# Model Pre-training

> Data mixtures, next-token objectives, scaling laws, distributed training, checkpoints, and training stability.

Canonical: https://learn.significanthobbies.com/curriculum/concepts/ml-pretraining

Data mixtures, next-token objectives, scaling laws, distributed training, checkpoints, and training stability.

- Difficulty: core
- Tracks: AI Systems

## Mental model

Pre-training converts data and compute into general capability. The core loop is data sampling, forward/backward passes, optimizer updates, checkpointing, and continuous loss/quality diagnosis.



## Primary sources

- [Stanford CS336 — Language Modeling from Scratch](https://cs336.stanford.edu/) (doc)

## Practice

### Design exercise: Model Pre-training

Data mixtures, next-token objectives, scaling laws, distributed training, checkpoints, and training stability. Implement designOutline() returning non-empty values for: dataMixture, trainingLoop, scalingPlan. Each value must name a concrete mechanism or decision.

**Expected evidence:** A design outline with dataMixture, trainingLoop, scalingPlan plus an explicit failure mode or trade-off.

## Review prompts

- You have a fixed compute budget. What do scaling laws tell you about splitting it between model size and tokens?

## Build evidence

- **Synthesize: AI Models & Training** — Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. Produce one working system, benchmark, or evidence-backed design that integrates the path.

## Prerequisites

- [AdamW Optimizer](https://learn.significanthobbies.com/curriculum/concepts/ml-adamw)

## Related concepts

- [AdamW Optimizer](https://learn.significanthobbies.com/curriculum/concepts/ml-adamw)
- [Training & Debugging](https://learn.significanthobbies.com/curriculum/concepts/ml-training)

## Learning paths

- [12-Week AI Models & Training](https://learn.significanthobbies.com/curriculum/roadmaps/ai-models-training-12w)