Rotary Position Embeddings (RoPE) is a technique for encoding token positions in a sequence. It is widely used in many models and works well for standard context lengths. However, it requires adaptation for longer contexts. In this article, you will…

import dataclasses import os   import datasets import tqdm import tokenizers import torch import torch.nn as nn import torch.nn.functional as F import torch.optim.lr_scheduler as lr_scheduler from torch import Tensor   # Load the tokenizer tokenizer = tokenizers.Tokenizer.from_file(“bpe_50K.json”)   # Load…

In this article, you will learn three reliable techniques — ordinal encoding, one-hot encoding, and target (mean) encoding — for turning categorical features into model-ready numbers while preserving their meaning. Topics we will cover include: When and how to apply…

A language model is a probability distribution over sequences of tokens. When you train a language model, you want to measure how accurately it predicts human language use. This is a difficult task, and you need a metric to evaluate…

Practical Agentic Coding with Google JulesImage by Editor Introducing Google Jules If you have an interest in agentic coding, there’s a pretty good chance you’ve heard of Google Jules by now. But if not, now’s the time to learn all…

Training a language model is memory-intensive, not only because the model itself is large but also because the long sequences in the training data batches. Training a model with limited memory is challenging. In this article, you will learn techniques…