Fine-tuning Qwen3-1.7B on the MATH dataset using supervised fine-tuning (SFT) on Lambda Labs, bridging the gap from local MacBook development to cloud GPU training.

A practical guide to developing ML training code on a MacBook with Apple Silicon, then seamlessly deploying to Google Colab or multi-GPU cloud instances like Lambda Labs.

The complete end-to-end journey — building a Transformer language model from scratch and training it on TinyStories, covering BPE tokenization, multi-head attention, training loop design, and text generation.

Assembling a complete training pipeline for Transformer LMs — from AdamW optimizer and learning rate scheduling to memory-efficient data loading, checkpointing, and decoding strategies.

Implementing Softmax, Log-Softmax, and Cross-Entropy from scratch in PyTorch, with key mathematical tricks for numerical stability and essential tensor operations explained.

Planning LLM training — deep dive into cross-entropy loss, perplexity, SGD vs AdamW optimizers, memory requirements, computational cost estimation, learning rate schedules, and gradient clipping.

Dissecting the computational anatomy of GPT-2 models — understanding where FLOPs go during inference, how scaling changes the picture, and which optimization techniques matter most.

A survey of popular Transformer architectures — comparing encoder-decoder, decoder-only, and encoder-only designs across GPT, BERT, T5, LLaMA, and modern trends in the field.

Building a Transformer language model from scratch in PyTorch — covering every component from multi-head attention and RoPE embeddings to SwiGLU activations and the complete model assembly.

Demystifying GPT-2's pre-tokenization regex pattern — how one carefully crafted regex handles text from multiple languages, scripts, and symbol sets for BPE tokenization.