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Eclectic-AI offers consulting services, on-site customized training and seminars, and innovative solutions using machine learning including deep learning and graph-based learning and computer vision technologies for applications in automotive, business and commerce, defense and homeland security, healthcare and medicine, and social media analysis and mining. 

Our researchers have over 50 years of combined experience in applying artificial intelligence, neural networks including deep learning, machine learning, pattern recognition, and video analytics to a broad range of projects and have successfully completed projects for US Air Force, Navy, local hospitals and industries. Integrated Knowledge Solutions has also successfully conducted seminars and webinars within Europe, USA and India.

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LLaMA 2 and its Symbolic Regression Explanation

On July 17, a new family of AI models, LLaMA 2 was announced by Meta. LLaMA 2 is trained on a mix of publicly available data. According to Meta LLaMA 2 performs significantly better than the previous generation of LLaMA models. Two flavors of the model: LLaMA 2 and LLaMA 2-Chat, a model fine tuned for two-way conversations, were released. Each flavor further has three versions with the parameters ranging from 7 billions to 70 billions. Meta is also freely releasing the code and data behind the model for  researchers to build upon and improve the technology. There are several ways to access LLaMA 2 for development work; you can download it from HuggingFace or access it via Microsoft Azure or Amazon SageMaker . For those interested in interacting with the LLaMA 2-Chat version, you can do so by visiting , a chatbot model demo hosted by the venture capitalist Andreessen Horowitz. This is the route I took to interact with LLaMA 2-Chat. Since I was reading an excellent paper on

Reinforcement Learning with Human Feedback: A Powerful Approach to AI Training

The unprecedented capabilities exhibited by the large language models (LLMs) such as ChatGPT and GPT-4 have created enormous excitement as well as concerns about the impact of AI on the society in near and far future. Behind the success of LLMs and AI in general lies among other techniques a learning approach called Reinforcement Learning with Human Feedback (RLHF). In this blog post, we will try to understand what RLHF is and why it offers a powerful approach to training AI models. However, before we do that, let's try to understand the concept of reinforcement learning (RL). What is Reinforcement Learning (RL)? RL, inspired by the principles of behavioral psychology, is a machine learning technique wherein the learner, called an agent , learns decision making by exploring an environment through a trial-and-error process to achieve its goal. Each action by the agent results in feedback in the form of a reward or punishment . While performing actions and receiving feedback, the a

Low Rank Adaptation (LoRA): Enhancing Fine-Tuning of LLMs

Pre-trained large language models (LLMs) are being used for numerous natural language processing applications. These models perform well out of the box and are fine-tuned for any desired down-stream application. However, fine-tuning these models to adapt to specific tasks often poses challenges due to their large parameter sizes. To address this, a technique called Low Rank Adaptation (LoRA) has emerged, enabling efficient fine-tuning of LLMs. In this post, we will try to understand LoRA, and delve into its importance and application in fine-tuning LLMs. We will begin our journey by first looking at the concept of rank of a matrix, followed by a look at matrix factorization, and then to LoRA. Rank of a Matrix The rank of a matrix indicates the number of independent rows or column in the matrix. As an example, consider the following 4x4 matrix A: A = [[2, 4, 6, 8], [1, 3, 5, 7], [4, 8, 12, 16], [3, 9, 15, 21]] Looking at the first and third row of this matrix, we see that the third row