صقل Fine Tuning النموذج GPT-3.5 Turbo

This project focuses on fine-tuning GPT-3.5 for a custom dataset in JSONL format, designed to enhance the customer service experience for a bookstore. The dataset consists of conversational data where a virtual assistant interacts with customers, answering common queries related to book recommendations, purchasing processes, promotions, and store policies. The assistant, modeled using GPT-3.5, provides responses in Arabic, offering a localized and culturally relevant experience.

The fine-tuning process involves training GPT-3.5 on this JSONL dataset, which contains user queries and assistant responses in a structured format. Each entry in the dataset includes a multi-turn conversation between the system (assistant) and the user, ensuring the model learns to handle a wide variety of questions, including inquiries about book genres, events, bestsellers, and return policies.

By fine-tuning GPT-3.5 on this bookstore-specific data, the model becomes adept at offering personalized, accurate, and contextually appropriate responses, improving customer satisfaction and automating routine customer service interactions in the bookstore's ecosystem. Key aspects include data preprocessing, model fine-tuning, and performance optimization to align with business goals.