AI model training has four moving parts: pre-training, post-training (fine-tuning), training data, and sampling at inference. When these work together, the model sounds natural and stays useful. Here’s a concise overview.
Pre-training vs post-training
Before the details, here’s a side-by-side of pre-training vs post-training and their typical techniques:
Pre-training
In pre-training, the model learns from huge datasets by predicting the next token. Text becomes numbers through tokenization and embeddings.
This is self-supervised learning (no labels, lots of text). You can think of this phase as learning the language before the task. You get a general model with solid language instincts. It isn’t tuned to your task yet.
Post-training
Post-training makes it practical. With supervised fine-tuning, you show task-specific examples so the model follows directions more consistently.
Instruction tuning sharpens “do what I asked”. RLHF (reinforcement learning from human feedback) keeps tone, helpfulness, and safety close to what people want.
If resources are tight, use LoRA or QLoRA: instead of updating every weight, you train small adapters. It is faster and cheaper than retraining the whole model. This step adapts the model to your use case.
Training essentials: data, parameters, sampling
1. Training data
Strong models begin with clean, well-labeled data. Here’s the pipeline:
Checklist:
- Clean and deduplicate the corpus
- Label what matters
- Make clear train, validation, and test sets
- Consider data augmentation or synthetic data for rare cases
Messy data leads to odd mistakes and validation loss creeps up. Even modest cleaning can improve stability. Domain-specific data often lifts performance on domain tasks.
Mini example: we started with a general model, then fine-tuned on a small legal corpus (contracts and clauses). After two short runs, drafts read clearer and needed roughly 50% fewer edits from the legal team. A small, clean dataset beats a larger, messy one.
2. Model parameters (“param”)
Definitions
- Parameters are the learned weights
- Hyperparameters are your settings
Core hyperparameters
- Learning rate: too high is unstable; too low is slow
- Batch size and epochs: balance speed and generalization
- Optimizer: AdamW is a safe default
Regularization and stability
- Dropout, weight decay, gradient clipping, early stopping
Tracking progress
- Evaluation metrics, validation loss, sometimes perplexity
Production reality
In practice, hardware and budgets set the pace. GPUs/TPUs, memory, throughput, latency, and the context window decide what you can run in production.
3. Sampling parameters (inference)
Core settings
- Temperature: higher means more variety, lower means more precision
- Top-p (nucleus sampling): pick from the smallest set of tokens whose probabilities add up to p (for example, 0.9)
- Top-k: pick only among the top k tokens
Tuning tips
If outputs drift, lower temperature; if they feel constrained, raise it slightly.
Recommended defaults and guardrails
A common combo is top-p ≈ 0.9 with temperature ~0.7. Add repetition penalty, min/max length, and stop tokens to avoid loops and keep answers tight.
