# Model sources and license agreements
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Amazon SageMaker JumpStart provides access to hundreds of publicly available and proprietary foundation models from third-party sources and partners. You can explore the JumpStart foundation model selection directly in the SageMaker AI console, Studio, or Studio Classic. 

## Licenses and model sources
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Amazon SageMaker JumpStart provides access to both publicly available and proprietary foundation models. Foundation models are onboarded and maintained from third-party open source and proprietary providers. As such, they are released under different licenses as designated by the model source. Be sure to review the license for any foundation model that you use. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using the content. Some examples of common foundation model licenses include:
+ Alexa Teacher Model
+ Apache 2.0
+ BigScience Responsible AI License v1.0
+ CreativeML Open RAIL\$1\$1-M license

Similarly, for any proprietary foundation models, be sure to review and comply with any terms of use and usage guidelines from the model provider. If you have questions about license information for a specific proprietary model, reach out to model provider directly. You can find model provider contact information in the **Support** tab of each model page in AWS Marketplace.

## End-user license agreements
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Some JumpStart foundation models require explicit acceptance of an end-user license agreement (EULA) before use. 

### EULA acceptance in Amazon SageMaker Studio
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You may be prompted to accept an end-user license agreement before fine-tuning, deploying, or evaluating a JumpStart foundation model in Studio. To get started with JumpStart foundation models in Studio, see [Use foundation models in Studio](jumpstart-foundation-models-use-studio-updated.md). 

**Important**  
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the updated Studio experience. For information about using the Studio Classic application, see [Amazon SageMaker Studio Classic](studio.md).

Some JumpStart foundation models require acceptance of an end-user license agreement before deployment. If this applies to the foundation model that you choose to use, Studio prompts you with a window containing the EULA content. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

#### EULA acceptance in Amazon SageMaker Studio Classic
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You may be prompted to accept an end-user license agreement before deploying a JumpStart foundation model or opening a JumpStart foundation model notebook in Studio Classic. To get started with JumpStart foundation models in Studio Classic, see [Use foundation models in Amazon SageMaker Studio Classic](jumpstart-foundation-models-use-studio.md).

**Important**  
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see [Amazon SageMaker Studio](studio-updated.md).  
Studio Classic is still maintained for existing workloads but is no longer available for onboarding. You can only stop or delete existing Studio Classic applications and cannot create new ones. We recommend that you [migrate your workload to the new Studio experience](studio-updated-migrate.md).

Some JumpStart foundation models require acceptance of an end-user license agreement before deployment. If this applies to the foundation model that you choose to use, Studio Classic prompts you with a window titled **Review the End User License Agreement (EULA) and Acceptable Use Policy (AUP) below** after you choose either **Deploy** or **Open notebook**. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

### EULA acceptance with the SageMaker Python SDK
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The following sections show you how to explicitly declare EULA acceptance when deploying or fine-tuning a JumpStart model with the SageMaker Python SDK. For more information on getting started with JumpStart foundation models using the SageMaker Python SDK, see [Use foundation models with the SageMaker Python SDK](jumpstart-foundation-models-use-python-sdk.md).

Before you begin, make sure that you do the following:
+ Upgrade to the latest version of the model that you use. 
+ Install the latest version of the SageMaker Python SDK.

**Important**  
To use the following workflow you must have [v2.198.0](https://github.com/aws/sagemaker-python-sdk/releases/tag/v2.198.0) or later of the SageMaker Python SDK installed.

#### EULA acceptance when deploying a JumpStart model
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For models that require the acceptance of an end-user license agreement, you must explicitly declare EULA acceptance when deploying your JumpStart model.

```
from sagemaker.jumpstart.model import JumpStartModel
model_id = "meta-textgeneration-llama-2-13b"
my_model = JumpStartModel(model_id=model_id)

# Declare EULA acceptance when deploying your JumpStart model
predictor = my_model.deploy(accept_eula=True)
```

The `accept_eula` value is `None` by default and must be explicitly redefined as `True` in order to accept the end-user license agreement. For more information, see [JumpStartModel](https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.jumpstart.model.JumpStartModel).

#### EULA acceptance when fine-tuning a JumpStart model
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For fine-tuning models that require the acceptance of an end-user license agreement, you must explicitly declare EULA acceptance when running the `fit()` method for your JumpStart estimator. After fine-tuning a pre-trained model, the weights of the original model are changed. Therefore, when you deploy the fine-tuned model later, you do not need to accept a EULA.

**Note**  
The following example sets `accept_eula=False`. You should manually change the value to `True` in order to accept the EULA.

```
from sagemaker.jumpstart.estimator import JumpStartEstimator
model_id = "meta-textgeneration-llama-2-13b"

# Declare EULA acceptance when defining your JumpStart estimator
estimator = JumpStartEstimator(model_id=model_id)
estimator.fit(accept_eula=False,
{"train": training_dataset_s3_path, "validation": validation_dataset_s3_path}
)
```

The `accept_eula` value is `None` by default and must be explicitly redefined as `"true"` within the `fit()` method in order to accept the end-user license agreement. For more information, see [JumpStartEstimator](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html#sagemaker.jumpstart.estimator.JumpStartEstimator).

#### EULA acceptance SageMaker Python SDK versions earlier than 2.198.0
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**Important**  
When using versions earlier than [2.198.0](https://github.com/aws/sagemaker-python-sdk/releases/tag/v2.198.0) of the SageMaker Python SDK, you must use the SageMaker `Predictor` class to accept a model EULA. 

After deploying a JumpStart foundation model programmatically using the SageMaker Python SDK, you can run inference against your deployed endpoint with the SageMaker `[Predictor](https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html)` class. For models that require the acceptance of an end-user license agreement, you must explicitly declare EULA acceptance in your call to the `Predictor` class: 

```
predictor.predict(payload, custom_attributes="accept_eula=true")
```

The `accept_eula` value is `false` by default and must be explicitly redefined as `true` in order to accept the end-user license agreement. The predictor returns an error if you try to run inference while `accept_eula` is set to `false`. For more information on getting started with JumpStart foundation models using the SageMaker Python SDK, see [Use foundation models with the SageMaker Python SDK](jumpstart-foundation-models-use-python-sdk.md).

**Important**  
The `custom_attributes` parameter accepts key-value pairs in the format `"key1=value1;key2=value2"`. If you use the same key multiple times, the inference server uses the last value associated with the key. For example, if you pass `"accept_eula=false;accept_eula=true"` to the `custom_attributes` parameter, then the inference server associates the value `true` with the `accept_eula` key.