Browsing: Guide

In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with the fundamentals…

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from pathlib import Path import re def parse_trc(trc_path): “””Parse a .trc file and return…

In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context language model inference more efficient. We…

In this tutorial, we explore how to use Google’s LangExtract library to transform unstructured text into structured, machine-readable information. We begin by installing the required dependencies…

Open-weight models are driving the latest excitement in the AI landscape. Running powerful models locally improves privacy, cuts costs, and enables offline use. But the open-source…

print(“\n📊 MODEL EVALUATION\n”) eval_results = trainer.evaluate() print(” Evaluation Results:”) for key, value in eval_results.items(): if isinstance(value, float): print(f” {key:<25}: {value:.4f}”) from sklearn.metrics import classification_report, confusion_matrix preds_output…

Most AI tools rely on the internet, sending your prompts to remote servers for processing before returning results. This process has always been invisible to users.…

Image by Author   # Introduction  Running a top-performing AI model locally no longer requires a high-end workstation or expensive cloud setup. With lightweight tools and smaller…

In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision acceleration can be explored in a…

In this tutorial, we build a complete end-to-end pipeline using NVIDIA Model Optimizer to train, prune, and fine-tune a deep learning model directly in Google Colab.…