# Fine-tune a large language model (LLM) using domain adaptation
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Domain adaptation fine-tuning allows you to leverage pre-trained foundation models and adapt them to specific tasks using limited domain-specific data. If prompt engineering efforts do not provide enough customization, you can use domain adaption fine-tuning to get your model working with domain-specific language, such as industry jargon, technical terms, or other specialized data. This fine-tuning process modifies the weights of the model. 

To fine-tune your model on a domain-specific dataset:

1. Prepare your training data. For instructions, see [Prepare and upload training data for domain adaptation fine-tuning](#jumpstart-foundation-models-fine-tuning-domain-adaptation-prepare-data).

1. Create your fine-tuning training job. For instructions, see [Create a training job for instruction-based fine-tuning](#jumpstart-foundation-models-fine-tuning-domain-adaptation-train).

You can find end-to-end examples in [Example notebooks](#jumpstart-foundation-models-fine-tuning-domain-adaptation-examples).

Domain adaptation fine-tuning is available with the following foundation models:

**Note**  
Some JumpStart foundation models, such as Llama 2 7B, require acceptance of an end-user license agreement before fine-tuning and performing inference. For more information, see [End-user license agreements](jumpstart-foundation-models-choose.md#jumpstart-foundation-models-choose-eula).
+ Bloom 3B
+ Bloom 7B1
+ BloomZ 3B FP16
+ BloomZ 7B1 FP16
+ GPT-2 XL
+ GPT-J 6B
+ GPT-Neo 1.3B
+ GPT-Neo 125M
+ GPT-NEO 2.7B
+ Llama 2 13B
+ Llama 2 13B Chat
+ Llama 2 13B Neuron
+ Llama 2 70B
+ Llama 2 70B Chat
+ Llama 2 7B
+ Llama 2 7B Chat
+ Llama 2 7B Neuron

## Prepare and upload training data for domain adaptation fine-tuning
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Training data for domain adaptation fine-tuning can be provided in CSV, JSON, or TXT file format. All training data must be in a single file within a single folder.

The training data is taken from the **Text** column for CSV or JSON training data files. If no column is labeled **Text**, then the training data is taken from the first column for CSV or JSON training data files.

The following is an example body of a TXT file to be used for fine-tuning:

```
This report includes estimates, projections, statements relating to our
business plans, objectives, and expected operating results that are “forward-
looking statements” within the meaning of the Private Securities Litigation
Reform Act of 1995, Section 27A of the Securities Act of 1933, and Section 21E
of ....
```

### Split data for training and testing
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You can optionally provide another folder containing validation data. This folder should also include one CSV, JSON, or TXT file. If no validation dataset is provided, then a set amount of the training data is set aside for validation purposes. You can adjust the percentage of training data used for validation when you choose the hyperparameters for fine-tuning your model. 

### Upload fine-tuning data to Amazon S3
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Upload your prepared data to Amazon Simple Storage Service (Amazon S3) to use when fine-tuning a JumpStart foundation model. You can use the following commands to upload your data:

```
from sagemaker.s3 import S3Uploader
import sagemaker
import random

output_bucket = sagemaker.Session().default_bucket()
local_data_file = "train.txt"
train_data_location = f"s3://{output_bucket}/training_folder"
S3Uploader.upload(local_data_file, train_data_location)
S3Uploader.upload("template.json", train_data_location)
print(f"Training data: {train_data_location}")
```

## Create a training job for instruction-based fine-tuning
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After your data is uploaded to Amazon S3, you can fine-tune and deploy your JumpStart foundation model. To fine-tune your model in Studio, see [Fine-tune a model in Studio](jumpstart-foundation-models-use-studio-updated-fine-tune.md). To fine-tune your model using the SageMaker Python SDK, see [Fine-tune publicly available foundation models with the `JumpStartEstimator` class](jumpstart-foundation-models-use-python-sdk-estimator-class.md).

## Example notebooks
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For more information on domain adaptation fine-tuning, see the following example notebooks:
+ [SageMaker JumpStart Foundation Models - Fine-tuning text generation GPT-J 6B model on domain specific dataset](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/domain-adaption-finetuning-gpt-j-6b.html)
+ [Fine-tune LLaMA 2 models on JumpStart](https://sagemaker-examples.readthedocs.io/en/latest/introduction_to_amazon_algorithms/jumpstart-foundation-models/llama-2-finetuning.html)