This repo contains the code for the workshop on Generative AI Large Language Model Workshop for Financial Services. The workshop is designed to help you understand how to use leverage SageMaker to train, tune, and deploy Large Language Models.

Getting started

If running this as part of an AWS hosted event, follow the instructions here to setup your environment.

If running this on your own, follow the instructions below to setup your environment.

1. Make sure you have access to a SageMaker Studio environment. You can also use a SageMaker Notebook Instance or any other Jupyter Notebook environment that has programmatic access to AWS resources.
2. Ensure your execution role has the following permissions:

SageMaker

CreateModel
CreateEndpointConfig / DeleteEndpointConfig
CreateEndpoint / DeleteEndpoint
CreateTrainingJob 

SageMaker Runtime

InvokeEndpoint

3. Clone this repo to your environment

   git clone https://github.com/aws-samples/large-model-workshop-financial-services.git
   cd large-model-workshop-financial-services

4. Navigate to the lab1 directory and open the few_shot_learning.ipynb notebook. Follow the instructions in the notebook to complete the lab.

Contents

Lab 1: Few Shot Learning - Introductory example showing how to fine-tune a sentence-transformer model for a classification task.

Lab 2: Large Language Model Tuning - Shows how to fine-tune a FLAN-T5 model for dialogue summarization.

Lab 3: Cost Effective Multi-Model Deployments - Shows how to deploy multiple models in a single endpoint to reduce inference costs.

Security

See CONTRIBUTING for more information.

License

This repo is licensed under the MIT-0 License. See the LICENSE file.