/LLM-Recommendation-Systems

This project fine-tunes large language models (LLMs) for text-based recommendations, using a novel prompt mechanism to improve accuracy and user satisfaction. It demonstrates efficient model adaptation with diverse datasets, leveraging advanced libraries and techniques for optimal performance.

Primary LanguageJupyter Notebook

LLM-Recommendation-Systems

Fine-Tuning LLMs for Text-Based Recommendations

This project explores the potential of fine-tuning large language models (LLMs) specifically for text-based recommendations, aiming to improve accuracy and user satisfaction. It introduces a novel prompt mechanism that transforms relationship information into natural language text, enabling the LLM to interpret user interactions and make more informed recommendations.

Project Overview

The project focuses on demonstrating the efficiency of fine-tuning LLMs for recommendation tasks using a diverse and expansive dataset. The implementation includes the following phases:

  1. Data Extraction and Preprocessing:

    • Extracted subsets of Amazon review data and converted them into DataFrames.
    • Removed unnecessary columns.
    • Filtered out users with insufficient purchase histories.
    • Transformed the remaining data into prompts, forming the input for the LLM.

  2. Prompt Engineering:

    • Explored various prompting techniques.
    • Settled on a three-part structure for each prompt: instruction, input details, and ground truth output.
    • This structure provides the model with a clear task, relevant information, and the desired output format.

  3. Model Implementation:

    • Utilized the Unsloth AI and HuggingFace libraries for pre-trained models and customization tools.
    • Considered QLoRA, a technique for improving model performance.
    • Created consolidated SFTTrainer and TrainingArguments objects to centralize hyperparameter adjustments during training.