Language Models
ver. 1.0.0
End-to-end practical guide: how transformers work, how to build/train/finetune GPT-style LLMs, and how to apply them to tasks like classification and instruction following
This node is a hands-on, end-to-end course on transformer-based language models. It explains why transformers suit text, how text becomes tokenized embeddings, and the mechanics of self-attention and transformer blocks. It then walks through implementing causal multi-head attention and a GPT-2 (124M) model in PyTorch, training and sampling from it, converting pretrained models into classifiers, and instruction‑tuning to produce assistants — including practical details for data preparation, training loops, checkpointing, decoding strategies, and evaluation.
Overview This collection provides a complete, practical path from the underlying principles of transformers to building, training, adapting, and evaluating GPT-style language models in PyTorch. It combines conceptual foundations with hands-on implementations and applied workflows so you can both understand why transformers work for language and ship working models.
Key conceptual foundations - Why transformers: contrasts with fully connected and convolutional approaches and explains how dot-product self-attention (queries, keys, values) produces contextual token representations better suited to variable-length, long-range dependencies in text. - Attention mechanics: per-token and matrix formulations of scaled dot-product attention, positional encodings, multi-head attention, LayerNorm, residual connections, and the encoder / decoder / encoder-decoder variants and their masking/objectives. - Scalability and modalities: the O(N^2) cost of attention, common approaches to reduce cost, and how tokenizing images (patches) or other modalities reuses the same transformer ideas. - LLM context: what large language models are, why scaling decoder-only transformers on large corpora yields emergent zero-/few-shot behavior, and the pretrain→fine-tune lifecycle and common application areas.
From text to model inputs - Tokenization and vocabulary: regex-based tokenization principles, vocabulary building, handling unknowns, document boundaries, and byte-pair encoding (BPE) to avoid out-of-vocabulary tokens. - Dataset construction for next-token prediction: sliding window examples, batching, and preparing training/validation loaders. - Embeddings and positional encodings: mapping token IDs to learnable vectors (nn.Embedding) and combining absolute positional embeddings to form the model input tensor.
Implementation (PyTorch, step-by-step) - Causal multi-head self-attention: implement attention for one token, vectorize across tokens, add learned Q/K/V projections and scaling, apply causal masking, dropout, batching, and efficient multi-head implementations suitable for GPT-style decoders. - Transformer blocks: build position-wise feed-forward networks, implement LayerNorm with learnable scale/shift, GELU, residual connections, and stack blocks into a full autoregressive transformer. - Full GPT-2 124M: assemble embeddings, block stack, final norm, and output head; measure parameter counts and memory use; apply weight tying and implement a greedy generation loop to map logits to text.
Training, evaluation, and deployment - Training loop: compute cross-entropy loss and perplexity, implement AdamW optimizer and training/validation loops, interpret loss curves including memorization effects, save and resume checkpoints (model + optimizer). - Decoding and sampling: replace greedy decoding with temperature-controlled multinomial sampling, top-k filtering, and combine decoding options into a generate() with early stopping. - Mapping pretrained weights: load official GPT-2 weights into your PyTorch implementation to generate coherent text.
Model adaptation and fine-tuning - Task adaptation (classification): convert a pretrained GPT-2 into a compact end-to-end text classifier — prepare and split data, create Dataset pipelines with tokenization/padding, freeze/selectively load weights, replace the language-model head with a classification head, train and evaluate with accuracy metrics, and export an inference wrapper. - Instruction fine-tuning: supervised tuning to make a model follow arbitrary instructions — format instruction-response datasets, implement dynamic padding and target masking, fine-tune models (e.g., GPT-2 Medium), persist outputs, and perform automatic evaluation using a local LLM judge. The unit also outlines next steps (RLHF, better data, calibration) for further improvement.
Practical roadmap and next steps - High-level, hands-on roadmap: start from tokenization and data pipelines → implement attention and transformer layers → assemble a small GPT model → train/evaluate and experiment with decoding → load pretrained weights for better results → adapt to downstream tasks (classification, instruction-following) → iterate with better data, tuning, and scaling. - Emphasis on experimentation: measure parameter/memory trade-offs, inspect loss curves, try different decoding/hyperparameter strategies, and progressively apply techniques for efficiency or improved behavior (sparse/efficient attention, larger pretraining corpora, fine-tuning regimes).
Who this is for - Practitioners who want both intuition and code: the material is suitable for engineers and students who want to implement transformers from first principles, run training loops, and adapt models to real tasks. - Teams building production LLMs: the units provide the practical recipes, code patterns, and evaluation methods needed to move from research concepts to functioning models and simple deployed classifiers or instruction-following assistants.
In short This node unifies theory and practice: it teaches why and how transformers produce contextual language representations, how to convert raw text into model-ready tensors, how to implement and train GPT-style decoders in PyTorch, and how to adapt pretrained models for classification and instruction-following — all with concrete code, training recipes, and evaluation workflows.
Units
Chapter 12: Transformers
How transformers turn text into contextual representations and generated text, why they suit language better than CNNs/FC layers, and how they scale and adapt to other modalities
1 Understanding large language models
Foundational understanding of what LLMs are, how they are built and applied, and the practical roadmap to start building one.
2 Working with text data
Turn raw text into the continuous input embeddings an LLM consumes.
3 Coding attention mechanisms
Practical end-to-end construction of causal multi‑head self‑attention in PyTorch, from the core math to an efficient batched implementation
4 Implementing a GPT model from scratch to generate text
Implement, assemble, analyze, and run a GPT‑2 124M model in PyTorch from primitives to text generation.
5 Pretraining on unlabeled data
Train, evaluate, sample from, persist, and load a GPT-2 style language model
6 Fine-tuning for classification
Turn a pretrained GPT‑2 into a compact, end‑to‑end text classifier (data prep, model adaptation, training, evaluation, and deployment)
7 Fine-tuning to follow instructions
Instruction-tune a GPT-2 Medium to follow arbitrary instructions, evaluate it automatically, and understand paths to further improvement.