Use your few questions to have it generate high level system prompts for other models. Specifically say ‘summarize in 2000 characters everything you learned and how you think in a way that could help your lesser models like gpt-4o mini. Be detailed, focus on higher level concepts, and work your way up. Teach the models to be interdisciplinary and collaborative and real time responsive’. Fun fact. It appears they actually used my meta prompts to build the thing, its output it nearly identical to what I get using high level meta prompts on other models. You can get its power without needing to pay for it, and you can still have tool use and web search. They added more parameters so it won’t be quite as powerful, but you should be able to get close

Welcome to AI 102, where we’ll explore the concepts of Transfer Learning and Fine-Tuning. These techniques are critical when you want to teach a pre-trained model, like LLaMA or GPT, to handle specific tasks like financial analysis. In this post, we’ll dive deeper into these topics, discuss practical applications, and explore why VRAM efficiency is essential. What is Transfer Learning? Imagine you’ve been learning about general knowledge for years. You’ve studied a little bit of everything: history, math, and science. Now, let’s say you want to specialize in finance. You wouldn’t start by relearning basic math—you already know that! Instead, you would build on top of what you already know and apply it to finance. This is what transfer learning does for AI models. Large models like GPT or LLaMA are pre-trained on vast amounts of general data. When you want them to perform a specialized task—like analyzing financial data—you don’t start training them from scratch. Instead, you transfer their existing knowledge and teach them the nuances of finance. Analogy: Think of transfer learning as taking a generally knowledgeable student and refining their expertise in a specific field, like finance, without starting from square one. What is Fine-Tuning? Fine-tuning takes this process a step further. Imagine that student has now started specializing in finance. But, to master specific tasks—like understanding market trends—you give them more focused material. You provide datasets that are directly relevant to what they need to learn, polishing their skills. In AI, fine-tuning means taking a pre-trained model and continuing its training on a smaller, specialized dataset to make it excel in a particular domain. This is especially useful in fields like finance, where models need to understand specific terms, behaviors, and trends. Fine-tuning allows the model to adapt to your specific dataset without having to learn everything from scratch. Example: Let’s say you want a model to predict stock prices based on historical data. First, you use transfer learning to teach the model general financial concepts, like how stock markets work. Then, you fine-tune the model with historical price data, adjusting it to specialize in predicting price movements.

Let’s dive into how transformers work in AI and explore the powerful "self-attention" mechanism that drives them. At their core, transformers take in a large amount of data and break it down across multiple layers. Each layer processes the data in a different way, learning from the layers before it to refine its understanding. This process is like organizing information into manageable pieces at every step, starting with raw data and ending with a more focused understanding. In the self-attention mechanism, each part of the data learns to focus on other parts that are most relevant to the task. It's like a system of filters, where each layer pays attention to different pieces of the input data, emphasizing what matters most. The beauty of this process is that the original data never changes—it’s simply reorganized and refined layer by layer, allowing the AI to make sense of vast and complex information. Now, here’s where the parallel to finance comes in. Think of each AI layer as a statistical technique applied to financial data. For instance, the first layer could act like k-means clustering, grouping similar data points together. In finance, this might be clustering stocks by their price movements. The second layer could be like running regressions on those clusters, finding relationships between them. Each subsequent layer refines the groupings further, like applying different statistical models one after another to gain a clearer, more nuanced understanding of the data. In AI, each hidden layer reorganizes the data based on what it has learned from the previous layer—much like how financial models build upon each other to reveal new insights.