The Ultimate Guide to Fine-Tuning Foundation Models on AWS Sagemaker

Based on the detailed instructions provided, here's a summary of how to set up and run fine-tuning jobs for large language models (LLMs) like Llama 2 using Amazon SageMaker:

Prerequisites:

1. AWS Account: Ensure you have an AWS account with necessary permissions.
2. S3 Bucket: Set up an S3 bucket where your dataset, scripts, and model checkpoints will be stored.

Steps to Follow:

Step 1: Prepare Your Environment

• Install AWS CLI if not already installed.
• Set Up IAM Role: Ensure you have the necessary permissions for SageMaker and S3 access. You can use an existing role or create a new one with appropriate policies.

Step 2: Create Dataset & Scripts

• Prepare Training Data: Your dataset should be preprocessed and stored in your S3 bucket.
• Training Script (training_script.py): This script will contain the logic for fine-tuning your model. Ensure it's uploaded to your S3 bucket.

Step 3: Configure S3 Bucket Policy

• Add a policy to your S3 bucket that allows SageMaker access to read and write objects in the bucket.

Example Policy:

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