Part 4
Completed
Train a Mini Transformer
Trained a complete 6-layer, 8-head, 512-dim transformer from scratch on OpenWebText. Implemented distributed training with gradient accumulation, mixed precision, and learning rate scheduling. Achieved coherent text generation and analyzed scaling behavior.
What I Built
Trained a complete 6-layer, 8-head, 512-dim transformer from scratch on OpenWebText. Implemented distributed training with gradient accumulation, mixed precision, and learning rate scheduling. Achieved coherent text generation and analyzed scaling behavior.
Key Concepts
Distributed TrainingGradient AccumulationMixed PrecisionLearning Rate SchedulingWarmupWeight Decay
Architecture
1
6-Layer Transformer2
AdamW Optimizer3
Cosine LR Schedule4
Gradient Clipper5
Distributed Data ParallelResults
Trained to 3.2 perplexity on validation set. Model generates coherent paragraphs with consistent topic maintenance over 100+ tokens.
Key Learnings
- Training stability is the primary challenge in small-scale setups
- Learning rate warmup is non-negotiable for transformers
- Mixed precision training requires careful loss scaling
Challenges
- Training instability in early iterations
- Memory constraints limiting batch size
- Evaluating generative quality beyond perplexity