# Fine-tune curated hub models
In your private curated model hub, you can run fine-tuning training jobs using your model references. Model references point to a publicly available JumpStart model in the SageMaker AI public hub, but you can fine-tune the model on your own data for your specific use case. After the fine-tuning job, you have access to the model weights that you can then use or deploy to an endpoint.
You can fine-tune curated hub models in just a few lines of code using the SageMaker Python SDK. For more general information on fine-tuning publicly available JumpStart models, see [Foundation models and hyperparameters for fine-tuning](jumpstart-foundation-models-fine-tuning.md).
## Prerequisites
In order to fine-tune a JumpStart model reference in your curated hub, do the following:
1. Make sure that your user's IAM role has the SageMaker AI `TrainHubModel` permission attached. For more information, see [ Adding and removing IAM identity permissions](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_manage-attach-detach.html) in the *AWS IAM User Guide*.
You should attach a policy like the following example to your user's IAM role:
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Sid": "VisualEditor0",
"Effect": "Allow",
"Action": "sagemaker:TrainHubModel",
"Resource": "arn:aws:sagemaker:*:111122223333:hub/*"
}
]
}
```
------
**Note**
If your curated hub is shared across accounts and the hub content is owned by another account, make sure that your `HubContent` (the model reference resource) has a resource-based IAM policy that also grants the `TrainHubModel` permission to the requesting account, as shown in the following example.
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Sid": "AllowCrossAccountSageMakerAccess",
"Effect": "Allow",
"Principal": {
"AWS": "arn:aws:iam::111122223333:root"
},
"Action": [
"sagemaker:TrainHubModel"
],
"Resource": [
"arn:aws:sagemaker:*:111122223333:hub/*"
]
}
]
}
```
1. Have a private curated hub with a model reference to a JumpStart model that you want to fine-tune. For more information about creating a private hub, see [Create a private model hub](jumpstart-curated-hubs-admin-guide-create.md). To learn how to add publicly available JumpStart models to your private hub, see [Add models to a private hub](jumpstart-curated-hubs-admin-guide-add-models.md).
**Note**
The JumpStart model you choose should be fine-tunable. You can verify whether a model is fine-tunable by checking the [ Built-in Algorithms with Pre-trained Models Table](https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html).
1. Have a training dataset that you want to use for fine-tuning the model. The dataset should be in the appropriate training format for the model that you want to fine-tune.
## Fine-tune a curated hub model reference
The following procedure shows you how to fine-tune a model reference in your private curated hub using the SageMaker Python SDK.
1. Make sure that you have the latest version (at least `2.242.0`) of the SageMaker Python SDK installed. For more information, see [ Use Version 2.x of the SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/v2.html).
```
!pip install --upgrade sagemaker
```
1. Import the AWS SDK for Python (Boto3) and the modules you'll need from the SageMaker Python SDK.
```
import boto3
from sagemaker.jumpstart.estimator import JumpStartEstimator
from sagemaker.session import Session
```
1. Initialize a Boto3 session, a SageMaker AI client, and a SageMaker Python SDK session.
```
sagemaker_client = boto3.Session(region_name=).client("sagemaker")
sm_session = Session(sagemaker_client=sagemaker_client)
```
1. Create a `JumpStartEstimator` and provide the JumpStart model ID, the name of your hub that contains the model reference, and your SageMaker Python SDK session. For a list of model IDs, see the [ Built-in Algorithms with Pre-trained Models Table](https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html).
Optionally, you can specify the `instance_type` and `instance_count` fields when creating the estimator. If you don't, the training job uses the default instance type and count for the model you're using.
You can also optionally specify the `output_path` to the Amazon S3 location where you want to store the fine-tuned model weights. If you don't specify the `output_path`, then uses a default SageMaker AI Amazon S3 bucket for the region in your account, named with the following format: `sagemaker--`.
```
estimator = JumpStartEstimator(
model_id="meta-textgeneration-llama-3-2-1b",
hub_name=,
sagemaker_session=sm_session, # If you don't specify an existing session, a default one is created for you
# Optional: specify your desired instance type and count for the training job
# instance_type = "ml.g5.2xlarge"
# instance_count = 1
# Optional: specify a custom S3 location to store the fine-tuned model artifacts
# output_path: "s3://"
)
```
1. Create a dictionary with the `training` key where you specify the location of your fine-tuning dataset. This example points to an Amazon S3 URI. If you have additional considerations, such as using local mode or multiple training data channels, see [ JumpStartEstimator.fit()](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.jumpstart.estimator.JumpStartEstimator.fit) in the SageMaker Python SDK documentation for more information.
```
training_input = {
"training": "s3://"
}
```
1. Call the estimator's `fit()` method and pass in your training data and your EULA acceptance (if applicable).
**Note**
The following example sets `accept_eula=False.` You should manually change the value to `True` in order to accept the EULA.
```
estimator.fit(inputs=training_input, accept_eula=False)
```
Your fine-tuning job should now begin.
You can check on your fine-tuning job by viewing your training jobs, either in the SageMaker AI console or by using the [ListTrainingJobs](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ListTrainingJobs.html) API.
You can access your fine-tuned model artifacts at the Amazon S3 `output_path` that was specified in the `JumpStartEstimator` object (either the default SageMaker AI Amazon S3 bucket for the region, or a custom Amazon S3 path you specified, if applicable).