Utilizing Amazon Sagemaker and other AWS tools to fine-tune the Meta Llama 2 7B foundation model. This model has been trained for text-generation tasks. The goal is to adapt this model to your selected domain, enhancing its ability to understand and generate domain-specific text.
This repository features a "Generative AI with AWS" initiative, which serves as a practical demonstration of fine-tuning large language models using AWS SageMaker. The project is aimed at enhancing students' expertise in machine learning and large language models (LLMs) by developing a model customized for a specific domain. Using AWS tools, the project focuses on training a language model to produce text that is both contextually relevant and accurate. A significant aspect of this project is the rigorous evaluation of the fine-tuned model compared to the original, ensuring the model's improvements and effectiveness.
The primary goal is to create a domain-specific language model for the Information Technology (IT) sector. By leveraging advanced natural language processing (NLP) techniques, this project trains and deploys a large language model on AWS SageMaker, enabling it to generate highly relevant and informative text responses within the IT domain.
The dataset used is centered around issues in ubiquitous data management within IT. It includes discussions on topics such as mobility support, context awareness, collaborative capabilities, adaptability, and user interactions in computing environments. This dataset is instrumental in fine-tuning the language model to enhance its expertise in generating IT-specific content.
Choose a relevant dataset that covers IT-related challenges and requirements.
Configure an AWS SageMaker IAM Role to ensure the necessary permissions. Set up an AWS SageMaker Notebook Instance for code development. Request a GPU instance (ml.g5.2xlarge) to facilitate model fine-tuning.
Deploy the Meta Llama 2 7B model on AWS SageMaker. Use Python scripts to fine-tune the model with the IT dataset, improving its ability to understand and generate domain-specific text.
Deploy the fine-tuned model on SageMaker for generating responses.
Evaluate the model’s performance on IT-related tasks and queries( Dataset can be choosen as per requirement). Conduct a comparative analysis between the fine-tuned model and the original model to measure the improvements achieved through fine-tuning.
1.Amazon SageMaker: A service that facilitates the building, training, and deployment of machine learning models. 2.Meta Llama 2 7B Model: The foundational model used for fine-tuning, pre-trained for text generation tasks. 3.Python: Employed for scripting and interfacing with AWS services. 4.AWS Services: Includes IAM for managing access, EC2 for GPU resources, and S3 for storing data and model artifacts.
The project incorporates a detailed comparative evaluation to assess the performance enhancements of the fine-tuned model compared to the original, validating the effectiveness of domain-specific fine-tuning.
A thorough report documenting the project's approach and solutions developed has been prepared for submission.