AI Insights Blog

Why and where to Use LoRA / QLoRA and RAG?

Modern Large Language Models (LLMs) are increasingly central to AI systems — powering everything from enterprise chatbots to multimodal assistants and autonomous agents. However, running, fine-tuning, and maintaining these massive models can be resource-intensive and expensive, especially for Europe

How to Use Google Cloud TPUs with Hugging Face Libraries

The rapid growth of large language models (LLMs) and transformer architectures has driven the demand for specialized hardware. While GPUs have been the traditional choice, Google Cloud TPUs (Tensor Processing Units) offer significant acceleration for deep learning workloads, especially when working

PyTorch vs TensorFlow: Which to Choose, When, and Why?

The AI and machine learning ecosystem has grown rapidly, with PyTorch and TensorFlow emerging as two of the most widely adopted frameworks. Choosing between them depends on project requirements, developer expertise, ecosystem compatibility, and deployment goals.

Revolutionizing Recruitment: Building an AI-Powered HR System with FastAPI, Streamlit, and Groq

built an AI-Powered HR Recruitment System that streamlines candidate screening, evaluates resumes, and even generates interview questionsΓÇè-ΓÇèall with the help of FastAPI, Next.js, and Groq API.

Optimizing LLMs: LoRA, QLoRA, SFT, PEFT, and OPD Explained

Understand how to efficiently optimize large language models using techniques like LoRA, QLoRA, SFT, PEFT, and OPD. A must-read for AI engineers and ML practitioners working with LLMs.