This article explains BERT, a language model designed to understand text rather than generate it. It discusses the transformer architecture BERT is based on and provides a step-by-step guide to building and training a BERT model for sentiment analysis.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
This article provides a comparative analysis of popular embedding libraries for generative AI, evaluating their strengths, limitations, and suitability for different use cases.
This paper surveys different prompt engineering techniques used to improve the performance of large language models on various Natural Language Processing (NLP) tasks. It categorizes these techniques by NLP task, highlights their performance on different datasets, and discusses state-of-the-art methods for specific datasets. The survey covers 44 research papers exploring 39 prompting methods across 29 NLP tasks.
A Github Gist containing a Python script for text classification using the TxTail API
Exploratory data analysis (EDA) is a powerful technique to understand the structure of word embeddings, the basis of large language models. In this article, we'll apply EDA to GloVe word embeddings and find some interesting insights.
The article discusses the integration of Large Language Models (LLMs) and search engines, exploring two themes: Search4LLM, which focuses on enhancing LLMs using search engines, and LLM4Search, which looks at improving search engines with LLMs.
This tutorial covers fine-tuning BERT for sentiment analysis using Hugging Face Transformers. Learn to prepare data, set up environment, train and evaluate the model, and make predictions.
A research team introduces Super Tiny Language Models (STLMs) to address the resource-intensive nature of large language models, providing high performance with significantly reduced parameter counts.
