Practical Guide to Transformer Configurations — Templates, Trade-offs & Tuning

1. Overview of Transformer Configurations

A "transformer configuration" describes the set of architectural hyperparameters and training settings that define a transformer model instance. These include structural choices (number of encoder/decoder layers, model dimension, number of attention heads, feed-forward hidden size), regularization (dropout rates, layernorm placement), and training/runtime settings (batch size, sequence length, optimizer and learning-rate schedule). This section briefly defines the pieces you will see in the templates below and why they matter for performance, compute, and latency.

Key structural parameters

These are the parameters that primarily determine model capacity and memory usage:

• Num layers (L): Total transformer blocks; more layers usually increase representational power but cost more memory and inference time.
• Model dimension (d_model): Embedding and attention projection width. Controls expressiveness per token.
• Heads (h): Number of attention heads; each head has size d_k = d_model / h (should divide evenly).
• FFN dimension (d_ff): Size of the position-wise feedforward layer; common ratios range from 2× to 4× d_model.

2. Choosing Configurations for Specific Tasks

2.1. Low-latency on-device (mobile, edge)

For on-device inference prioritize smaller d_model and fewer layers, and reduce sequence length if possible. Use fewer heads (e.g., 2–4) to simplify attention projection and prefer 1.5–2× FFN ratio. Quantization-aware training and knowledge distillation are recommended to retain accuracy.

• Example: L=6, d_model=320, h=4, d_ff=1024, dropout=0.1
• Behavior: low latency, good for short-sequence classification and small NER tasks.

2.2. High-accuracy medium-sized models (research / production)

Balanced "base" configurations work well for many NLP and vision-transformer tasks. They trade compute and memory for stronger generalization and are appropriate for server-side serving or fine-tuning on moderately sized datasets.

• Example: L=12, d_model=768, h=12, d_ff=3072, dropout=0.1
• Behavior: strong on language understanding, generation with manageable training cost.

2.3. Large models (pretraining / state-of-the-art)

Large configurations scale d_model, L, and often use wider FFNs and more heads. They require distributed training and careful optimizer/schedule choices to converge reliably.

• Example: L=24–48, d_model=1024–2048, h=16–32, d_ff=4096–8192, dropout=0.1
• Behavior: excellent generalization and transfer, but high compute and memory demands.

3. Common Configuration Templates (task-ready)

Templates explained

Below are practical, copy-pastable configuration templates (structural + training hints). Use them as starting points and adapt batch size, learning rate, and sequence length to your hardware and dataset.

4. Practical Tips for Tuning Transformer Configurations

Hyperparameter prioritization

If you must tune a small set of knobs, prioritize model size (d_model & L) first, then learning rate and batch size. Adjust number of heads only if d_model/h becomes too small (keep head size ≥ 32 for stable gradients in many implementations).

Regularization and stability

Use dropout (0.1) and layernorm variants (post-LN or Pre-LN depending on architecture). For deep models, Pre-LN often provides more stable training. Gradient clipping (1.0) prevents spikes; mixed-precision training (AMP) reduces memory and speeds training but monitor for instability.

5. Quick Comparison: When to pick which configuration

• Edge / Mobile: Tiny templates, aggressive quantization, distillation.
• Fine-tuning / Production: Base templates with careful LR and batch-size tuning.
• Pretraining / Research: Large templates with distributed training and advanced optimizers.

Checklist before committing to a configuration

• Does d_model divide evenly by the number of heads? If not, adjust h or d_model.
• Estimate GPU memory: bigger d_model and batch size multiply memory. Use gradient accumulation to simulate larger batches.
• Decide whether to use Pre-LN or Post-LN architecture based on depth and stability needs.

Final notes

Use the templates above as starting points and iteratively adjust based on validation metrics and hardware constraints. Track both throughput (tokens/sec) and per-token latency during evaluation. When in doubt, start from the "Base" template and run targeted ablations: reduce or increase L and d_model independently to observe marginal gains.