Explains reasoning models vs LLMs, philosophizes about building from scratch, and shows Python/PyTorch setup with uv.
A talk on implementing LLM architectures from scratch in Python and PyTorch, covering workflow for analyzing new models.
A talk on implementing LLM architectures from scratch in Python and PyTorch, focusing on tracing design changes in open-weight models.
A developer shares how they solve programming problems by inspecting objects and searching manually, avoiding LLM reliance.
A daily tech reading list covering Kubernetes updates, AI tools, Java, JavaScript trends, and new software stacks.
Compares DGX Spark and Mac Mini for local PyTorch development, focusing on LLM inference and fine-tuning performance benchmarks.
A technical comparison of the DGX Spark and Mac Mini M4 Pro for local PyTorch development and LLM inference, including benchmarks.
A hands-on guide to understanding and implementing the Qwen3 large language model architecture from scratch using pure PyTorch.
A hands-on tutorial implementing the Qwen3 large language model architecture from scratch using pure PyTorch, explaining its core components.
A tutorial on creating a production-ready Docker image for PyTorch models using Torch Serve, including model archiving and dependency management.
Guide to deploying a low-cost NVIDIA GPU VM on Azure using MSDN credits for PyTorch and TensorFlow machine learning development.
A guide to implementing LoRA and the new DoRA method for efficient model finetuning in PyTorch from scratch.
A technical guide implementing DoRA, a new low-rank adaptation method for efficient model finetuning, from scratch in PyTorch.
Techniques to reduce memory usage by up to 20x when training LLMs and Vision Transformers in PyTorch.
A guide to 9 PyTorch techniques for drastically reducing memory usage when training vision transformers and LLMs, enabling training on consumer hardware.
A tutorial on fine-tuning a BERT model for text classification using the new PyTorch 2.0 framework and the Hugging Face Transformers library.
Learn techniques to speed up PyTorch model training by 8x using PyTorch Lightning, maintaining accuracy while reducing training time.
Techniques to accelerate PyTorch model training by 8x using PyTorch Lightning, with a DistilBERT fine-tuning example.
A tutorial on coding self-attention, multi-head attention, causal attention, and cross-attention in LLMs using Python and PyTorch.
A comparison of AutoAugment, RandAugment, AugMix, and TrivialAugment image augmentation methods in PyTorch for reducing overfitting.
