Pick a depth. Each prompt opens in your AI pre-loaded with the lesson. Click a row to preview the prompt.
Every serious open-weight release ships a model card, and the post-training section is usually the densest, most information-rich part of it, because it's where the lab explains how a base model became the assistant you're downloading. Learning to read it fluently is a practical skill: it tells you which stages were used (SFT only, SFT plus DPO, full RLHF, RLAIF), roughly how much data went into each, what safety process ran alongside it, and what known limitations survived post-training. This matters directly for your own work — if you're about to fine-tune a released instruct model further, the card tells you what alignment you might disturb, what chat template it expects, and what the lab already tried and rejected, saving you from re-discovering the same failure modes they already documented.
This isn't a code demo so much as a reading exercise — the snippet below is a simple checklist-extraction pattern you can literally reuse while skimming any model card.
apply_chat_template actually produces.CHECKLIST = [
"What SFT data mix was used (size, sources, human vs synthetic)?",
"Was a reward model trained? On what preference data / from whom?",
"Which RL algorithm was used, if any (PPO, DPO, GRPO, RLAIF)?",
"Was there a distinct 'safety' or 'harmlessness' stage, and how?",
"What chat template / special tokens does the instruct model expect?",
"What known limitations or regressions does the card admit to?",
]
def audit_card(card_text: str):
findings = {}
for item in CHECKLIST:
# In a real audit you'd search card_text for the relevant section;
# here we just show the structure of what you're looking for.
findings[item] = "<search card_text for this>"
return findings
# Try this for real: paste a model card's text into card_text and manually
# fill in each checklist item as you read.
card_text = "<paste a real model card here>"
for q, a in audit_card(card_text).items():
print(f"- {q}\n -> {a}")python3 main.py