Evolution of Language Model Size and Applications

Andrej Karpathy has released one of the most comprehensive guides on LLMs In just 3.5 hours, he dives deep into the architecture, training, and applications of LLMs. Here’s what makes this video a must-watch: 1. Evolution of Language Models Karpathy traces the journey from simple statistical methods to advanced neural networks like Transformers. He explains how these models are trained on vast datasets, enabling them to generate human-like text and perform tasks like translation and code generation. 2. Inner Workings Unveiled A significant part of the video breaks down complex concepts such as attention mechanisms, tokenization, and large-scale data in model training. Karpathy also addresses common challenges like model bias and ethical considerations, emphasizing the importance of fine-tuning models for specific applications. 3. Practical Applications Karpathy highlights how LLMs are transforming various industries, including healthcare, finance, and entertainment. He provides examples of how these models improve services, enhance user experiences, and drive innovation. 4. Clear Explanations Karpathy’s ability to simplify complex topics makes this video accessible to both newcomers and seasoned professionals. His thorough analysis offers valuable insights into the future of artificial intelligence. For those looking to deepen their understanding of LLMs, this video is an invaluable resource. Watch the full video to learn from one of the leading experts in the field: https://lnkd.in/dswuqDhm

🤔 Think LLMs need billions of parameters to be useful? Think again! New research shows Small Language Models (100M-5B params) are closing the gap with their larger counterparts - and they can run right on your phone. Key findings from a comprehensive survey of 59 state-of-the-art SLMs: Performance Gap Shrinking: From 2022-2024, SLMs showed remarkable improvement - outpacing even LLaMA's evolution. They achieved gains of: 10.4% in commonsense reasoning 13.5% in problem-solving 13.5% in mathematics Size Isn't Everything: The latest 1.5B parameter models can outperform 3B parameter models in specific tasks. For example, Qwen2's 1.5B variant beats many 3B models while using less compute. Deployment Reality: Running on a smartphone's CPU, these models can process prompts at ~70ms per token. With GPU acceleration on edge devices, that drops to ~30ms - making real-time interactions possible. Most intriguing? SLMs trained on open-source datasets are now approaching the performance of those trained on proprietary data in commonsense tasks. The gap remains mainly in complex reasoning and mathematics. What's your take - will SLMs eventually replace cloud-based LLMs for most day-to-day tasks? Research paper in the comments. #AI #MachineLearning #EdgeComputing #TinyML #LLM

What to expect from Al in 2024? “As large language models (LLMs) become commercial commodities, there is a growing focus on making them smaller and faster. Small models, which can be squeezed onto fewer chips, are much less expensive to train and to run-some can even run on a laptop or smartphone Developers have been training smaller models with more training data. For instance,"Chinchilla", an LLM developed in 2022 by Google DeepMind, outperforms OpenAI's GPT-3, despite being a quarter of the size: it was trained on four times the data. Focus is also shifting from "how much" data to "how good", as stocks of new, high-quality text are expected to gradually dry up. A bigger advance, however, will be in learning how to use them better. To enhance the accuracy of responses, for example, LLMs can be combined with extra software and a specialised database of knowledge. Showing what is possible has turned into figuring out what is practical.” -2024 in preview: Smaller, better, faster, stronger Al - The Economist 29th Dec.

Language Models are all the buzz today, with LLMs like GPT-3 and its successors dominating headlines. But did you know that these incredible models aren't a recent fad? They've been shaping the world of AI and language understanding for over half a century. Let's embark on a fascinating journey through the key events in the evolution of LLMs. 💡 1960s: The Dawn of LLMs It all began in the 1960s when early language models were conceived. However, these models were rudimentary and lacked the sophistication we witness today. 💡 1970s: The Age of Early Experiments In the 1970s, researchers started experimenting with the "unigram" language models, which considered words in isolation. These models paved the way for more complex structures. 💡 1980s: The Arrival of N-grams The 1980s saw the introduction of N-grams, a model that considered the probability of words based on their context within a fixed window. This approach enhanced language understanding significantly. 💡 1990s: The Emergence of Statistical Models Statistical models, such as Hidden Markov Models, gained popularity in the 1990s. They allowed for more accurate predictions by considering the underlying structure of language. 💡 Early 2000s: Machine Learning Integration The early 2000s brought the integration of machine learning techniques into language models, enhancing their predictive power and context awareness. 💡 2010s: The Deep Learning Revolution The 2010s marked a significant shift with the rise of deep learning. Models like Word2Vec and Seq2Seq demonstrated the potential of neural networks in language understanding. 💡 2020s: The LLM Explosion We have now entered an era where LLMs like GPT-3, BERT, and their successors are making headlines. These models leverage the power of deep learning, massive data sets, and transfer learning to deliver remarkable language understanding capabilities. As we reflect on the journey of LLMs, it's clear that they've come a long way from their humble beginnings. Today, they power chatbots, translators, content generation, and so much more. The evolution of LLMs is a testament to the relentless pursuit of excellence in the world of AI and natural language understanding. The future holds even more exciting possibilities as LLMs continue to evolve. Stay tuned as we witness the next chapter in the remarkable story of Language Models. #LanguageModels #AI #NLP #EvolutionOfTech #AIHistory 🌐📈🤖