How Foundation Models Are Built and Extended Beyond Text

Data, scaling, MoE, efficient adaptation, multimodality, diffusion, prompting, and evaluation.

On this page

• 1 What the reader will build
• 2 Chapter 1 · Data becomes model behavior
• 3 Chapter 2 · Scaling training without losing control
• 4 Chapter 3 · Sparse models and efficient adaptation
• 5 Chapter 4 · Extending language models to vision
• 6 Chapter 5 · Diffusion and generative media
• 7 Chapter 6 · Prompt optimization and honest evaluation
• 8 Reproducibility checklist

Note

This is a living essay under active development. Every chapter will be backed by a reproducible Colab notebook and at least one controlled experiment.

Thesis. Model capability is not produced by architecture alone. Data, compute allocation, optimization, adaptation, modality interfaces, inference strategy, and evaluation form one coupled system.

1 What the reader will build

• A small pre-training data-cleaning and packing pipeline
• A Transformer compute and memory estimator
• A toy MoE router plus LoRA and model-soup experiments
• A miniature vision-language alignment pipeline
• A toy diffusion model with a visual denoising trajectory
• A prompt and evaluation harness with confidence intervals

2 Chapter 1 · Data becomes model behavior

2.1 History and theory

2.2 Mathematics

2.3 Colab implementation

2.4 Ablation and visualization

2.5 Failure analysis and interview takeaways

3 Chapter 2 · Scaling training without losing control

3.1 History and theory

3.2 Mathematics

3.3 Colab implementation

3.4 Ablation and visualization

4 Chapter 3 · Sparse models and efficient adaptation

4.1 History and theory

4.2 Mathematics

4.3 Colab implementation

4.4 Ablation and visualization

5 Chapter 4 · Extending language models to vision

5.1 History and theory

5.2 Mathematics

5.3 Colab implementation

5.4 Ablation and visualization

6 Chapter 5 · Diffusion and generative media

6.1 History and theory

6.2 Mathematics

6.3 Colab implementation

6.4 Ablation and visualization

7 Chapter 6 · Prompt optimization and honest evaluation

7.1 History and theory

7.2 Mathematics

7.3 Colab implementation

7.4 Model landscape

8 Reproducibility checklist

• Data sources, filters, and sampling weights are documented
• Compute and memory estimates state their assumptions
• Adapter and soup checkpoints share a compatible base
• Every multimodal experiment reports its token budget
• Generative comparisons control seeds and sampling settings
• Benchmark claims include dates, versions, and primary sources
• Confidence intervals and failure cases accompany headline metrics