Guide

Superb-tuning large language models (LLMs) like Llama 3 involves adapting a pre-trained model to specific tasks using a domain-specific dataset. This process leverages the model's pre-existing knowledge, making it efficient and cost-effective in comparison...

Now it’s time for some contrastive learning. To mitigate the difficulty of insufficient annotation labels and fully utilize the massive quantity of unlabelled data, contrastive learning may very well be used to effectively help...

Moving data around could be slow. Here’s how you'll be able to squeeze every little bit of performance optimization out of Python.Python is commonly criticized for being among the many slowest programming languages. While...

In 2023, I’d been coding for data projects for two years and was trying to create my first portfolio to present my data science projects. I discovered the Matt Chapman’s TDS article and the...

Large Language Models (LLMs) are powerful tools not only for generating human-like text, but in addition for creating high-quality synthetic data. This capability is changing how we approach AI development, particularly in scenarios where...

Gemma 2 builds upon its predecessor, offering enhanced performance and efficiency, together with a collection of modern features that make it particularly appealing for each research and practical applications. What sets Gemma 2 apart...

Code embeddings are a transformative solution to represent code snippets as dense vectors in a continuous space. These embeddings capture the semantic and functional relationships between code snippets, enabling powerful applications in AI-assisted programming....

LLMs like GPT-3, GPT-4, and their open-source counterpart often struggle with up-to-date information retrieval and might sometimes generate hallucinations or misinformation.Retrieval-Augmented Generation (RAG) is a way that mixes the ability of LLMs with external...