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… →