[Feature] Direct Support / Documentation for Early Stopping

[devansh-lodha]

Hi Unsloth Team,

First off, thanks for the great library and making finetuning more accessible!

I understand that early stopping (based on validation loss, for example) is currently possible by configuring the standard Hugging Face TrainingArguments when using SFTTrainer (by setting evaluation_strategy, eval_steps, load_best_model_at_end, metric_for_best_model, early_stopping_patience, etc., and providing an eval_dataset).

However, as a user, especially one newer to the ecosystem, discovering and configuring these multiple arguments correctly can be a bit of a hurdle. It would be really helpful to have more "direct" support or visibility for this feature within the Unsloth ecosystem.

Specifically, I'd like to request:

1. Clear Documentation: Adding a dedicated section or example to docs.unsloth.ai explaining exactly how to set up early stopping (based on validation loss) within an Unsloth training script (like the Qwen-VL example notebook). Showing the necessary SFTConfig arguments would be fantastic.
2. (Optional) CLI Support: Perhaps consider adding relevant command-line arguments (like --evaluation_strategy, --eval_steps, --load_best_model_at_end, --metric_for_best_model, --early_stopping_patience) to the unsloth-cli.py script for users who prefer that interface.

Making this useful feature more discoverable and straightforward to implement would be a great enhancement.

Thanks for considering!