diff --git a/doc/source/custom_directives.py b/doc/source/custom_directives.py
index 6e81d401c833e..2683160332d78 100644
--- a/doc/source/custom_directives.py
+++ b/doc/source/custom_directives.py
@@ -481,6 +481,7 @@ def key(cls: type) -> str:
 class Framework(ExampleEnum):
     """Framework type for example metadata."""
 
+    AWSNEURON = "AWS Neuron"
     PYTORCH = "PyTorch"
     LIGHTNING = "Lightning"
     TRANSFORMERS = "Transformers"
diff --git a/doc/source/train/examples.yml b/doc/source/train/examples.yml
index 8b4f1c7cf8f24..0e7f6725e1005 100644
--- a/doc/source/train/examples.yml
+++ b/doc/source/train/examples.yml
@@ -119,7 +119,17 @@ examples:
     contributor: community
     link: examples/intel_gaudi/llama_pretrain
 
-  - title: Fine-tune a Llama-2 text generation models with DeepSpeed and Hugging Face Accelerate
+  - title: Fine-tune Llama3.1 with AWS Trainium
+    frameworks:
+      - pytorch
+      - aws neuron 
+    skill_level: advanced
+    use_cases:
+      - natural language processing
+      - large language models
+    contributor: community
+    link: examples/aws-trainium/llama3
+  - title: Fine-tune a Llama-2 text generation model with DeepSpeed and Hugging Face Accelerate
     frameworks:
       - accelerate
       - deepspeed
diff --git a/doc/source/train/examples/aws-trainium/llama3.rst b/doc/source/train/examples/aws-trainium/llama3.rst
new file mode 100644
index 0000000000000..ee7b89faf39ee
--- /dev/null
+++ b/doc/source/train/examples/aws-trainium/llama3.rst
@@ -0,0 +1,103 @@
+:orphan:
+
+Distributed fine-tuning of Llama 3.1 8B on AWS Trainium with Ray and PyTorch Lightning
+======================================================================================
+
+
+This example demonstrates how to fine-tune the `Llama 3.1 8B <https://huggingface.co/NousResearch/Meta-Llama-3.1-8B/>`__ model on `AWS
+Trainium <https://aws.amazon.com/ai/machine-learning/trainium/>`__ instances using Ray Train, PyTorch Lightning, and AWS Neuron SDK.
+
+AWS Trainium is the machine learning (ML) chip that AWS built for deep
+learning (DL) training of 100B+ parameter models. `AWS Neuron
+SDK <https://aws.amazon.com/machine-learning/neuron/>`__ helps
+developers train models on Trainium accelerators.
+
+Prepare the environment
+-----------------------
+
+See `Setup EKS cluster and tools <https://github.com/aws-neuron/aws-neuron-eks-samples/tree/master/llama3.1_8B_finetune_ray_ptl_neuron#setupeksclusterandtools>`__ for setting up an Amazon EKS cluster leveraging AWS Trainium instances.
+
+Create a Docker image
+---------------------
+When the EKS cluster is ready, create an Amazon ECR repository for building and uploading the Docker image containing artifacts for fine-tuning a Llama3.1 8B model:
+
+1. Clone the repo.
+
+::
+
+   git clone https://github.com/aws-neuron/aws-neuron-eks-samples.git
+
+2. Go to the ``llama3.1_8B_finetune_ray_ptl_neuron`` directory.
+
+::
+
+   cd aws-neuron-eks-samples/llama3.1_8B_finetune_ray_ptl_neuron
+
+3. Trigger the script.
+
+::
+
+   chmod +x 0-kuberay-trn1-llama3-finetune-build-image.sh 
+   ./0-kuberay-trn1-llama3-finetune-build-image.sh
+
+4. Enter the zone your cluster is running in, for example: us-east-2.
+
+5. Verify in the AWS console that the Amazon ECR service has the newly
+   created ``kuberay_trn1_llama3.1_pytorch2`` repository.
+
+6. Update the ECR image ARN in the manifest file used for creating the Ray cluster.
+
+Replace the <AWS_ACCOUNT_ID> and <REGION> placeholders with actual values in the ``1-llama3-finetune-trn1-create-raycluster.yaml`` file using commands below to reflect the ECR image ARN created above:
+
+::
+
+   export AWS_ACCOUNT_ID=<enter_your_aws_account_id> # for ex: 111222333444
+   export REGION=<enter_your_aws_region> # for ex: us-east-2
+   sed -i "s/<AWS_ACCOUNT_ID>/$AWS_ACCOUNT_ID/g" 1-llama3-finetune-trn1-create-raycluster.yaml
+   sed -i "s/<REGION>/$REGION/g" 1-llama3-finetune-trn1-create-raycluster.yaml
+
+Configuring Ray Cluster
+-----------------------
+
+The ``llama3.1_8B_finetune_ray_ptl_neuron`` directory in the AWS Neuron samples repository simplifies the
+Ray configuration. KubeRay provides a manifest that you can apply
+to the cluster to set up the head and worker pods. 
+
+Run the following command to set up the Ray cluster:
+
+::
+
+   kubectl apply -f 1-llama3-finetune-trn1-create-raycluster.yaml
+
+
+Accessing Ray Dashboard
+-----------------------
+Port forward from the cluster to see the state of the Ray dashboard and
+then view it on `http://localhost:8265 <http://localhost:8265/>`__.
+Run it in the background with the following command:
+
+::
+
+   kubectl port-forward service/kuberay-trn1-head-svc 8265:8265 &
+
+Launching Ray Jobs
+------------------
+
+The Ray cluster now ready to handle workloads. Initiate the data preparation and fine-tuning Ray jobs:
+
+1. Launch the Ray job for downloading the dolly-15k dataset and the Llama3.1 8B model artifacts:
+
+::
+
+   kubectl apply -f 2-llama3-finetune-trn1-rayjob-create-data.yaml
+
+2. When the job has executed successfully, run the following fine-tuning job:
+
+::
+
+   kubectl apply -f 3-llama3-finetune-trn1-rayjob-submit-finetuning-job.yaml
+
+3. Monitor the jobs via the Ray Dashboard
+
+
+For detailed information on each of the steps above, see the `AWS documentation link <https://github.com/aws-neuron/aws-neuron-eks-samples/blob/master/llama3.1_8B_finetune_ray_ptl_neuron/README.md/>`__.
\ No newline at end of file