Final model training run and evaluation report · CS336 Language Modeling From Scratch

Deliverableartifact

A short written report (markdown or notebook) summarizing your final model setup, metrics, sample outputs, observed limitations, and proposed future work.

Goals

Run a final training session with your chosen best config and document its training/validation curves and perplexity.
Generate a diverse set of samples for several prompts and include them in a short report.
Write a reflection on where your LM performs well vs poorly, grounded in evidence.
Propose at least three concrete next steps (e.g., better tokenizer, larger dataset, distributed training) and what you’d expect to learn from each.

Resources

📰CS336: Language Models From Scratch - Andy Timm 📰Stanford CS336 - Language Modeling from Scratch | Personal Website

Overview: what this report is and why it matters

You’re creating a final model training run and evaluation report: a short, concrete summary of what you actually trained, how it performed, what it’s good and bad at, and what you’d do next.

Think of it as the “state of the model” memo someone could read six months from now to:

Reproduce your best setup
Understand what the numbers mean in practice
Decide what to try next

We’ll structure your report into four core sections:

Best configuration you’re declaring as “final”
Metrics and sample outputs
Strengths and weaknesses
Future work

You can write this in Markdown (e.g. final_report.md) or as a notebook cell. I’ll give you a skeleton you can paste and fill in, then explain what belongs in each part and why.

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