- Kubeflow Training Operator is a unified interface for model training on Kubernetes. + Kubeflow Training Operator is a unified interface for model training and fine-tuning on Kubernetes. It runs scalable and distributed training jobs for popular frameworks including PyTorch, TensorFlow, MPI, MXNet, PaddlePaddle, and XGBoost.
+
+- Once user executes `train` API, Training Operator creates PyTorchJob with appropriate resources
+ to fine-tune LLM.
+
+- Storage initializer InitContainer is added to the PyTorchJob worker 0 to download
+ pre-trained model and dataset with provided parameters.
+
+- PVC with [`ReadOnlyMany` access mode](https://kubernetes.io/docs/concepts/storage/persistent-volumes/#access-modes)
+ it attached to each PyTorchJob worker to distribute model and dataset across Pods. **Note**: Your
+ Kubernetes cluster must support volumes with `ReadOnlyMany` access mode, otherwise you can use a
+ single PyTorchJob worker.
+
+- Every PyTorchJob worker runs LLM Trainer that fine-tunes model using provided parameters.
+
+Training Operator implements `train` API with these pre-created components:
+
+### Model Provider
+
+Model provider downloads pre-trained model. Currently, Training Operator supports
+[HuggingFace model provider](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/sdk/python/kubeflow/storage_initializer/hugging_face.py#L56)
+that downloads model from HuggingFace Hub.
+
+You can implement your own model provider by using [this abstract base class](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/sdk/python/kubeflow/storage_initializer/abstract_model_provider.py#L4)
+
+### Dataset Provider
+
+Dataset provider downloads dataset. Currently, Training Operator supports
+[AWS S3](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/sdk/python/kubeflow/storage_initializer/s3.py#L37)
+and [HuggingFace](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/sdk/python/kubeflow/storage_initializer/hugging_face.py#L92)
+dataset providers.
+
+You can implement your own dataset provider by using [this abstract base class](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/sdk/python/kubeflow/storage_initializer/abstract_dataset_provider.py)
+
+### LLM Trainer
+
+Trainer implements training loop to fine-tune LLM. Currently, Training Operator supports
+[HuggingFace trainer](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/sdk/python/kubeflow/trainer/hf_llm_training.py#L118-L139)
+to fine-tune LLMs.
+
+You can implement your own trainer for other ML use-cases such as image classification,
+voice recognition, etc.
diff --git a/content/en/docs/components/training/user-guides/fine-tuning.md b/content/en/docs/components/training/user-guides/fine-tuning.md
new file mode 100644
index 0000000000..26e08b057a
--- /dev/null
+++ b/content/en/docs/components/training/user-guides/fine-tuning.md
@@ -0,0 +1,97 @@
++++
+title = "How to Fine-Tune LLMs with Kubeflow"
+description = "Overview of LLM fine-tuning API in Training Operator"
+weight = 10
++++
+
+{{% alert title="Warning" color="warning" %}}
+This feature is in **alpha** stage and Kubeflow community is looking for your feedback. Please
+share your experience using [#kubeflow-training-operator Slack channel](https://kubeflow.slack.com/archives/C985VJN9F)
+or [Kubeflow Training Operator GitHib](https://github.com/kubeflow/training-operator/issues/new).
+{{% /alert %}}
+
+This page describes how to use a [`train` API from Training Python SDK](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/sdk/python/kubeflow/training/api/training_client.py#L112) that simplifies the ability to fine-tune LLMs with
+distributed PyTorchJob workers.
+
+If you want to learn more about how the fine-tuning API fit in the Kubeflow ecosystem, head to
+[explanation guide](/docs/components/training/explanation/fine-tuning).
+
+## Prerequisites
+
+You need to install Training Python SDK [with fine-tuning support](/docs/components/training/installation/#install-python-sdk-with-fine-tuning-capabilities)
+to run this API.
+
+## How to use Fine-Tuning API ?
+
+You need to provide the following parameters to use the `train` API:
+
+- Pre-trained model parameters.
+- Dataset parameters.
+- Trainer parameters.
+- Number of PyTorch workers and resources per workers.
+
+For example, you can use `train` API as follows to fine-tune BERT model using Yelp Review dataset
+from HuggingFace Hub:
+
+```python
+import transformers
+from peft import LoraConfig
+
+from kubeflow.training import TrainingClient
+from kubeflow.storage_initializer.hugging_face import (
+ HuggingFaceModelParams,
+ HuggingFaceTrainerParams,
+ HuggingFaceDatasetParams,
+)
+
+TrainingClient().train(
+ name="fine-tune-bert",
+ # BERT model URI and type of Transformer to train it.
+ model_provider_parameters=HuggingFaceModelParams(
+ model_uri="hf://google-bert/bert-base-cased",
+ transformer_type=transformers.AutoModelForSequenceClassification,
+ ),
+ # Use 3000 samples from Yelp dataset.
+ dataset_provider_parameters=HuggingFaceDatasetParams(
+ repo_id="yelp_review_full",
+ split="train[:3000]",
+ ),
+ # Specify HuggingFace Trainer parameters. In this example, we will skip evaluation and model checkpoints.
+ trainer_parameters=HuggingFaceTrainerParams(
+ training_parameters=transformers.TrainingArguments(
+ output_dir="test_trainer",
+ save_strategy="no",
+ evaluation_strategy="no",
+ do_eval=False,
+ disable_tqdm=True,
+ log_level="info",
+ ),
+ # Set LoRA config to reduce number of trainable model parameters.
+ lora_config=LoraConfig(
+ r=8,
+ lora_alpha=8,
+ lora_dropout=0.1,
+ bias="none",
+ ),
+ ),
+ num_workers=4, # nnodes parameter for torchrun command.
+ num_procs_per_worker=2, # nproc-per-node parameter for torchrun command.
+ resources_per_worker={
+ "gpu": 2,
+ "cpu": 5,
+ "memory": "10G",
+ },
+)
+```
+
+After you execute `train`, Training Operator will orchestrate appropriate PyTorchJob resources
+to fine-tune LLM.
+
+## Next Steps
+
+- Run example to [fine-tune TinyLlama LLM](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/examples/pytorch/language-modeling/train_api_hf_dataset.ipynb)
+
+- Check this example to compare `create_job` and `train` Python API for
+ [fine-tuning BERT LLM](https://github.com/kubeflow/training-operator/blob/6ce4d57d699a76c3d043917bd0902c931f14080f/examples/pytorch/text-classification/Fine-Tune-BERT-LLM.ipynb).
+
+- Understand [the architecture behind `train` API](/docs/components/training/reference/fine-tuning).