# Fine-tune publicly available foundation models with the `JumpStartEstimator` class
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**Note**  
For instructions on fine-tuning foundation models in a private curated hub, see [Fine-tune curated hub models](jumpstart-curated-hubs-fine-tune.md).

You can fine-tune a built-in algorithm or pre-trained model in just a few lines of code using the SageMaker Python SDK.

1. First, find the model ID for the model of your choice in the [Built-in Algorithms with pre-trained Model Table](https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html). 

1. Using the model ID, define your training job as a JumpStart estimator.

   ```
   from sagemaker.jumpstart.estimator import JumpStartEstimator
   
   model_id = "huggingface-textgeneration1-gpt-j-6b"
   estimator = JumpStartEstimator(model_id=model_id)
   ```

1. Run `estimator.fit()` on your model, pointing to the training data to use for fine-tuning.

   ```
   estimator.fit(
       {"train": training_dataset_s3_path, "validation": validation_dataset_s3_path}
   )
   ```

1. Then, use the `deploy` method to automatically deploy your model for inference. In this example, we use the GPT-J 6B model from Hugging Face.

   ```
   predictor = estimator.deploy()
   ```

1. You can then run inference with the deployed model using the `predict` method.

   ```
   question = "What is Southern California often abbreviated as?"
   response = predictor.predict(question)
   print(response)
   ```

**Note**  
This example uses the foundation model GPT-J 6B, which is suitable for a wide range of text generation use cases including question answering, named entity recognition, summarization, and more. For more information about model use cases, see [Available foundation models](jumpstart-foundation-models-latest.md).

You can optionally specify model versions or instance types when creating your `JumpStartEstimator`. For more information about the `JumpStartEstimator `class and its parameters, see [JumpStartEstimator](https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.jumpstart.estimator.JumpStartEstimator).

## Check default instance types
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You can optionally include specific model versions or instance types when fine-tuning a pre-trained model using the `JumpStartEstimator` class. All JumpStart models have a default instance type. Retrieve the default training instance type using the following code:

```
from sagemaker import instance_types

instance_type = instance_types.retrieve_default(
    model_id=model_id,
    model_version=model_version,
    scope="training")
print(instance_type)
```

You can see all supported instance types for a given JumpStart model with the `instance_types.retrieve()` method.

## Check default hyperparameters
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To check the default hyperparameters used for training, you can use the `retrieve_default()` method from the `hyperparameters` class.

```
from sagemaker import hyperparameters

my_hyperparameters = hyperparameters.retrieve_default(model_id=model_id, model_version=model_version)
print(my_hyperparameters)

# Optionally override default hyperparameters for fine-tuning
my_hyperparameters["epoch"] = "3"
my_hyperparameters["per_device_train_batch_size"] = "4"

# Optionally validate hyperparameters for the model
hyperparameters.validate(model_id=model_id, model_version=model_version, hyperparameters=my_hyperparameters)
```

For more information on available hyperparameters, see [Commonly supported fine-tuning hyperparameters](jumpstart-foundation-models-fine-tuning.md#jumpstart-foundation-models-fine-tuning-hyperparameters).

## Check default metric definitions
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You can also check the default metric definitions:

```
print(metric_definitions.retrieve_default(model_id=model_id, model_version=model_version))
```