Itanran-tuning

O yi awọn ipilẹ ti a kọ pada lati ṣe atunṣe ihuwasi. O yatọ si fifi awọn apẹẹrẹ kun si iyara tabi gbigba awọn iwe aṣẹ ni akoko idahun, ati pe ko tọju alaye otitọ lọwọlọwọ.

Awọn gbigba bọtini

• Ṣe apejuwe ihuwasi ti o yẹ ki o ṣe deede.
• Ṣe afiwe awọn omiiran ti o rọrun.
• Ṣe ayẹwo awọn anfani ati awọn atunṣe lori awọn iṣẹ-ṣiṣe ti a ṣe.

Define the behavior that needs to change. Consistent output style, a specialized classification task, and use of recent facts are different requirements. Prompting or retrieval may solve some of them without a training job. Compare those alternatives before adding model-maintenance work. Build examples that reflect the intended behavior and include difficult cases. Keep a held-out evaluation set apart from training and tuning decisions. Review labels, duplicate records, permissions, and any confidential information before using the dataset. Adaptation can update all parameters or a selected subset, depending on the method. Lower memory or less trainable parameters do not remove the need to evaluate the result model. Check both the target task and capabilities that should remain intact. Record the base model, data version, training settings, and resulting checkpoint. Evaluate deployment costs, response time, and rollback before release. When the source knowledge changes, decide whether to update retrieval, review the dataset, retrain, or change the product's evidence workflow.

1. Foju inu wo oluranlọwọ atilẹyin kan ti o mọ bi o ṣe le dahun ni kedere ṣugbọn o nilo eto imulo ti a ṣe imudojuiwọn ni gbogbo ọsẹ.
2. Bẹrẹ nipa idanwo gbigba ti eto imulo lọwọlọwọ dipo atunkọ nikan lati fi ọrọ tuntun sii.
3. Ti iṣoro gangan ba jẹ ikuna itẹramọṣẹ lati tẹle ọna kika idahun iduroṣinṣin, ṣe afiwe awọn ayipada kiakia ati data ti a ṣe ayẹwo daradara.

Ipinnu yii ṣe iyatọ awọn ẹri iyipada lati iyipada ihuwasi ti a kẹkọọ.