Algorithm – Up As Pro

In this post, we’ll explore three groundbreaking advancements that are pushing the boundaries of AI and machine learning. First, dive into the intricacies of LLaMa 3, implemented from scratch in Python, where every aspect, from attention mechanisms to tokenization, is meticulously explained, making it a must-see for anyone interested in model architecture. Next, discover how LinearBoost, a new linear classifier-based algorithm, outperforms traditional GBDTs like CatBoost and XGBoost, showcasing superior accuracy and response time across five benchmark datasets. Lastly, we’ll delve into the debate on Low-Rank Adaptation (LoRA) in fine-tuning large language models, revealing why LoRA might not match full fine-tuning in specialized domains but offers remarkable regularization benefits. These insights are not only educational but also essential for staying at the forefront of AI research and application.

Academic Algorithm Code Concept paper Series

August 16, 2024 admin

Deep dive: Transformers by Gemma, Iterative Reasoning PO, inner work of Transformers

Demystifying Transformers with Google’s Gemma, boosting reasoning tasks with Meta’s Iterative Reasoning Preference Optimization, and enhancing understanding of Transformer models with a unified interpretability framework. These are the latest strides in AI, making complex concepts accessible and improving model performance. Stay tuned for more! 🚀🧠🤖

UpasPro, Pedram Agand personal blog. AI and financial advice

Academic Algorithm Code Concept Machine Learning

May 7, 2024 admin

Better Images, Less Training

The longer text-to-image models train, the better their output — but the training is costly. Researchers built a system that

Algorithm Code Concept

April 28, 2024 admin

Deep dive: Knowledge distillation

In this deep dive seri, we are going over Knowledge distillation (KD), Partial Function Application, Learning rate Scheduler for LLM

Academic Algorithm Code Concept FEATURED Machine Learning

April 26, 2024 admin

QLoRA: efficiently LLM Fine-Tuning

Parameter-efficient training (PEFT) techniques offer a way to fine-tune large language models (LLMs) on custom datasets with minimal computational resources.