Product design question

How would you improve onboarding for a developer using Transformers for the first time?

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What this question tests

Product design for developer onboarding, where the user is technical but unfamiliar with this specific library's conventions.

Clarify the user: a developer comfortable with Python but new to Transformers' API, model loading, and tokenizer conventions.
State the pain point: too many ways to load a model and unclear which pretrained checkpoint or pipeline abstraction fits their task.
Propose the flow: a task-first quickstart, where the user picks their task, like text classification, and gets one clear working code snippet.
Improve error messages for common first mistakes, like mismatched tokenizer and model, with direct links to the fix.
Define success: time from install to first successful inference call, and drop-off rate at the first code example.

Anchors onboarding around task selection, like the high-level pipeline abstraction, rather than the lower-level model and tokenizer classes that overwhelm beginners.
Proposes fixing specific known friction points, like confusing errors from tokenizer and model mismatches, with actionable messages.
Names a concrete metric, like time to first successful inference under 10 minutes, as the target for a good onboarding experience.
Distinguishes this from a general beginner tutorial by keeping the audience technical but library-unfamiliar.

Designing a generic beginner-friendly UI when the real user is a developer who just needs the right entry point.
Ignoring that confusing error messages are often the actual point of drop-off, not the initial documentation.

How would you measure where developers get stuck in the Transformers docs today?
What would change about this if the user is fine-tuning rather than just running inference?