Model Training & Fine-Tuning¶

Series Overview

This series walks you from raw data to a production-ready fine-tuned model. Each article is self-contained but designed to be read in order. You will build intuition, then skills, then production habits.

What You Will Learn¶

Article	Topic	Level
1 — Datasets	Curating, cleaning, and tokenizing training data	Beginner
2 — Training	Pre-training loop, optimizers, schedulers, mixed precision	Beginner → Intermediate
3 — Fine-Tuning	Full fine-tune, LoRA, QLoRA, instruction tuning, RLHF	Intermediate → Advanced
4 — Evaluation	Perplexity, BLEU, ROUGE, benchmarks, human eval	Intermediate
5 — Experiment Tracking	MLflow, W&B, reproducible runs, hyperparameter search	Intermediate → Advanced

Mental Model¶

Raw Text / Labeled Data
        │
        ▼
  ┌─────────────┐
  │  Dataset    │  ← collect, clean, split, tokenize
  └──────┬──────┘
         │
         ▼
  ┌─────────────┐
  │  Training   │  ← forward pass, loss, backward, optimizer step
  └──────┬──────┘
         │
         ▼
  ┌─────────────┐
  │  Fine-Tune  │  ← adapt pre-trained weights to your task
  └──────┬──────┘
         │
         ▼
  ┌─────────────┐
  │ Evaluation  │  ← measure quality, catch regressions
  └──────┬──────┘
         │
         ▼
  ┌──────────────────────┐
  │ Experiment Tracking  │  ← log everything, compare runs, reproduce
  └──────────────────────┘

Prerequisites¶

Python 3.10+
PyTorch 2.x
transformers, datasets, peft, trl, evaluate from HuggingFace
Basic understanding of neural networks (forward/backward pass, loss)

pip install torch transformers datasets peft trl evaluate \
            accelerate bitsandbytes mlflow wandb

Key Vocabulary¶

Term	Meaning
Pre-training	Training a model from scratch on a massive unlabeled corpus
Fine-tuning	Continuing training on a smaller, task-specific dataset
PEFT	Parameter-Efficient Fine-Tuning — only update a small subset of weights
LoRA	Low-Rank Adaptation — inject small trainable matrices into frozen layers
SFT	Supervised Fine-Tuning on instruction/response pairs
RLHF	Reinforcement Learning from Human Feedback
Tokenizer	Converts text ↔ integer token IDs
Perplexity	How "surprised" the model is by held-out text — lower is better