A reflection on a decade-old blog post about deep learning, examining past predictions on architecture, scaling, and the field's evolution.
A curated collection of articles on software architecture, AI tools, code quality, and developer psychology, exploring foundational concepts and modern challenges.
A tutorial on building a transformer-based language model in R from scratch, covering tokenization, self-attention, and text generation.
Explains how LLMs work by converting words to numerical embeddings, using vector spaces for semantic understanding, and managing context windows.
Introducing ModernBERT, a new family of state-of-the-art encoder models designed as a faster, more efficient replacement for the widely-used BERT.
The article explores how the writing process of AI models can inspire humans to overcome writer's block by adopting a less perfectionist approach.
Explores methods for using and finetuning pretrained large language models, including feature-based approaches and parameter updates.
A practical guide to fine-tuning open-source large language models (LLMs) using Hugging Face's TRL and Transformers libraries in 2024.
A beginner-friendly guide to using HuggingFace's Transformers and Diffusers libraries for practical AI applications, including image generation.
Explains AI transformers, tokens, and embeddings using a simple LEGO analogy to demystify how language models process and understand text.
A guide to 9 PyTorch techniques for drastically reducing memory usage when training vision transformers and LLMs, enabling training on consumer hardware.
Guide to finetuning large language models on a single GPU using gradient accumulation to overcome memory limitations.
A tutorial on fine-tuning a BERT model for text classification using the new PyTorch 2.0 framework and the Hugging Face Transformers library.
A technical guide on fine-tuning large FLAN-T5 models (XL/XXL) using DeepSpeed ZeRO and Hugging Face Transformers for efficient multi-GPU training.
A curated reading list of key academic papers for understanding the development and architecture of large language models and transformers.
A curated reading list of key academic papers for understanding the development and architecture of large language models and transformers.
A tutorial on fine-tuning a BERT model for text classification using Hugging Face Transformers and Google Cloud TPUs with PyTorch.
A tutorial on fine-tuning the LiLT model for language-agnostic document understanding and information extraction using Hugging Face Transformers.
A technical explanation of the attention mechanism in transformers, building intuition from key-value lookups to the scaled dot product equation.
A tutorial on creating custom inference handlers for Hugging Face Inference Endpoints to add business logic and dependencies.
