieve near real-time response. This work highlights the potential of RISC-V as an efficient platform for deploying advanced AI models in resource-constrained environments, contributing to the growing field of edge computing and embedded #AI applications.

This Machine Learning Paper from Microsoft Proposes ChunkAttention: A Novel Self-Attention Module to Efficiently Manage KV Cache and Accelerate the Self-Attention Kernel for LLMs Inference https://lnkd.in/g5QksKSH Developing large language models (LLMs) in artificial intelligence represents a significant leap forward. These models underpin many of today’s advanced natural language processing tasks and have become indispensable tools for understanding and generating human language. However, these models’ computational and memory demands, especially during inference with long sequences, pose substantial challenges. The core challenge in deploying LLMs efficiently lies in the self-attention mechanism, which significantly impacts performance due to its memory-intensive operations. The mechanism’s memory complexity grows with the context length, leading to increased...

Discover how Post-Training Quantization (#PTQ) and Quantization-Aware Training (#QAT) help balance performance and efficiency in Large Language Models. Perfect for resource-constrained environments! #AI #MachineLearning #LLM #Quantization #DeepLearning #TechInsights