In this tutorial, we’ll walk through how to set up and perform fine-tuning on the Llama 3.2 3B Instruct model using a specially curated Python code dataset. By the end of this guide, you’ll have a better understanding of how to customize large language models for code-related tasks and practical insight into the tools and…
Vision-language models (VLMs) face a critical challenge in achieving robust generalization beyond their training data while maintaining computational resources and cost efficiency. Approaches, such as chain-of-thought supervised fine-tuning (CoT-SFT), often lead to overfitting, where models perform well on seen data but struggle with new, unseen scenarios. This limitation reduces their effectiveness in applications that demand…
Large language model (LLM) post-training focuses on refining model behavior and enhancing capabilities beyond their initial training phase. It includes supervised fine-tuning (SFT) and reinforcement learning to align models with human preferences and specific task requirements. Synthetic data is crucial, allowing researchers to evaluate and optimize post-training techniques. However, open research in this domain is…
The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, particularly conversational agents. However, these models face inherent limitations due to their fixed context windows, which can lead to loss of relevant information over time. While Retrieval-Augmented Generation (RAG) methods provide external knowledge to supplement LLMs, they often rely on static document…
Regression tasks, which involve predicting continuous numeric values, have traditionally relied on numeric heads such as Gaussian parameterizations or pointwise tensor projections. These traditional approaches have strong distributional assumption requirements, require a lot of labeled data, and tend to break down when modeling advanced numerical distributions. New research on large language models introduces a different…
Transformer-based language models process text by analyzing word relationships rather than reading in order. They use attention mechanisms to focus on keywords, but handling longer text is challenging. The Softmax function, which distributes attention, weakens as the input size grows, causing attention fading. This reduces the model’s focus on important words, making it harder to…
Neural Ordinary Differential Equations are significant in scientific modeling and time-series analysis where data changes every other moment. This neural network-inspired framework models continuous-time dynamics with a continuous transformation layer governed by differential equations, which sets them apart from vanilla neural nets. While Neural ODEs have cracked down on handling dynamic series efficiently, cost-effective gradient…
Directed graphs are crucial in modeling complex real-world systems, from gene regulatory networks and flow networks to stochastic processes and graph metanetworks. Representing these directed graphs presents significant challenges, particularly in causal reasoning applications where understanding cause-and-effect relationships is paramount. Current methodologies face a fundamental limitation in balancing directional and distance information within the representation…