:

🤖 Domain Expert AI Model: Fine-Tuning Meta Llama 2 Project Overview This project demonstrates the development of a domain-specific expert AI model by fine-tuning the Meta Llama 2 (7B) foundation model. The goal is to enhance customer experience and streamline information delivery through applications like intelligent chatbots, internal knowledge systems, and automated text generation for company collateral.

Project Objectives Fine-tune a pre-trained large language model to specialize in a chosen domain (Finance, Medical, or IT). Create a proof of concept (POC) for a domain expert model capable of generating accurate and contextually relevant text responses. Use Amazon SageMaker and other AWS tools to implement and deploy the solution effectively. 🛠 Key Features Domain Adaptation: The model is trained on unstructured text datasets specific to a domain, ensuring deep understanding of industry-specific language and concepts. Applications: AI-powered chat applications. Internal knowledge management tools. Automated text generation for business collateral.

Learning Outcomes Through this project, you'll gain:

Hands-on experience in fine-tuning large language models. Practical knowledge of AWS tools like Amazon SageMaker. Insights into applying AI for real-world business challenges.

Deliverables Fine-Tuned Model: A domain-specific language model. Report & Presentation: Documentation of the training process, challenges faced, and solutions implemented.

Technologies Used Model: Meta Llama 2 (7B) Platform: Amazon SageMaker, AWS Toolkit Programming: Python