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:
- AWS Account: Ensure you have an AWS account with necessary permissions.
- 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:
Read the full article at Towards AI - Medium
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