model-selection

Model Selection

Guides the user through selecting a base model based on their use case.

When to Use

• User asks which model to use
• User wants to select or change their base model
• User mentions a model name or family (e.g., "Llama", "Mistral", "Nova") — the exact Hub model ID still needs to be resolved
• User wants to evaluate a base model before deciding whether to finetune

Prerequisites

• A use_case_spec.md file exists. If not, activate the use-case-specification skill to generate it first.

Workflow

Step 1: Check Region

Run:

python -c "import boto3; print(boto3.session.Session().region_name)"

• None → STOP. Tell user: "Set your region via export AWS_DEFAULT_REGION=us-west-2 or aws configure."
• Set → store REGION in context, continue.

Step 2: Discover Hub

1. List all available SageMaker Hubs in the user's region by calling the SageMaker ListHubs API using the aws___call_aws tool.

2. From the results, filter out any hub whose HubDescription contains "AI Registry" — these do not contain JumpStart models.

3. The remaining hubs are eligible (e.g., SageMakerPublicHub and any private hubs).

4. If exactly one eligible hub exists, use it automatically — do not ask the user.

5. If multiple eligible hubs exist, present them to the user and ask which one to use. Example:

   I found the following model hubs:
   - SageMakerPublicHub — SageMaker Public Hub
   - Private-Hub-XYZ — Private Hub models
   Which hub would you like to use?
   

6. Store the selected hub name for use in subsequent steps.

Step 3: Select Base Model

First, retrieve all available SageMaker Hub model names by running: python model-selection/scripts/get_model_names.py <hub-name>.

Present all available models to the user with their licenses before making any recommendations. Cross-reference the model list with references/model-licenses.md and display each as <model name> - [<license>](<url>). For example: "Qwen3-4B - Apache 2.0"