Repository: kaito-project/kaito

Issue:
Onboard oci artifacts for model file distribution
**Is your feature request related to a problem? Please describe.**

https://github.com/kaito-project/kaito/blob/main/docs/proposals/20250609-model-as-oci-artifacts.md

**Describe the solution you'd like**

- [x] Rebuild model image in oci artifacts form #1173 
- [x] Rebuild base image with zstd compression #1173 
- [x] Add initContainer to pull model artifacts #1188 
- [x] Adapt preset test #1186


**Describe alternatives you've considered**

**Additional context**

Hints from issue comments:
@zhuangqh: ![Image](https://github.com/user-attachments/assets/75fde2e8-d005-48f2-b426-288f6c5db0db)

Evaluation

Solution patch:
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
index 4239464..e168d73 100644
--- a/.github/ISSUE_TEMPLATE/feature_request.md
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -1,6 +1,6 @@
 ---
 name: Feature request
-about: Suggest a new feature for Kaito
+about: Suggest a new feature for KAITO
 title: ''
 labels: 'enhancement'
 assignees: ''
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 272b76d..384433d 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,5 +1,5 @@
 **Reason for Change**:
-<!-- What does this PR improve or fix in Kaito? Why is it needed? -->
+<!-- What does this PR improve or fix in KAITO? Why is it needed? -->
 
 **Requirements**
 
diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
new file mode 100644
index 0000000..30a68e1
--- /dev/null
+++ b/.github/workflows/deploy-docs.yml
@@ -0,0 +1,68 @@
+name: deploy-docs
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - '.github/workflows/deploy-docs.yaml'
+      - 'website/**'
+  pull_request:
+    branches:
+      - main
+    paths:
+      - '.github/workflows/deploy-docs.yaml'
+      - 'website/**'
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+    name: Generate docs website to GitHub Pages
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    defaults:
+      run:
+        working-directory: website
+    steps:
+      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
+
+      - name: Harden Runner
+        uses: step-security/harden-runner@4d991eb9b905ef189e4c376166672c3f2f230481
+        with:
+          disable-sudo: true
+          egress-policy: block
+          allowed-endpoints: >
+            github.com:443
+            registry.yarnpkg.com:443
+            *.githubusercontent.com:443
+            *.blob.core.windows.net:443
+
+      - name: Setup Node
+        uses: actions/setup-node@cdca7365b2dadb8aad0a33bc7601856ffabcc48e # v4.3.0
+        with:
+          node-version: 20.x
+
+      - name: Get yarn cache
+        id: yarn-cache
+        run: echo "dir=$(yarn cache dir)" > $GITHUB_OUTPUT
+
+      - name: Cache dependencies
+        uses: actions/cache@d4323d4df104b026a6aa633fdb11d772146be0bf # v4.2.2
+        with:
+          path: ${{ steps.yarn-cache.outputs.dir }}
+          key: ${{ runner.os }}-website-${{ hashFiles('**/yarn.lock') }}
+          restore-keys: |
+            ${{ runner.os }}-website-
+
+      - run: yarn install --frozen-lockfile
+      - run: yarn build
+
+      - name: Deploy to GitHub Pages
+        uses: peaceiris/actions-gh-pages@4f9cc6602d3f66b9c108549d475ec49e8ef4d45e # v4.0.0
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./website/build
+          destination_dir: ./docs
\ No newline at end of file
diff --git a/README.md b/README.md
index 36cb8bf..6048852 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Kubernetes AI Toolchain Operator (Kaito)
+# Kubernetes AI Toolchain Operator (KAITO)
 
 ![GitHub Release](https://img.shields.io/github/v/release/kaito-project/kaito)
 [![Go Report Card](https://goreportcard.com/badge/github.com/kaito-project/kaito)](https://goreportcard.com/report/github.com/kaito-project/kaito)
@@ -7,13 +7,13 @@
 
 | ![notification](docs/img/bell.svg) What is NEW!                                                                                                                                                                                                |
 | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Coming soon:** Kaito v0.5.0. Retrieval-augmented generation (RAG) - RagEngine support with LlamaIndex orchestration and Faiss as the default vectorDB, learn about recent updates [here](https://github.com/kaito-project/kaito/issues/734)! |
-| Latest Release: May 14th, 2025. Kaito v0.4.6.                                                                                                                                                                                                  |
-| First Release: Nov 15th, 2023. Kaito v0.1.0.                                                                                                                                                                                                   |
+| **Coming soon:** KAITO v0.5.0. Retrieval-augmented generation (RAG) - RagEngine support with LlamaIndex orchestration and Faiss as the default vectorDB, learn about recent updates [here](https://github.com/kaito-project/kaito/issues/734)! |
+| Latest Release: May 14th, 2025. KAITO v0.4.6.                                                                                                                                                                                                  |
+| First Release: Nov 15th, 2023. KAITO v0.1.0.                                                                                                                                                                                                   |
 
-Kaito is an operator that automates the AI/ML model inference or tuning workload in a Kubernetes cluster.
+KAITO is an operator that automates the AI/ML model inference or tuning workload in a Kubernetes cluster.
 The target models are popular open-sourced large models such as [falcon](https://huggingface.co/tiiuae) and [phi-3](https://huggingface.co/docs/transformers/main/en/model_doc/phi3).
-Kaito has the following key differentiations compared to most of the mainstream model deployment methodologies built on top of virtual machine infrastructures:
+KAITO has the following key differentiations compared to most of the mainstream model deployment methodologies built on top of virtual machine infrastructures:
 
 - Manage large model files using container images. An OpenAI-compatible server is provided to perform inference calls.
 - Provide preset configurations to avoid adjusting workload parameters based on GPU hardware.
@@ -21,28 +21,28 @@ Kaito has the following key differentiations compared to most of the mainstream
 - Auto-provision GPU nodes based on model requirements.
 - Host large model images in the public Microsoft Container Registry (MCR) if the license allows.
 
-Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
+Using KAITO, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
 
 ## Architecture
 
-Kaito follows the classic Kubernetes Custom Resource Definition(CRD)/controller design pattern. User manages a `workspace` custom resource which describes the GPU requirements and the inference or tuning specification. Kaito controllers will automate the deployment by reconciling the `workspace` custom resource.
+KAITO follows the classic Kubernetes Custom Resource Definition(CRD)/controller design pattern. User manages a `workspace` custom resource which describes the GPU requirements and the inference or tuning specification. KAITO controllers will automate the deployment by reconciling the `workspace` custom resource.
 <div align="left">
-  <img src="docs/img/arch.png" width=80% title="Kaito architecture" alt="Kaito architecture">
+  <img src="docs/img/arch.png" width=80% title="KAITO architecture" alt="KAITO architecture">
 </div>
 
-The above figure presents the Kaito architecture overview. Its major components consist of:
+The above figure presents the KAITO architecture overview. Its major components consist of:
 
-- **Workspace controller**: It reconciles the `workspace` custom resource, creates `machine` (explained below) custom resources to trigger node auto provisioning, and creates the inference or tuning workload (`deployment`, `statefulset` or `job`) based on the model preset configurations.
-- **Node provisioner controller**: The controller's name is *gpu-provisioner* in [gpu-provisioner helm chart](https://github.com/Azure/gpu-provisioner/tree/main/charts/gpu-provisioner). It uses the `machine` CRD originated from [Karpenter](https://sigs.k8s.io/karpenter) to interact with the workspace controller. It integrates with Azure Resource Manager REST APIs to add new GPU nodes to the AKS or AKS Arc cluster.
+- **Workspace controller**: It reconciles the `workspace` custom resource, creates `NodeClaim` (explained below) custom resources to trigger node auto provisioning, and creates the inference or tuning workload (`deployment`, `statefulset` or `job`) based on the model preset configurations.
+- **Node provisioner controller**: The controller's name is *gpu-provisioner* in [gpu-provisioner helm chart](https://github.com/Azure/gpu-provisioner/tree/main/charts/gpu-provisioner). It uses the `NodeClaim` CRD originated from [Karpenter](https://sigs.k8s.io/karpenter) to interact with the workspace controller. It integrates with Azure Resource Manager REST APIs to add new GPU nodes to the AKS or AKS Arc cluster.
 > Note: The [*gpu-provisioner*](https://github.com/Azure/gpu-provisioner) is an open sourced component. It can be replaced by other controllers if they support [Karpenter-core](https://sigs.k8s.io/karpenter) APIs.
 
 ## Installation
 
-Please check the installation guidance [here](./docs/installation.md) for deployment using Azure CLI and [here](./terraform/README.md) for deployment using Terraform.
+Please check the installation guidance [here](https://kaito-project.github.io/kaito/docs/installation) for deployment using Azure CLI and [here](./terraform/README.md) for deployment using Terraform.
 
 ## Quick start
 
-After installing Kaito, one can try following commands to start a phi-3.5-mini-instruct inference service.
+After installing KAITO, one can try following commands to start a phi-3.5-mini-instruct inference service.
 
 ```sh
 $ cat examples/inference/kaito_workspace_phi_3.5-instruct.yaml
@@ -109,11 +109,12 @@ $ kubectl run -it --rm --restart=Never curl --image=curlimages/curl -- curl -X P
 
 ## Usage
 
-The detailed usage for Kaito supported models can be found in [**HERE**](presets/README.md). In case users want to deploy their own containerized models, they can provide the pod template in the `inference` field of the workspace custom resource (please see [API definitions](api/v1alpha1/workspace_types.go) for details). The controller will create a deployment workload using all provisioned GPU nodes. Note that currently the controller does **NOT** handle automatic model upgrade. It only creates inference workloads based on the preset configurations if the workloads do not exist.
+The detailed usage for Kaito supported models can be found in [**HERE**](https://kaito-project.github.io/kaito/docs/usage). In case users want to deploy their own containerized models, they can provide the pod template in the `inference` field of the workspace custom resource (please see [API definitions](../../api/v1alpha1/workspace_types.go) for details). The controller will create a deployment workload using all provisioned GPU nodes. Note that currently the controller does **NOT** handle automatic model upgrade. It only creates inference workloads based on the preset configurations if the workloads do not exist.
 
-The number of the supported models in Kaito is growing! Please check [this](./docs/How-to-add-new-models.md) document to see how to add a new supported model.
+The number of the supported models in Kaito is growing! Please check [this](https://kaito-project.github.io/kaito/docs/preset-onboarding) document to see how to add a new supported model.
+
+Starting with version v0.3.0, Kaito supports model fine-tuning and using fine-tuned adapters in the inference service. Refer to the [tuning document](https://kaito-project.github.io/kaito/docs/tuning) and [inference document](https://kaito-project.github.io/kaito/docs/inference) for more information.
 
-Starting with version v0.3.0, Kaito supports model fine-tuning and using fine-tuned adapters in the inference service. Refer to the [tuning document](./docs/tuning/README.md) and [inference document](./docs/inference/README.md) for more information.
 
 ## FAQ
 
@@ -132,9 +133,9 @@ Then the node should have the label: `apps=falcon-7b`.
 
 When using hosted public models, a user can delete the existing inference workload (`Deployment` of `StatefulSet`) manually, and the workspace controller will create a new one with the latest preset configuration (e.g., the image version) defined in the current release. For private models, it is recommended to create a new workspace with a new image version in the Spec.
 
-### How to update model/inference parameters to override the Kaito Preset Configuration?
+### How to update model/inference parameters to override the KAITO Preset Configuration?
 
-Kaito provides a limited capability to override preset configurations for models that use `transformer` runtime manually.
+KAITO provides a limited capability to override preset configurations for models that use `transformer` runtime manually.
 To update parameters for a deployed model, perform `kubectl edit` against the workload, which could be either a `StatefulSet` or `Deployment`.
 For example, to enable 4-bit quantization on a `falcon-7b-instruct` deployment, you would execute:
 
@@ -172,7 +173,7 @@ On the other hand, non-instruct, or raw models, are designed for further fine-tu
 
 ## Contributing
 
-[Read more](docs/contributing/readme.md)
+[Read more](https://kaito-project.github.io/kaito/docs/contributing)
 <!-- markdown-link-check-disable -->
 This project welcomes contributions and suggestions. The contributions require you to agree to a
 Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
@@ -197,6 +198,6 @@ KAITO has adopted the [Cloud Native Compute Foundation Code of Conduct](https://
 <!-- markdown-link-check-enable -->
 ## Contact
 
-"Kaito devs" <kaito-dev@microsoft.com>
+"KAITO devs" <kaito-dev@microsoft.com>
 
-[Kaito Community Slack](https://join.slack.com/t/kaito-z6a6575/shared_invite/zt-2wm17rttz-t4E6_rMIuY03DwBHaJq1sg)
+[KAITO Community Slack](https://join.slack.com/t/kaito-z6a6575/shared_invite/zt-37gh89vw7-odHfqmPRc5oRnDG99SBJNA)
diff --git a/demo/README.md b/demo/README.md
index f76da1a..2277409 100644
--- a/demo/README.md
+++ b/demo/README.md
@@ -1,7 +1,7 @@
-## Kaito Demos Overview
+## KAITO Demos Overview
 
-Welcome to the KAITO demos directory! Here you'll find a collection of demonstration 
-applications designed to showcase various functionalities and 
+Welcome to the KAITO demos directory! Here you'll find a collection of demonstration
+applications designed to showcase various functionalities and
 integrations with the KAITO Workspace. Feel free to explore!
 
 For specific instructions and details, please refer to the README.md file within each demo's directory.
\ No newline at end of file
diff --git a/docs/How-to-add-new-models.md b/docs/How-to-add-new-models.md
deleted file mode 100644
index 887702c..0000000
--- a/docs/How-to-add-new-models.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# New OSS model onboarding
-
-This document describes how to add a new supported OSS model in Kaito. The process is designed to allow community users to initiate the request. Kaito maintainers will follow up and deal with managing the model images and guiding the code changes to set up the model preset configurations.
-
-## Step 1: Make a proposal
-
-This step is done by the requestor. The requestor should make a PR to describe the target OSS model following this [template](./proposals/YYYYMMDD-model-template.md). The proposal status should be `provisional` in the beginning. Kaito maintainers will review the PR and decide to accept or reject the PR. The PR could be rejected if the target OSS model has low usage, or it has strict license limitations, or it is a relatively small model with limited capabilities.
-
-
-## Step 2: Validate and test the model
-
-This step is done by Kaito maintainers. Based on the information provided in the proposal, Kaito maintainers will download the model and test it using the specified runtime. The entire process is automated via GitHub actions when Kaito maintainers file a PR to add the model to the [supported\_models.yaml](../presets/workspace/models/supported_models.yaml).
-
-
-## Step 3: Push model image to MCR
-
-This step is done by Kaito maintainers. If the model license allows, Kaito maintainers will push the model image to MCR, making the image publicly available. This step is skipped if only private access is allowed for the model image. Once this step is done, Kaito maintainers will update the status of the proposal submitted in Step 1 to `ready to integrate`.
-
-## Step 4: Add preset configurations
-
-This step is done by the requestor. The requestor will work on a PR to register the model with preset configurations. The PR will contain code changes to implement a simple inference interface. [Here](../presets/workspace/models/falcon/model.go) is an existing example. In the same PR, or a separate PR, the status of the proposal status should be updated to `integrated`.
-
-## Step 5: Add an E2E test
-
-This step is done by the requestor. A new e2e test should be added to [here](../test/e2e/preset_test.go) which ensures the inference service is up and running with preset configurations.
-
-
-After all the above are done, a new model becomes available in Kaito.
diff --git a/docs/RAG/RAGEngine-installation.md b/docs/RAG/RAGEngine-installation.md
deleted file mode 100644
index f03f87c..0000000
--- a/docs/RAG/RAGEngine-installation.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Kaito RAG Engine Installation 
-
-> Be sure you've cloned this repo and followed [kaito workspace installation](../installation.md)
-```bash
-helm install ragengine ./charts/kaito/ragengine --namespace kaito-ragengine --create-namespace
-```
-
-## Verify installation
-You can run the following commands to verify the installation of the controllers were successful.
-
-Check status of the Helm chart installations.
-
-```bash
-helm list -n kaito-ragengine
-```
-
-Check status of the `ragengine`.
-
-```bash
-kubectl describe deploy ragengine -n kaito-ragengine
-```
-
-## Clean up
-
-```bash
-helm uninstall kaito-ragengine
-```
\ No newline at end of file
diff --git a/docs/controller-metrics/kaito-workspace_controller_metrics.md b/docs/controller-metrics/kaito-workspace_controller_metrics.md
deleted file mode 100644
index 46b6f34..0000000
--- a/docs/controller-metrics/kaito-workspace_controller_metrics.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Prometheus Metrics
-
-Workspace controller exposes Prometheus metrics for monitoring workspace states.
-
-> [!NOTE]
-> In addition to the metrics listed below, the workspace controller also exposes all metrics provided by the Kubernetes controller-runtime. For more information, see the [controller-runtime metrics package](https://github.com/kubernetes-sigs/controller-runtime/tree/main/pkg/metrics).
-
-## Workspace Metrics
-
-The workspace controller exports metrics at the `/metrics` endpoint. These metrics provide insights into the number of workspaces in different phases, helping you monitor the overall status of workspaces in your system.
-
-| Category | Metric Name | Type | Description |
-|----------|-------------|------|-------------|
-| Workspace Stats | `kaito_workspace_count` | Gauge | Number of Workspaces in a certain phase |
-
-### Labels
-
-The `kaito_workspace_count` metric includes the following labels:
-
-| Label | Description |
-|-------|-------------|
-| `phase` | Workspace phase: `succeeded`, `error`, `pending`, or `deleting` |
-
-### Phase Definitions
-
-- `succeeded`: Workspaces that have successfully completed their execution
-- `error`: Workspaces that have encountered errors during execution 
-- `pending`: Workspaces that are in progress or waiting to be processed
-- `deleting`: Workspaces that are in the process of being deleted
diff --git a/docs/img/arch.png b/docs/img/arch.png
deleted file mode 100644
index 0b13a69..0000000
Binary files a/docs/img/arch.png and /dev/null differ
diff --git a/docs/aws/clusterconfig.yaml.template b/examples/aws/clusterconfig.yaml.template
similarity index 97%
rename from docs/aws/clusterconfig.yaml.template
rename to examples/aws/clusterconfig.yaml.template
index 36e5b86..bfed57d 100644
--- a/docs/aws/clusterconfig.yaml.template
+++ b/examples/aws/clusterconfig.yaml.template
@@ -35,4 +35,4 @@ managedNodeGroups:
   maxSize: 10
 
 addons:
-- name: eks-pod-identity-agent
\ No newline at end of file
+- name: eks-pod-identity-agent
diff --git a/docs/custom-model-integration/Dockerfile.reference b/examples/custom-model-integration/Dockerfile.reference
similarity index 100%
rename from docs/custom-model-integration/Dockerfile.reference
rename to examples/custom-model-integration/Dockerfile.reference
diff --git a/docs/custom-model-integration/custom-deployment-template.yaml b/examples/custom-model-integration/custom-deployment-template.yaml
similarity index 100%
rename from docs/custom-model-integration/custom-deployment-template.yaml
rename to examples/custom-model-integration/custom-deployment-template.yaml
diff --git a/docs/custom-model-integration/reference-image-deployment.yaml b/examples/custom-model-integration/reference-image-deployment.yaml
similarity index 100%
rename from docs/custom-model-integration/reference-image-deployment.yaml
rename to examples/custom-model-integration/reference-image-deployment.yaml
diff --git a/pkg/utils/common-preset.go b/pkg/utils/common-preset.go
index 9b76f43..21fb97f 100644
--- a/pkg/utils/common-preset.go
+++ b/pkg/utils/common-preset.go
@@ -4,6 +4,7 @@ package utils
 
 import (
 	"fmt"
+	"os"
 
 	corev1 "k8s.io/api/core/v1"
 )
@@ -13,6 +14,7 @@ const (
 	DefaultConfigMapMountPath = "/mnt/config"
 	DefaultDataVolumePath     = "/mnt/data"
 	DefaultAdapterVolumePath  = "/mnt/adapter"
+	DefaultWeightsVolumePath  = "/workspace/weights"
 )
 
 func ConfigResultsVolume(outputPath string, outputVolume *corev1.VolumeSource) (corev1.Volume, corev1.VolumeMount) {
@@ -182,3 +184,29 @@ func ConfigAdapterVolume() (corev1.Volume, corev1.VolumeMount) {
 	}
 	return volume, volumeMount
 }
+
+func ConfigModelWeightsVolume() (corev1.Volume, corev1.VolumeMount) {
+	var volume corev1.Volume
+	var volumeMount corev1.VolumeMount
+
+	volumeSource := corev1.VolumeSource{
+		EmptyDir: &corev1.EmptyDirVolumeSource{},
+	}
+
+	volume = corev1.Volume{
+		Name:         "model-weights-volume",
+		VolumeSource: volumeSource,
+	}
+
+	volumeMount = corev1.VolumeMount{
+		Name:      "model-weights-volume",
+		MountPath: DefaultWeightsVolumePath,
+	}
+	return volume, volumeMount
+}
+
+func GetPresetImageName(name, tag string) string {
+	return fmt.Sprintf("%s/kaito-%s:%s",
+		os.Getenv("PRESET_REGISTRY_NAME"),
+		name, tag)
+}
diff --git a/pkg/utils/common.go b/pkg/utils/common.go
index add115c..d02a378 100644
--- a/pkg/utils/common.go
+++ b/pkg/utils/common.go
@@ -326,3 +326,18 @@ func GetRayLeaderHost(meta metav1.ObjectMeta) string {
 	return fmt.Sprintf("%s-0.%s-headless.%s.svc.cluster.local",
 		meta.Name, meta.Name, meta.Namespace)
 }
+
+// DedupVolumeMounts removes duplicate volume mounts by only keeping the first occurrence of each name
+func DedupVolumeMounts(mounts []v1.VolumeMount) []v1.VolumeMount {
+	seen := make(map[string]bool)
+	var result []v1.VolumeMount
+
+	for _, mount := range mounts {
+		if !seen[mount.Name] {
+			seen[mount.Name] = true
+			result = append(result, mount)
+		}
+	}
+
+	return result
+}
diff --git a/pkg/workspace/image/README.md b/pkg/workspace/image/README.md
index 8a40f6d..33ed07a 100644
--- a/pkg/workspace/image/README.md
+++ b/pkg/workspace/image/README.md
@@ -1,10 +1,10 @@
-# Kaito Adapter Image Format Specification
+# KAITO Adapter Image Format Specification
 
-This document describes the specification for Kaito-compatible adapter images. It assumes the reader is familiar with the [OCI Image Format Specification](https://github.com/opencontainers/image-spec).
+This document describes the specification for KAITO-compatible adapter images. It assumes the reader is familiar with the [OCI Image Format Specification](https://github.com/opencontainers/image-spec).
 
 ## Image Layout
 
-A Kaito-compatible adapter image consists of any OCI image with the file structure below. Files added to the image but not specified here are simply ignored. The image can use any base and have as many layers as needed.
+A KAITO-compatible adapter image consists of any OCI image with the file structure below. Files added to the image but not specified here are simply ignored. The image can use any base and have as many layers as needed.
 ```
 /
 └── data
@@ -22,7 +22,7 @@ COPY adapter_model.safetensors /data/
 
 ## Building Platform-Independent Images
 
-To build a platform-independent image compatible with Kaito, you can leverage [pusher.sh](pusher.sh). Simply run `./pusher.sh ${PATH_TO_DATA_DIRECTORY} ${IMAGE_REFERENCE}` to build and push a platform-independent image containing the files in the specified directory.
+To build a platform-independent image compatible with KAITO, you can leverage [pusher.sh](pusher.sh). Simply run `./pusher.sh ${PATH_TO_DATA_DIRECTORY} ${IMAGE_REFERENCE}` to build and push a platform-independent image containing the files in the specified directory.
 
 The following example pushes `~/Documents/kaito/adapter/data` to `ghcr.io/kaito-project/kaito/adapter` with tag `1.2.3`:
 
diff --git a/pkg/workspace/inference/preset-inferences.go b/pkg/workspace/inference/preset-inferences.go
index 7aa9071..e91303b 100644
--- a/pkg/workspace/inference/preset-inferences.go
+++ b/pkg/workspace/inference/preset-inferences.go
@@ -5,7 +5,6 @@ package inference
 import (
 	"context"
 	"fmt"
-	"os"
 	"strconv"
 
 	"github.com/samber/lo"
@@ -83,25 +82,7 @@ func GetInferenceImageInfo(ctx context.Context, workspaceObj *v1beta1.Workspace,
 		}
 	}
 
-	// Three possible cases for inference workload image selection:
-	// 1. If the preset is set to download at runtime, use the 'kaito-base' image.
-	// 2. Otherwise, use the preset image, which has the model weights packaged in.
-	var imageName, imageTag string
-	if presetObj.DownloadAtRuntime {
-		// Force the use of kaito-base image if the preset is set to download at runtime.
-		// The kaito-base image is the same as other preset images but without the model
-		// files packaged in.
-		imageName = "base"
-		imageTag = metadata.MustGet(imageName).Tag
-	} else {
-		imageName = string(workspaceObj.Inference.Preset.Name)
-		imageTag = presetObj.Tag
-	}
-
-	registryName := os.Getenv("PRESET_REGISTRY_NAME")
-	imageName = fmt.Sprintf("%s/kaito-%s:%s", registryName, imageName, imageTag)
-
-	return imageName, imagePullSecretRefs
+	return GetBaseImageName(), imagePullSecretRefs
 }
 
 func GeneratePresetInference(ctx context.Context, workspaceObj *v1beta1.Workspace, revisionNum string,
@@ -175,11 +156,22 @@ func GeneratePresetInference(ctx context.Context, workspaceObj *v1beta1.Workspac
 	if shmVolumeMount.Name != "" {
 		volumeMounts = append(volumeMounts, shmVolumeMount)
 	}
+
+	// add adapter volume mount if adapters are enabled
 	if len(workspaceObj.Inference.Adapters) > 0 {
 		adapterVolume, adapterVolumeMount := utils.ConfigAdapterVolume()
 		volumes = append(volumes, adapterVolume)
 		volumeMounts = append(volumeMounts, adapterVolumeMount)
 	}
+
+	// add model weights volume mount if the model is not downloaded at runtime
+	if !inferenceParam.DownloadAtRuntime {
+		modelWeightsVolume, modelWeightsVolumeMount := utils.ConfigModelWeightsVolume()
+		volumes = append(volumes, modelWeightsVolume)
+		volumeMounts = append(volumeMounts, modelWeightsVolumeMount)
+	}
+
+	// additional environment variables
 	if inferenceParam.DownloadAtRuntime {
 		envVars = append(envVars, corev1.EnvVar{
 			Name: "HF_TOKEN",
@@ -194,6 +186,9 @@ func GeneratePresetInference(ctx context.Context, workspaceObj *v1beta1.Workspac
 		})
 	}
 
+	// additional initContainers
+	initContainers := manifests.GenerateModelPullerContainer(ctx, workspaceObj, inferenceParam)
+
 	// inference command
 	runtimeName := v1beta1.GetWorkspaceRuntimeName(workspaceObj)
 	commands := inferenceParam.GetInferenceCommand(pkgmodel.RuntimeContext{
@@ -220,10 +215,10 @@ func GeneratePresetInference(ctx context.Context, workspaceObj *v1beta1.Workspac
 		livenessProbe := getDistributedInferenceProbe(probeTypeLiveness, workspaceObj, 60, 10, 5)
 		readinessProbe := getDistributedInferenceProbe(probeTypeReadiness, workspaceObj, 0, 10, 1)
 		depObj = manifests.GenerateStatefulSetManifest(workspaceObj, revisionNum, image, imagePullSecrets, numNodes, commands,
-			containerPorts, livenessProbe, readinessProbe, resourceReq, tolerations, volumes, volumeMounts, envVars)
+			containerPorts, livenessProbe, readinessProbe, resourceReq, tolerations, volumes, volumeMounts, envVars, initContainers)
 	} else {
 		depObj = manifests.GenerateDeploymentManifest(workspaceObj, revisionNum, image, imagePullSecrets, numNodes, commands,
-			containerPorts, defaultLivenessProbe, defaultReadinessProbe, resourceReq, tolerations, volumes, volumeMounts, envVars)
+			containerPorts, defaultLivenessProbe, defaultReadinessProbe, resourceReq, tolerations, volumes, volumeMounts, envVars, initContainers)
 	}
 	return depObj, nil
 }
@@ -275,3 +270,8 @@ func getDistributedInferenceProbe(probeType probeType, wObj *v1beta1.Workspace,
 
 	return probe
 }
+
+func GetBaseImageName() string {
+	presetObj := metadata.MustGet("base")
+	return utils.GetPresetImageName(presetObj.Name, presetObj.Tag)
+}
diff --git a/pkg/workspace/manifests/manifests.go b/pkg/workspace/manifests/manifests.go
index 79e160a..8719b4e 100644
--- a/pkg/workspace/manifests/manifests.go
+++ b/pkg/workspace/manifests/manifests.go
@@ -4,6 +4,7 @@
 package manifests
 
 import (
+	"context"
 	"fmt"
 	"path"
 
@@ -15,7 +16,9 @@ import (
 	"k8s.io/apimachinery/pkg/util/intstr"
 	"k8s.io/utils/ptr"
 
+	"github.com/kaito-project/kaito/api/v1beta1"
 	kaitov1beta1 "github.com/kaito-project/kaito/api/v1beta1"
+	pkgmodel "github.com/kaito-project/kaito/pkg/model"
 	"github.com/kaito-project/kaito/pkg/utils"
 	"github.com/kaito-project/kaito/pkg/workspace/image"
 )
@@ -95,11 +98,12 @@ func GenerateServiceManifest(workspaceObj *kaitov1beta1.Workspace, serviceType c
 func GenerateStatefulSetManifest(workspaceObj *kaitov1beta1.Workspace, revisionNum string, imageName string,
 	imagePullSecretRefs []corev1.LocalObjectReference, replicas int, commands []string, containerPorts []corev1.ContainerPort,
 	livenessProbe, readinessProbe *corev1.Probe, resourceRequirements corev1.ResourceRequirements,
-	tolerations []corev1.Toleration, volumes []corev1.Volume, volumeMount []corev1.VolumeMount, envVars []corev1.EnvVar) *appsv1.StatefulSet {
+	tolerations []corev1.Toleration, volumes []corev1.Volume, volumeMount []corev1.VolumeMount, envVars []corev1.EnvVar, initContainers []corev1.Container) *appsv1.StatefulSet {
 
 	pullerContainers, pullerEnvVars, pullerVolumes := GeneratePullerContainers(workspaceObj, volumeMount)
 	envVars = append(envVars, pullerEnvVars...)
 	volumes = append(volumes, pullerVolumes...)
+	initContainers = append(initContainers, pullerContainers...)
 
 	nodeRequirements := make([]corev1.NodeSelectorRequirement, 0, len(workspaceObj.Resource.LabelSelector.MatchLabels))
 	for key, value := range workspaceObj.Resource.LabelSelector.MatchLabels {
@@ -157,7 +161,7 @@ func GenerateStatefulSetManifest(workspaceObj *kaitov1beta1.Workspace, revisionN
 							},
 						},
 					},
-					InitContainers: pullerContainers,
+					InitContainers: initContainers,
 					Containers: []corev1.Container{
 						{
 							Name:           workspaceObj.Name,
@@ -193,11 +197,11 @@ func GenerateTuningJobManifest(wObj *kaitov1beta1.Workspace, revisionNum string,
 	// TODO: make containers only mount the volumes they need
 
 	for i := range initContainers {
-		initContainers[i].VolumeMounts = append(initContainers[i].VolumeMounts, volumeMounts...)
+		initContainers[i].VolumeMounts = utils.DedupVolumeMounts(append(initContainers[i].VolumeMounts, volumeMounts...))
 	}
 
 	for i := range sidecarContainers {
-		sidecarContainers[i].VolumeMounts = append(sidecarContainers[i].VolumeMounts, volumeMounts...)
+		sidecarContainers[i].VolumeMounts = utils.DedupVolumeMounts(append(sidecarContainers[i].VolumeMounts, volumeMounts...))
 	}
 
 	// Construct the complete list of containers (main and sidecars)
@@ -258,7 +262,7 @@ func GenerateTuningJobManifest(wObj *kaitov1beta1.Workspace, revisionNum string,
 func GenerateDeploymentManifest(workspaceObj *kaitov1beta1.Workspace, revisionNum string, imageName string,
 	imagePullSecretRefs []corev1.LocalObjectReference, replicas int, commands []string, containerPorts []corev1.ContainerPort,
 	livenessProbe, readinessProbe *corev1.Probe, resourceRequirements corev1.ResourceRequirements,
-	tolerations []corev1.Toleration, volumes []corev1.Volume, volumeMount []corev1.VolumeMount, envVars []corev1.EnvVar) *appsv1.Deployment {
+	tolerations []corev1.Toleration, volumes []corev1.Volume, volumeMount []corev1.VolumeMount, envVars []corev1.EnvVar, initContainers []corev1.Container) *appsv1.Deployment {
 
 	nodeRequirements := make([]corev1.NodeSelectorRequirement, 0, len(workspaceObj.Resource.LabelSelector.MatchLabels))
 	for key, value := range workspaceObj.Resource.LabelSelector.MatchLabels {
@@ -279,6 +283,7 @@ func GenerateDeploymentManifest(workspaceObj *kaitov1beta1.Workspace, revisionNu
 	pullerContainers, pullerEnvVars, pullerVolumes := GeneratePullerContainers(workspaceObj, volumeMount)
 	envVars = append(envVars, pullerEnvVars...)
 	volumes = append(volumes, pullerVolumes...)
+	initContainers = append(initContainers, pullerContainers...)
 
 	return &appsv1.Deployment{
 		ObjectMeta: v1.ObjectMeta{
@@ -324,7 +329,7 @@ func GenerateDeploymentManifest(workspaceObj *kaitov1beta1.Workspace, revisionNu
 							},
 						},
 					},
-					InitContainers: pullerContainers,
+					InitContainers: initContainers,
 					Containers: []corev1.Container{
 						{
 							Name:           workspaceObj.Name,
@@ -434,3 +439,35 @@ func GenerateDeploymentManifestWithPodTemplate(workspaceObj *kaitov1beta1.Worksp
 		},
 	}
 }
+
+func GetModelImageName(presetObj *pkgmodel.PresetParam) string {
+	return utils.GetPresetImageName(presetObj.Name, presetObj.Tag)
+}
+
+// GenerateModelPullerContainer creates an init container that pulls model images using ORAS
+func GenerateModelPullerContainer(ctx context.Context, workspaceObj *v1beta1.Workspace, presetObj *pkgmodel.PresetParam) []corev1.Container {
+	if presetObj.DownloadAtRuntime {
+		// If the preset is set to download at runtime, we don't need to pull the model weights.
+		return nil
+	}
+
+	puller := corev1.Container{
+		Name:  "model-weights-downloader",
+		Image: "ghcr.io/oras-project/oras:v1.2.2",
+		Command: []string{
+			"oras",
+			"pull",
+			GetModelImageName(presetObj),
+			"-o",
+			utils.DefaultWeightsVolumePath,
+		},
+		VolumeMounts: []corev1.VolumeMount{
+			{
+				Name:      "model-weights-volume",
+				MountPath: utils.DefaultWeightsVolumePath,
+			},
+		},
+	}
+
+	return []corev1.Container{puller}
+}
diff --git a/pkg/workspace/tuning/preset-tuning.go b/pkg/workspace/tuning/preset-tuning.go
index 0183bda..5757a13 100644
--- a/pkg/workspace/tuning/preset-tuning.go
+++ b/pkg/workspace/tuning/preset-tuning.go
@@ -6,7 +6,6 @@ package tuning
 import (
 	"context"
 	"fmt"
-	"os"
 	"path/filepath"
 	"strings"
 
@@ -24,6 +23,7 @@ import (
 	"github.com/kaito-project/kaito/pkg/utils/resources"
 	"github.com/kaito-project/kaito/pkg/workspace/image"
 	"github.com/kaito-project/kaito/pkg/workspace/manifests"
+	metadata "github.com/kaito-project/kaito/presets/workspace/models"
 )
 
 const (
@@ -64,12 +64,9 @@ func getInstanceGPUCount(sku string) int {
 	return 1
 }
 
-func GetTuningImageInfo(ctx context.Context, workspaceObj *kaitov1beta1.Workspace, presetObj *model.PresetParam) string {
-	imageName := string(workspaceObj.Tuning.Preset.Name)
-	imageTag := presetObj.Tag
-	registryName := os.Getenv("PRESET_REGISTRY_NAME")
-	imageName = fmt.Sprintf("%s/kaito-%s:%s", registryName, imageName, imageTag)
-	return imageName
+func GetTuningImageInfo() string {
+	presetObj := metadata.MustGet("base")
+	return utils.GetPresetImageName(presetObj.Name, presetObj.Tag)
 }
 
 func GetDataSrcImageInfo(ctx context.Context, wObj *kaitov1beta1.Workspace) (string, []corev1.LocalObjectReference) {
@@ -188,6 +185,7 @@ func CreatePresetTuning(ctx context.Context, workspaceObj *kaitov1beta1.Workspac
 	volumes = append(volumes, trainingOutputVolume)
 	volumeMounts = append(volumeMounts, trainingOutputVolumeMount)
 
+	// Add volume for training input
 	initContainer, dataSourceVolumes, dataSourceVolumeMounts := prepareDataSource(ctx, workspaceObj)
 	volumes = append(volumes, dataSourceVolumes...)
 	volumeMounts = append(volumeMounts, dataSourceVolumeMounts...)
@@ -195,6 +193,12 @@ func CreatePresetTuning(ctx context.Context, workspaceObj *kaitov1beta1.Workspac
 		initContainers = append(initContainers, *initContainer)
 	}
 
+	// Add volume for model weights access
+	modelWeightsVolume, modelWeightsVolumeMount := utils.ConfigModelWeightsVolume()
+	volumes = append(volumes, modelWeightsVolume)
+	volumeMounts = append(volumeMounts, modelWeightsVolumeMount)
+	initContainers = append(initContainers, manifests.GenerateModelPullerContainer(ctx, workspaceObj, tuningObj)...)
+
 	sidecarContainer, imagePushSecret, imagePushSecretVolume, imagePushSecretVolumeMount := prepareDataDestination(ctx, workspaceObj, outputDir)
 	volumes = append(volumes, imagePushSecretVolume...)
 	volumeMounts = append(volumeMounts, imagePushSecretVolumeMount...)
@@ -229,7 +233,7 @@ func CreatePresetTuning(ctx context.Context, workspaceObj *kaitov1beta1.Workspac
 	}
 
 	commands, resourceReq := prepareTuningParameters(ctx, workspaceObj, modelCommand, tuningObj, skuNumGPUs)
-	tuningImage := GetTuningImageInfo(ctx, workspaceObj, tuningObj)
+	tuningImage := GetTuningImageInfo()
 
 	var envVars []corev1.EnvVar
 	presetName := strings.ToLower(string(workspaceObj.Tuning.Preset.Name))
diff --git a/presets/README.md b/presets/README.md
deleted file mode 100644
index fdfecf9..0000000
--- a/presets/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Kaito Preset Configurations
-The current supported model families with preset configurations are listed below.
-
-| Model Family                                | Compatible Kaito Versions |
-|---------------------------------------------|---------------------------|
-| [falcon](./workspace/models/falcon)         | v0.0.1+                   |
-| [mistral](./workspace/models/mistral)       | v0.2.0+                   |
-| [phi2](./workspace/models/phi2)             | v0.2.0+                   |
-| [phi3](./workspace/models/phi3)             | v0.3.0+                   |
-| [phi4](./workspace/models/phi4)             | v0.4.5+                   |
-| [qwen7b](./workspace/models/qwen)           | v0.4.1+                   |
-| [qwen32b](./workspace/models/qwen)          | v0.4.5+                   |
-| [llama3](./workspace/models/llama3)         | v0.4.6+                   |
-
-
-## Validation
-Each preset model has its own hardware requirements in terms of GPU count and GPU memory defined in the respective `model.go` file. Kaito controller performs a validation check of whether the specified SKU and node count are sufficient to run the model or not. In case the provided SKU is not in the known list, the controller bypasses the validation check which means users need to ensure the model can run with the provided SKU. 
-
-## Distributed inference
-
-For models that support distributed inference, when the node count is larger than one, [torch distributed elastic](https://pytorch.org/docs/stable/distributed.elastic.html) is configured with master/worker pods running in multiple nodes and the service endpoint is the master pod.
diff --git a/presets/workspace/dependencies/README.md b/presets/workspace/dependencies/README.md
index c267182..55595f7 100644
--- a/presets/workspace/dependencies/README.md
+++ b/presets/workspace/dependencies/README.md
@@ -1,3 +1,3 @@
 # Requirement
 
-Pip dependencies for both Kaito inference and tuning.
\ No newline at end of file
+Pip dependencies for both KAITO inference and tuning.
\ No newline at end of file
diff --git a/presets/workspace/inference/chat_templates/chat_template_guide.md b/presets/workspace/inference/chat_templates/chat_template_guide.md
index 5333711..7d655d6 100644
--- a/presets/workspace/inference/chat_templates/chat_template_guide.md
+++ b/presets/workspace/inference/chat_templates/chat_template_guide.md
@@ -46,7 +46,7 @@ A chat template is defined in a `.jinja` file. Below is an example template:
 
 ## Using Chat Templates with Inference API
 
-Kaito has already provided chat template options in the inference_api code. Simply use the --chat-template flag to pass the path to your .jinja template file.
+KAITO has already provided chat template options in the inference_api code. Simply use the --chat-template flag to pass the path to your .jinja template file.
 
 Find the existing jinja template in the [`chat_templates`](https://github.com/kaito-project/kaito/tree/main/presets/workspace/inference/chat_templates) directory.
 
diff --git a/presets/workspace/models/deepseek/README.md b/presets/workspace/models/deepseek/README.md
index 7bd1000..d25afa7 100644
--- a/presets/workspace/models/deepseek/README.md
+++ b/presets/workspace/models/deepseek/README.md
@@ -6,7 +6,7 @@
 
 
 ## Image Source
-- **Public**: Kaito maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
+- **Public**: KAITO maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
 
 ## Usage
 
diff --git a/presets/workspace/models/falcon/README.md b/presets/workspace/models/falcon/README.md
index daa7d3e..24d77fc 100644
--- a/presets/workspace/models/falcon/README.md
+++ b/presets/workspace/models/falcon/README.md
@@ -7,7 +7,7 @@
 | falcon-40b          |     [tiiuae](https://huggingface.co/tiiuae/falcon-40b)      |     [link](../../../../examples/inference/kaito_workspace_falcon_40b.yaml)      |     Deployment      |         false         |
 
 ## Image Source
-- **Public**: Kaito maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
+- **Public**: KAITO maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
 
 ## Usage
 
diff --git a/presets/workspace/models/llama3/README.md b/presets/workspace/models/llama3/README.md
index c258529..9fe91d5 100644
--- a/presets/workspace/models/llama3/README.md
+++ b/presets/workspace/models/llama3/README.md
@@ -6,7 +6,7 @@
 
 ## Image Source
 
-- **Public**: Kaito maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
+- **Public**: KAITO maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
 
 ## Usage
 
diff --git a/presets/workspace/models/mistral/README.md b/presets/workspace/models/mistral/README.md
index 62a5ae3..e66f58b 100644
--- a/presets/workspace/models/mistral/README.md
+++ b/presets/workspace/models/mistral/README.md
@@ -6,7 +6,7 @@
 
 
 ## Image Source
-- **Public**: Kaito maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
+- **Public**: KAITO maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
 
 ## Usage
 
diff --git a/presets/workspace/models/phi2/README.md b/presets/workspace/models/phi2/README.md
index 14f62c2..653a0ba 100644
--- a/presets/workspace/models/phi2/README.md
+++ b/presets/workspace/models/phi2/README.md
@@ -5,7 +5,7 @@
 
 
 ## Image Source
-- **Public**: Kaito maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
+- **Public**: KAITO maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
 
 ## Usage
 
diff --git a/presets/workspace/models/phi3/README.md b/presets/workspace/models/phi3/README.md
index a7aa096..ab3a8ca 100644
--- a/presets/workspace/models/phi3/README.md
+++ b/presets/workspace/models/phi3/README.md
@@ -8,7 +8,7 @@
 | phi-3.5-mini-instruct      | [microsoft](https://huggingface.co/microsoft/Phi-3.5-mini-instruct)      | [link](../../../../examples/inference/kaito_workspace_phi_3.5-instruct.yaml)  |     Deployment      |         false         |
 
 ## Image Source
-- **Public**: Kaito maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
+- **Public**: KAITO maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
 
 ## Usage
 
diff --git a/presets/workspace/models/phi4/README.md b/presets/workspace/models/phi4/README.md
index 63b41cc..7ef9dec 100644
--- a/presets/workspace/models/phi4/README.md
+++ b/presets/workspace/models/phi4/README.md
@@ -5,7 +5,7 @@
 | phi-4-mini-instruct       |   [microsoft](https://huggingface.co/microsoft/phi-4-mini-instruct)     |   [link](../../../../examples/inference/kaito_workspace_phi_4_mini.yaml)     |     Deployment      |         false         |
 
 ## Image Source
-- **Public**: Kaito maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
+- **Public**: KAITO maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
 
 ## Usage
 
diff --git a/presets/workspace/models/qwen/README.md b/presets/workspace/models/qwen/README.md
index 69fc66f..e49755c 100644
--- a/presets/workspace/models/qwen/README.md
+++ b/presets/workspace/models/qwen/README.md
@@ -5,7 +5,7 @@
 | qwen2.5-coder-32b-instruct | [qwen](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) | [link](../../../../examples/inference/kaito_workspace_qwen_2.5_coder_32b-instruct.yaml) |     Deployment      |         false         |
 
 ## Image Source
-- **Public**: Kaito maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
+- **Public**: KAITO maintainers manage the lifecycle of the inference service images that contain model weights. The images are available in Microsoft Container Registry (MCR).
 
 ## Usage
 
diff --git a/website/.gitignore b/website/.gitignore
new file mode 100644
index 0000000..b2d6de3
--- /dev/null
+++ b/website/.gitignore
@@ -0,0 +1,20 @@
+# Dependencies
+/node_modules
+
+# Production
+/build
+
+# Generated files
+.docusaurus
+.cache-loader
+
+# Misc
+.DS_Store
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
diff --git a/website/README.md b/website/README.md
new file mode 100644
index 0000000..77ff402
--- /dev/null
+++ b/website/README.md
@@ -0,0 +1,30 @@
+# KAITO Documentation Website
+
+This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator.
+
+## Installation
+
+```bash
+cd website
+npm install
+```
+
+## Local Development
+
+```bash
+npm start
+```
+
+This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
+
+## Build
+
+```bash
+npm run build
+```
+
+This command generates static content into the `build` directory and can be served using any static contents hosting service.
+
+## Deployment
+
+The website is automatically deployed to GitHub Pages when changes are pushed to the main branch.
diff --git a/website/babel.config.js b/website/babel.config.js
new file mode 100644
index 0000000..e00595d
--- /dev/null
+++ b/website/babel.config.js
@@ -0,0 +1,3 @@
+module.exports = {
+  presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
+};
diff --git a/docs/aws/aws_installation.md b/website/docs/aws.md
similarity index 96%
rename from docs/aws/aws_installation.md
rename to website/docs/aws.md
index 07be30a..14d56a5 100644
--- a/docs/aws/aws_installation.md
+++ b/website/docs/aws.md
@@ -1,3 +1,7 @@
+---
+title: AWS Deployment
+---
+
 # Installation using AWS EKS cluster
 
 Before you begin, ensure you have the following tools installed:
@@ -60,7 +64,7 @@ kubectl describe deploy karpenter -n $KARPENTER_NAMESPACE
 ```
 
 ## Create a Workspace and start an inference service
-Once the Kaito and Karpenter controllers are installed, you can follow these commands to start a falcon-7b inference service.
+Once the KAITO and Karpenter controllers are installed, you can follow these commands to start a falcon-7b inference service.
 
 ```bash
 $ export kaito_workspace_aws="../../examples/inference/kaito_workspace_falcon_7b_aws.yaml"
@@ -96,6 +100,6 @@ $ kubectl get svc aws-workspace
 NAME                  TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)            AGE
 aws-workspace         ClusterIP   <CLUSTERIP>  <none>        80/TCP,29500/TCP   10m
 
-export CLUSTERIP=$(kubectl get svc aws-workspace -o jsonpath="{.spec.clusterIPs[0]}") 
+export CLUSTERIP=$(kubectl get svc aws-workspace -o jsonpath="{.spec.clusterIPs[0]}")
 $ kubectl run -it --rm --restart=Never curl --image=curlimages/curl -- curl -X POST http://$CLUSTERIP/chat -H "accept: application/json" -H "Content-Type: application/json" -d "{\"prompt\":\"YOUR QUESTION HERE\"}"
 ```
\ No newline at end of file
diff --git a/docs/contributing/readme.md b/website/docs/contributing.md
similarity index 68%
rename from docs/contributing/readme.md
rename to website/docs/contributing.md
index 256b585..35449ab 100644
--- a/docs/contributing/readme.md
+++ b/website/docs/contributing.md
@@ -1,16 +1,10 @@
-# Contributing
+---
+title: Contributing
+---
 
-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit <https://cla.opensource.microsoft.com>.
+This project welcomes contributions and suggestions.
 
-When you submit a pull request, a CLA bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+KAITO has adopted the [Cloud Native Compute Foundation Code of Conduct](https://github.com/cncf/foundation/blob/main/code-of-conduct.md). For more information see the [KAITO Code of Conduct](https://github.com/kaito-project/kaito/blob/main/CODE_OF_CONDUCT.md).
 
 ## Development
 
@@ -45,7 +39,7 @@ cluster_name: <cluster_name>
 EOF
 ```
 
-- `default_registry`: The container registry where Tilt pushes development images (e.g. `<acr_name>.azurecr.io`). Ensure you’re logged in with `docker login` and have push/pull permissions.
+- `default_registry`: The container registry where Tilt pushes development images (e.g. `<acr_name>.azurecr.io`). Ensure you're logged in with `docker login` and have push/pull permissions.
 if you want to use Azure Container Registry (ACR), set up a new ACR instance and attach it to your AKS cluster with these commands:
 
 ```bash
@@ -92,15 +86,15 @@ Opening browser: http://localhost:10350/
 
 <br/>
 
-![Tilt UI](../img/tilt.png)
+![Tilt UI](../static/img/tilt.png)
 
 Tilt automates the following steps during `tilt up`:
 
 <!-- markdown-link-check-disable -->
 1. **Start the Tilt Server**: Runs locally at http://localhost:10350/ and opens the Tilt UI in your browser (press space).
 <!-- markdown-link-check-enable -->
-2. **Generate and Build**: Creates CRD YAML files (`make generate manifests`) and compiles the controller manager Go binary (`go build`), saving it to `./tilt_bin`.
-3. **Build Development Image**: Packages the binary into a container image and tag it with `<default_registry>/kaito/workspace:tilt-<hash>`), then pushes it to your container registry.
-4. **Apply CRDs**: Deploys CRDs to the cluster with `kubectl apply --server-side -f charts/kaito/crds/`.
-5. **Deploy Resources**: Renders the Helm chart (`helm template charts/kaito/workspace`) and applies it to the cluster, including the controller manager, webhooks, RBAC, and NVIDIA device plugin resources.
-6. **Live Updates**: Detects changes to the controller manager source code, rebuilds the binary, updates the container, and restarts the controller manager within the container without re-deploying the pod - all in ~30 seconds.
+1. **Generate and Build**: Creates CRD YAML files (`make generate manifests`) and compiles the controller manager Go binary (`go build`), saving it to `./tilt_bin`.
+2. **Build Development Image**: Packages the binary into a container image and tag it with `<default_registry>/kaito/workspace:tilt-<hash>`), then pushes it to your container registry.
+3. **Apply CRDs**: Deploys CRDs to the cluster with `kubectl apply --server-side -f charts/kaito/crds/`.
+4. **Deploy Resources**: Renders the Helm chart (`helm template charts/kaito/workspace`) and applies it to the cluster, including the controller manager, webhooks, RBAC, and NVIDIA device plugin resources.
+5. **Live Updates**: Detects changes to the controller manager source code, rebuilds the binary, updates the container, and restarts the controller manager within the container without re-deploying the pod - all in ~30 seconds.
diff --git a/docs/custom-model-integration/custom-model-integration-guide.md b/website/docs/custom-model.md
similarity index 83%
rename from docs/custom-model-integration/custom-model-integration-guide.md
rename to website/docs/custom-model.md
index a3b20f7..c3b7db7 100644
--- a/docs/custom-model-integration/custom-model-integration-guide.md
+++ b/website/docs/custom-model.md
@@ -1,10 +1,12 @@
-# Custom Model Integration Guide
+---
+title: Custom Model Integration
+---
 
 ## Option 1: Use Pre-Built Docker Image Without Model Weights
-If you want to avoid building a Docker image with model weights, use our pre-built reference image (`ghcr.io/kaito-project/kaito/llm-reference-preset:latest`). This image, built with [Dockerfile.reference](./Dockerfile.reference), dynamically downloads model weights from HuggingFace at runtime, reducing the need to create and maintain custom images.
+If you want to avoid building a Docker image with model weights, use our pre-built reference image (`ghcr.io/kaito-project/kaito/llm-reference-preset:latest`). This image, built with [Dockerfile.reference](../../examples/custom-model-integration/Dockerfile.reference), dynamically downloads model weights from HuggingFace at runtime, reducing the need to create and maintain custom images.
 
 
-- **[Sample Deployment YAML](./reference-image-deployment.yaml)**
+- **[Sample Deployment YAML](../../examples/custom-model-integration/reference-image-deployment.yaml)**
 
 
 ## Option 2: Build a Custom Docker Image with Model Weights
@@ -70,7 +72,7 @@ export WEIGHTS_PATH="kaito/phi-3-mini-4k-instruct/weights"
 
 2. Build and Push the Docker Image
 
-Navigate to the Kaito base directory and build the Docker image, ensuring the weights directory is included in the build context:
+Navigate to the KAITO base directory and build the Docker image, ensuring the weights directory is included in the build context:
 ```sh
 docker build -t <IMAGE_NAME> --file docker/presets/workspace/models/tfs/Dockerfile --build-arg WEIGHTS_PATH=<WEIGHTS_PATH> --build-arg MODEL_TYPE=text-generation --build-arg VERSION=<VERSION> .
 
@@ -78,4 +80,4 @@ docker push <IMAGE_NAME>
 ```
 
 ### Step 5: Deploy
-Follow the [Custom Template](./custom-deployment-template.yaml)
+Follow the [Custom Template](../../examples/custom-model-integration/custom-deployment-template.yaml)
\ No newline at end of file
diff --git a/website/docs/faq.md b/website/docs/faq.md
new file mode 100644
index 0000000..ba41b80
--- /dev/null
+++ b/website/docs/faq.md
@@ -0,0 +1,59 @@
+---
+title: FAQ
+---
+
+### How do I ensure preferred nodes are correctly labeled for use in my workspace?
+
+For using preferred nodes, make sure the node has the label specified in the labelSelector under matchLabels. For example, if your labelSelector is:
+
+```yaml
+labelSelector:
+  matchLabels:
+    apps: falcon-7b
+```
+
+Then the node should have the label: `apps=falcon-7b`.
+
+### How to upgrade the existing deployment to use the latest model configuration?
+
+When using hosted public models, you can delete the existing inference workload (`Deployment` or `StatefulSet`) manually, and the workspace controller will create a new one with the latest preset configuration (e.g., the image version) defined in the current release.
+
+For private models, it is recommended to create a new workspace with a new image version in the Spec.
+
+### How to update model/inference parameters to override the KAITO Preset Configuration?
+
+KAITO provides a limited capability to override preset configurations for models that use `transformer` runtime manually.
+
+To update parameters for a deployed model, perform `kubectl edit` against the workload, which could be either a `StatefulSet` or `Deployment`.
+
+For example, to enable 4-bit quantization on a `falcon-7b-instruct` deployment:
+
+```bash
+kubectl edit deployment workspace-falcon-7b-instruct
+```
+
+Within the deployment specification, locate and modify the command field.
+
+**Original:**
+```bash
+accelerate launch --num_processes 1 --num_machines 1 --machine_rank 0 --gpu_ids all inference_api.py --pipeline text-generation --torch_dtype bfloat16
+```
+
+**Modified to enable 4-bit Quantization:**
+```bash
+accelerate launch --num_processes 1 --num_machines 1 --machine_rank 0 --gpu_ids all inference_api.py --pipeline text-generation --torch_dtype bfloat16 --load_in_4bit
+```
+
+Currently, we allow users to change the following parameters manually:
+
+- `pipeline`: For text-generation models this can be either `text-generation` or `conversational`.
+- `load_in_4bit` or `load_in_8bit`: Model quantization resolution.
+
+Should you need to customize other parameters, kindly file an issue for potential future inclusion.
+
+### What is the difference between instruct and non-instruct models?
+
+The main distinction lies in their intended use cases:
+
+- **Instruct models**: Fine-tuned versions optimized for interactive chat applications. They are typically the preferred choice for most implementations due to their enhanced performance in conversational contexts.
+- **Non-instruct (raw) models**: Designed for further fine-tuning with your own data.
diff --git a/docs/inference/README.md b/website/docs/inference.md
similarity index 90%
rename from docs/inference/README.md
rename to website/docs/inference.md
index b71b0d2..a52f38d 100644
--- a/docs/inference/README.md
+++ b/website/docs/inference.md
@@ -1,10 +1,12 @@
-# Kaito Inference
+---
+title: Inference
+---
 
-This document presents how to use the Kaito `workspace` Custom Resource Definition (CRD) for model serving and serving with LoRA adapters.
+This document presents how to use the KAITO `workspace` Custom Resource Definition (CRD) for model serving and serving with LoRA adapters.
 
 ## Usage
 
-The basic usage for inference is simple. Users just need to specify the GPU SKU used for inference in the `resource` spec and one of the Kaito supported model name in the `inference` spec in the `workspace` custom resource. For example,
+The basic usage for inference is simple. Users just need to specify the GPU SKU used for inference in the `resource` spec and one of the KAITO supported model name in the `inference` spec in the `workspace` custom resource. For example,
 
 ```yaml
 apiVersion: kaito.sh/v1beta1
@@ -21,7 +23,7 @@ inference:
     name: "falcon-7b"
 ```
 
-If a user runs Kaito in an on-premise Kubernetes cluster where GPU SKUs are unavailable, the GPU nodes can be pre-configured. The user should ensure that the corresponding vendor-specific GPU plugin is installed successfully in every prepared node, i.e. the node status should report a non-zero GPU resource in the allocatable field. For example:
+If a user runs KAITO in an on-premise Kubernetes cluster where GPU SKUs are unavailable, the GPU nodes can be pre-configured. The user should ensure that the corresponding vendor-specific GPU plugin is installed successfully in every prepared node, i.e. the node status should report a non-zero GPU resource in the allocatable field. For example:
 
 ```
 $ kubectl get node $NODE_NAME -o json | jq .status.allocatable
@@ -36,9 +38,11 @@ $ kubectl get node $NODE_NAME -o json | jq .status.allocatable
 }
 ```
 
-Next, the user needs to add the node names in the `preferredNodes` field in the `resource` spec. As a result, the Kaito controller will skip the steps for GPU node provisioning and use the prepared nodes to run the inference workload.
-> [!IMPORTANT]
-> The node objects of the preferred nodes need to contain the same matching labels as specified in the `resource` spec. Otherwise, the Kaito controller would not recognize them.
+Next, the user needs to add the node names in the `preferredNodes` field in the `resource` spec. As a result, the KAITO controller will skip the steps for GPU node provisioning and use the prepared nodes to run the inference workload.
+
+:::warning
+The node objects of the preferred nodes need to contain the same matching labels as specified in the `resource` spec. Otherwise, the KAITO controller would not recognize them.
+:::
 
 ### Inference runtime selection
 
@@ -106,9 +110,9 @@ Key vLLM parameters include:
 
 For the complete list of vLLM parameters, refer to the [vLLM documentation](https://docs.vllm.ai/en/latest/serving/engine_args.html).
 
-### Inference with LoRA adapters 
+### Inference with LoRA adapters
 
-Kaito also supports running the inference workload with LoRA adapters produced by [model fine-tuning jobs](../tuning/README.md). Users can specify one or more adapters in the `adapters` field of the `inference` spec. For example,
+KAITO also supports running the inference workload with LoRA adapters produced by [model fine-tuning jobs](./tuning.md). Users can specify one or more adapters in the `adapters` field of the `inference` spec. For example,
 
 ```yaml
 apiVersion: kaito.sh/v1beta1
@@ -224,13 +228,11 @@ curl -X POST \
 
 # Inference workload
 
-Depending on whether the specified model supports distributed inference or not, the Kaito controller will choose to use either Kubernetes **apps.deployment** workload (by default) or Kubernetes **apps.statefulset** workload (if the model supports distributed inference) to manage the inference service, which is exposed using a Cluster-IP type of Kubernetes `service`.
+Depending on whether the specified model supports distributed inference or not, the KAITO controller will choose to use either Kubernetes **apps.deployment** workload (by default) or Kubernetes **apps.statefulset** workload (if the model supports distributed inference) to manage the inference service, which is exposed using a Cluster-IP type of Kubernetes `service`.
 
-When adapters are specified in the `inference` spec, the Kaito controller adds an initcontainer for each adapter in addition to the main container. The pod structure is shown in Figure 1.
+When adapters are specified in the `inference` spec, the KAITO controller adds an initcontainer for each adapter in addition to the main container. The pod structure is shown in Figure 1.
 
-<div align="left">
-  <img src="../img/kaito-inference-adapter.png" width=40% title="Kaito inference adapter" alt="Kaito inference adapter">
-</div>
+![KAITO inference service pod structure](../static/img/kaito-inference-adapter.png)
 
 If an image is specified as the adapter source, the corresponding initcontainer uses that image as its container image. These initcontainers ensure all adapter data is available locally before the inference service starts. The main container uses a supported model image, launching the [inference_api.py](../../presets/workspace/inference/text-generation/inference_api.py) script.
 
@@ -238,9 +240,9 @@ All containers share local volumes by mounting the same `EmptyDir` volumes, avoi
 
 ## Workload update
 
-To update the `adapters` field in the `inference` spec, users can modify the `workspace` custom resource. The Kaito controller will apply the changes, triggering a workload deployment update. This will recreate the inference service pod, resulting in a brief service downtime. Once the new adapters are merged with the raw model weights and loaded into GPU memory, the service will resume.
+To update the `adapters` field in the `inference` spec, users can modify the `workspace` custom resource. The KAITO controller will apply the changes, triggering a workload deployment update. This will recreate the inference service pod, resulting in a brief service downtime. Once the new adapters are merged with the raw model weights and loaded into GPU memory, the service will resume.
 
 
 # Troubleshooting
 
-TBD
+TBD
\ No newline at end of file
diff --git a/docs/installation.md b/website/docs/installation.md
similarity index 97%
rename from docs/installation.md
rename to website/docs/installation.md
index 2f4940a..f9d0e41 100644
--- a/docs/installation.md
+++ b/website/docs/installation.md
@@ -1,6 +1,8 @@
-# Installation 
+---
+title: Installation
+---
 
-The following guidance assumes **Azure Kubernetes Service(AKS)** is used to host the Kubernetes cluster. If you want to use Elastic Kubernetes Service (EKS) instead, please follow the installation guide [here](./aws/aws_installation.md)
+The following guidance assumes **Azure Kubernetes Service(AKS)** is used to host the Kubernetes cluster. If you want to use Elastic Kubernetes Service (EKS) instead, please follow the installation guide [here](https://github.com/kaito-project/kaito/blob/main/docs/aws/aws_installation.md).
 
 Before you begin, ensure you have the following tools installed:
 
@@ -54,7 +56,7 @@ Note that if you have installed another node provisioning controller that suppor
 ## Install gpu-provisioner controller
 
 #### Enable Workload Identity and OIDC Issuer features
-The *gpu-provisioner* controller requires the [workload identity](https://learn.microsoft.com/azure/aks/workload-identity-overview?tabs=dotnet) feature to acquire the access token to the AKS cluster. 
+The *gpu-provisioner* controller requires the [workload identity](https://learn.microsoft.com/azure/aks/workload-identity-overview?tabs=dotnet) feature to acquire the access token to the AKS cluster.
 
 > Run the following commands only if your AKS cluster does not already have the Workload Identity and OIDC issuer features enabled.
 
@@ -126,8 +128,8 @@ Check status of the `gpu-provisioner`.
 kubectl describe deploy gpu-provisioner -n gpu-provisioner
 ```
 
-## Troubleshooting 
-If you see that the `gpu-provisioner` deployment is not running after some time, it's possible that some values incorrect in your `values.ovveride.yaml`. 
+## Troubleshooting
+If you see that the `gpu-provisioner` deployment is not running after some time, it's possible that some values incorrect in your `values.ovveride.yaml`.
 
 Run the following command to check `gpu-provisioner` pod logs for additional details.
 
diff --git a/website/docs/intro.md b/website/docs/intro.md
new file mode 100644
index 0000000..300f4b6
--- /dev/null
+++ b/website/docs/intro.md
@@ -0,0 +1,54 @@
+---
+title: Introduction
+slug: /
+---
+
+:::info What's NEW!
+**Coming soon:** KAITO v0.5.0. Retrieval-augmented generation (RAG) - RagEngine support with LlamaIndex orchestration and Faiss as the default vectorDB, learn about recent updates [here](https://github.com/kaito-project/kaito/issues/734)!
+
+**Latest Release:** May 14th, 2025. KAITO v0.4.6.
+
+**First Release:** Nov 15th, 2023. KAITO v0.1.0.
+:::
+
+KAITO is an operator that automates the AI/ML model inference or tuning workload in a Kubernetes cluster.
+The target models are popular open-sourced large models such as [falcon](https://huggingface.co/tiiuae) and [phi-3](https://huggingface.co/docs/transformers/main/en/model_doc/phi3).
+
+## Key Features
+
+KAITO has the following key differentiations compared to most of the mainstream model deployment methodologies built on top of virtual machine infrastructures:
+
+- **Container-based Model Management**: Manage large model files using container images with an OpenAI-compatible server for inference calls
+- **Preset Configurations**: Avoid adjusting workload parameters based on GPU hardware with built-in configurations
+- **Multiple Runtime Support**: Support for popular inference runtimes including [vLLM](https://github.com/vllm-project/vllm) and [transformers](https://github.com/huggingface/transformers)
+- **Auto-provisioning**: Automatically provision GPU nodes based on model requirements
+- **Public Registry**: Host large model images in the public Microsoft Container Registry (MCR) when licenses allow
+
+Using KAITO, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
+
+## Architecture
+
+KAITO follows the classic Kubernetes Custom Resource Definition(CRD)/controller design pattern. Users manage a `workspace` custom resource which describes the GPU requirements and the inference or tuning specification. KAITO controllers automate the deployment by reconciling the `workspace` custom resource.
+
+![KAITO architecture](../static/img/arch.png)
+
+The above figure presents the KAITO architecture overview. Its major components consist of:
+
+- **Workspace controller**: Reconciles the `workspace` custom resource, creates `machine` custom resources to trigger node auto provisioning, and creates the inference or tuning workload (`deployment`, `statefulset` or `job`) based on the model preset configurations.
+- **Node provisioner controller**: The controller's name is *gpu-provisioner* in [gpu-provisioner helm chart](https://github.com/Azure/gpu-provisioner/tree/main/charts/gpu-provisioner). It uses the `machine` CRD originated from [Karpenter](https://sigs.k8s.io/karpenter) to interact with the workspace controller. It integrates with Azure Resource Manager REST APIs to add new GPU nodes to the AKS or AKS Arc cluster.
+
+:::note
+The [*gpu-provisioner*](https://github.com/Azure/gpu-provisioner) is an open sourced component. It can be replaced by other controllers if they support [Karpenter-core](https://sigs.k8s.io/karpenter) APIs.
+:::
+
+## Getting Started
+
+👉 To get started, please see the [Installation Guide](installation)!
+
+👉 For a quick start tutorial, check out [Quick Start](quick-start)!
+
+## Community
+
+- **GitHub**: [kaito-project/kaito](https://github.com/kaito-project/kaito)
+- **Slack**: [Join our community](https://join.slack.com/t/kaito-z6a6575/shared_invite/zt-37gh89vw7-odHfqmPRc5oRnDG99SBJNA)
+- **Email**: [kaito-dev@microsoft.com](mailto:kaito-dev@microsoft.com)
diff --git a/docs/kaito-on-BYO-gpu-nodes.md b/website/docs/kaito-on-byo-gpu-nodes.md
similarity index 83%
rename from docs/kaito-on-BYO-gpu-nodes.md
rename to website/docs/kaito-on-byo-gpu-nodes.md
index 557efcc..d51c66c 100644
--- a/docs/kaito-on-BYO-gpu-nodes.md
+++ b/website/docs/kaito-on-byo-gpu-nodes.md
@@ -1,6 +1,8 @@
-# Bring Your Own (BYO) GPU Nodes
+---
+title: Bring Your Own GPU Nodes
+---
 
-This guide walks you through deploying Kaito on a Kubernetes cluster with self-provisioned GPU nodes.
+This guide walks you through deploying KAITO on a Kubernetes cluster with self-provisioned GPU nodes.
 
 ## Prerequisites
 
@@ -15,8 +17,9 @@ If you are following the demo as is then you would need access to an Azure accou
 
 ## Set up a Kubernetes cluster with GPU nodes
 
-> [!NOTE]
-> If you already have a Kubernetes cluster, you can skip this section.
+:::note
+If you already have a Kubernetes cluster, you can skip this section.
+:::
 
 For the sake of this guide, we will create an Azure Kubernetes Service (AKS) cluster.
 
@@ -69,8 +72,9 @@ az aks create \
 
 Run the following commands to add or update the `aks-preview` extension:
 
-> [!IMPORTANT]
-> This is needed to enable the `--skip-gpu-driver-install` flag, you can read more about it [here](https://learn.microsoft.com/en-us/azure/aks/gpu-cluster?tabs=add-ubuntu-gpu-node-pool#skip-gpu-driver-installation-preview).
+:::warning
+This is needed to enable the `--skip-gpu-driver-install` flag, you can read more about it [here](https://learn.microsoft.com/en-us/azure/aks/gpu-cluster?tabs=add-ubuntu-gpu-node-pool#skip-gpu-driver-installation-preview).
+:::
 
 ```bash
 az extension add --name aks-preview
@@ -103,8 +107,9 @@ az aks get-credentials \
 
 ### Install the NVIDIA GPU operator
 
-> [!NOTE]
-> If you have already set up your Kubernetes cluster with Nvidia's GPU operator, you can skip the GPU operator installation.
+:::note
+If you have already set up your Kubernetes cluster with Nvidia's GPU operator, you can skip the GPU operator installation.
+:::
 
 Run the following commands to create a namespace for the GPU operator:
 
@@ -152,7 +157,7 @@ nvidia   nvidia    16m
 
 ### Label the GPU nodes
 
-We need to label the GPU nodes `apps=gpu`, so that the Kaito workspace controller can schedule the inference workloads on these nodes. If you are following along the guide, you can run the following command to label the GPU nodes:
+We need to label the GPU nodes `apps=gpu`, so that the KAITO workspace controller can schedule the inference workloads on these nodes. If you are following along the guide, you can run the following command to label the GPU nodes:
 
 ```bash
 kubectl get nodes \
@@ -162,13 +167,13 @@ kubectl get nodes \
     kubectl label --overwrite {} apps=gpu
 ```
 
-> [!TIP]
-> If you have used a different set up to create the GPU nodes, you can label the nodes manually by running the following command: `kubectl label node <node-name> apps=gpu`.
+:::tip
+If you have used a different set up to create the GPU nodes, you can label the nodes manually by running the following command: `kubectl label node <node-name> apps=gpu`.
+:::
 
+## Install KAITO on the Kubernetes cluster
 
-## Install Kaito on the Kubernetes cluster
-
-Run the following command to install Kaito:
+Run the following command to install KAITO:
 
 ```bash
 helm install workspace \
@@ -209,8 +214,9 @@ inference:
 EOF
 ```
 
-> [!NOTE]
-> In the above configuration you can see we have use a node labelSelector value as `apps: gpu`, this is the same label we have applied when we added the GPU node pool earlier.
+:::note
+In the above configuration you can see we have use a node labelSelector value as `apps: gpu`, this is the same label we have applied when we added the GPU node pool earlier.
+:::
 
 Ensure that the workspace is ready by running the following command:
 
diff --git a/docs/kaito-oom-prevention.md b/website/docs/kaito-oom-prevention.md
similarity index 86%
rename from docs/kaito-oom-prevention.md
rename to website/docs/kaito-oom-prevention.md
index d2450bf..d428f06 100644
--- a/docs/kaito-oom-prevention.md
+++ b/website/docs/kaito-oom-prevention.md
@@ -1,16 +1,16 @@
-# OOM Prevention in Kaito
+---
+title: OOM Prevention
+---
 
 Large Language Model (LLM) inference typically requires significant GPU memory. Unlike traditional virtual memory management in operating systems, CUDA does not support GPU memory swapping. This limitation often leads to CUDA Out-Of-Memory (OOM) errors when GPU memory is insufficient for inference requests. Addressing GPU OOM issues is one of the most challenging aspects of LLM deployment and tuning.
 
-This document explains the mechanisms Kaito implements to prevent CUDA OOM errors in inference workspaces. Since Kaito uses vLLM as its default inference engine, most of the discussion focuses on vLLM use cases.
+This document explains the mechanisms KAITO implements to prevent CUDA OOM errors in inference workspaces. Since KAITO uses vLLM as its default inference engine, most of the discussion focuses on vLLM use cases.
 
 ## Background
 
 The following diagram illustrates the GPU memory layout when serving inference requests using the vLLM engine:
 
-<div align="left">
-  <img src="./img/gpumemlayout.png" width=30% title="GPU memory layout" alt="GPU memory layout">
-</div>
+![GPU memory layout](../static/img/gpumemlayout.png)
 
 The GPU memory is divided into three main components:
 
@@ -22,28 +22,28 @@ The GPU memory is divided into three main components:
 
 The KV cache usage is dynamic and roughly proportional to the context length (the total length of input and output tokens). vLLM requires the KV cache to be sufficient for the full context when serving a request. To enforce this constraint, vLLM uses a `max-model-len` parameter, which defaults to the model's maximum context length (e.g., 128K for Phi-3 models) if not specified. During startup, vLLM performs an internal probing test using a fake request with the specified `max-model-len`. If the configured KV cache cannot handle this request, the vLLM engine fails to start. After startup, any inference request exceeding the `max-model-len` will fail.
 
-## OOM Scenarios and Kaito's Solutions
+## OOM Scenarios and KAITO's Solutions
 
 Based on the above understanding, we identify and address the following potential OOM scenarios:
 
 ### Scenario 1: Insufficient GPU Memory for Model Parameters
 
-Loading model parameters into GPU memory is a fundamental requirement for any LLM engine. Kaito records the required memory size for each supported model based on 16-bit floating-point precision, as documented in the model's HuggingFace documentation. The validation webhook checks if the specified GPU SKU has sufficient aggregated memory to meet these requirements. However, this check is skipped for SKUs not in Kaito's built-in SKU list.
+Loading model parameters into GPU memory is a fundamental requirement for any LLM engine. KAITO records the required memory size for each supported model based on 16-bit floating-point precision, as documented in the model's HuggingFace documentation. The validation webhook checks if the specified GPU SKU has sufficient aggregated memory to meet these requirements. However, this check is skipped for SKUs not in KAITO's built-in SKU list.
 
-> **Note**: Kaito allows users to modify the default vLLM configuration through a custom ConfigMap, which may include model quantization options. In such cases, the webhook's resource check might be overly restrictive because the actual memory footprint will be smaller if 4-bit or 8-bit quantization is enabled. Users can bypass this check by adding the annotation `kaito.sh/bypass-resource-checks: "true"` to the workspace custom resource when using quantized models on GPUs with limited memory.
+> **Note**: KAITO allows users to modify the default vLLM configuration through a custom ConfigMap, which may include model quantization options. In such cases, the webhook's resource check might be overly restrictive because the actual memory footprint will be smaller if 4-bit or 8-bit quantization is enabled. Users can bypass this check by adding the annotation `kaito.sh/bypass-resource-checks: "true"` to the workspace custom resource when using quantized models on GPUs with limited memory.
 
 ### Scenario 2: Insufficient Memory for Other Operations
 
-Setting a high `gpu-memory-utilization` value maximizes GPU memory utilization but can lead to OOM errors during operations like model activation if insufficient memory remains. Since different GPU SKUs have different GPU memory sizes, using a fixed percentage threshold for `gpu-memory-utilization` is suboptimal. Kaito dynamically adjusts the `gpu-memory-utilization` based on the GPU SKU's memory size, ensuring a smaller threshold for GPU SKUs with less than 20GB memory and a larger threshold (capped at 95%) for GPU SKUs with larger memory.
+Setting a high `gpu-memory-utilization` value maximizes GPU memory utilization but can lead to OOM errors during operations like model activation if insufficient memory remains. Since different GPU SKUs have different GPU memory sizes, using a fixed percentage threshold for `gpu-memory-utilization` is suboptimal. KAITO dynamically adjusts the `gpu-memory-utilization` based on the GPU SKU's memory size, ensuring a smaller threshold for GPU SKUs with less than 20GB memory and a larger threshold (capped at 95%) for GPU SKUs with larger memory.
 
 ### Scenario 3: KV Cache Size Insufficient for `max-model-len`
 
 This issue commonly occurs with models supporting large context windows (e.g., 128K). When users set a high `max-model-len` or use models with large default context windows, vLLM's internal probing test may fail on GPUs with limited memory. Typically, finding the optimal `max-model-len` to maximize KV cache utilization without causing OOM errors requires tedious manual searches.
 
-To simplify this process, Kaito implements an external probing test using a binary search algorithm to find a near-optimal `max-model-len` based on available GPU memory. While this test slightly increases service startup time, it eliminates the need for manual tuning. Users can find the calculated `max-model-len` in the inference service pod logs.
+To simplify this process, KAITO implements an external probing test using a binary search algorithm to find a near-optimal `max-model-len` based on available GPU memory. While this test slightly increases service startup time, it eliminates the need for manual tuning. Users can find the calculated `max-model-len` in the inference service pod logs.
 
 > **Note**: The external probing test currently only supports SKUs with a single GPU instance. For SKUs with multiple GPU instances and small per-instance memory (e.g., `Standard_NC12s_v3` with two 16GB v100 instances), users must manually specify the `max-model-len` in the workspace's ConfigMap. The validation webhook enforces this requirement.
 
 ## Generic Memory Usage Reduction
 
-Benchmark tests demonstrate that enabling `expandable_segments=true` in PYTORCH_CUDA_ALLOC_CONF helps reduce memory fragmentation and peak memory consumption, particularly for tuning jobs. Kaito enables this flag by default in workspace pods.
+Benchmark tests demonstrate that enabling `expandable_segments=true` in PYTORCH_CUDA_ALLOC_CONF helps reduce memory fragmentation and peak memory consumption, particularly for tuning jobs. KAITO enables this flag by default in workspace pods.
diff --git a/docs/inference/Monitoring.md b/website/docs/monitoring.md
similarity index 96%
rename from docs/inference/Monitoring.md
rename to website/docs/monitoring.md
index 924fce2..0f74ef6 100644
--- a/docs/inference/Monitoring.md
+++ b/website/docs/monitoring.md
@@ -1,4 +1,6 @@
-# Prometheus Metrics
+---
+title: Monitoring
+---
 
 Inference service exposes Prometheus metrics for monitoring system stats.
 
@@ -8,8 +10,9 @@ vLLM exposes Prometheus metrics at the `/metrics` endpoint. These metrics provid
 
 vLLM version: 0.8.2
 
-> [!NOTE]
-> vLLM V1 engine is now enabled by default. Check [here](https://docs.vllm.ai/en/latest/getting_started/v1_user_guide.html) for details.
+:::note
+vLLM V1 engine is now enabled by default. Check [here](https://docs.vllm.ai/en/latest/getting_started/v1_user_guide.html) for details.
+:::
 
 | Category | Metric Name | Type | Description |
 |----------|-------------|------|-------------|
diff --git a/website/docs/preset-onboarding.md b/website/docs/preset-onboarding.md
new file mode 100644
index 0000000..7ba3054
--- /dev/null
+++ b/website/docs/preset-onboarding.md
@@ -0,0 +1,30 @@
+---
+title: Preset onboarding
+---
+
+This document describes how to add a new supported OSS model in KAITO. The process is designed to allow community users to initiate the request. KAITO maintainers will follow up and deal with managing the model images and guiding the code changes to set up the model preset configurations.
+
+## Step 1: Make a proposal
+
+This step is done by the requestor. The requestor should make a PR to describe the target OSS model following this [template](https://github.com/kaito-project/kaito/blob/main/docs/proposals/YYYYMMDD-model-template.md). The proposal status should be `provisional` in the beginning. KAITO maintainers will review the PR and decide to accept or reject the PR. The PR could be rejected if the target OSS model has low usage, or it has strict license limitations, or it is a relatively small model with limited capabilities.
+
+
+## Step 2: Validate and test the model
+
+This step is done by KAITO maintainers. Based on the information provided in the proposal, KAITO maintainers will download the model and test it using the specified runtime. The entire process is automated via GitHub actions when KAITO maintainers file a PR to add the model to the [supported\_models.yaml](https://github.com/kaito-project/kaito/blob/main/presets/workspace/models/supported_models.yaml).
+
+
+## Step 3: Push model image to MCR
+
+This step is done by KAITO maintainers. If the model license allows, KAITO maintainers will push the model image to MCR, making the image publicly available. This step is skipped if only private access is allowed for the model image. Once this step is done, KAITO maintainers will update the status of the proposal submitted in Step 1 to `ready to integrate`.
+
+## Step 4: Add preset configurations
+
+This step is done by the requestor. The requestor will work on a PR to register the model with preset configurations. The PR will contain code changes to implement a simple inference interface. [Here](https://github.com/kaito-project/kaito/blob/main/presets/workspace/models/falcon/model.go) is an existing example. In the same PR, or a separate PR, the status of the proposal status should be updated to `integrated`.
+
+## Step 5: Add an E2E test
+
+This step is done by the requestor. A new e2e test should be added to [here](https://github.com/kaito-project/kaito/blob/main/test/e2e/preset_test.go) which ensures the inference service is up and running with preset configurations.
+
+
+After all the above are done, a new model becomes available in KAITO.
diff --git a/website/docs/presets.md b/website/docs/presets.md
new file mode 100644
index 0000000..8b6011d
--- /dev/null
+++ b/website/docs/presets.md
@@ -0,0 +1,24 @@
+---
+title: Presets
+---
+
+The current supported model families with preset configurations are listed below.
+
+| Model Family                                | Compatible KAITO Versions |
+|---------------------------------------------|---------------------------|
+| [falcon](https://github.com/kaito-project/kaito/tree/main/presets/workspace/models/falcon)         | v0.0.1+                   |
+| [mistral](https://github.com/kaito-project/kaito/tree/main/presets/workspace/models/mistral)       | v0.2.0+                   |
+| [phi2](https://github.com/kaito-project/kaito/tree/main/presets/workspace/models/phi2)             | v0.2.0+                   |
+| [phi3](https://github.com/kaito-project/kaito/tree/main/presets/workspace/models/phi3)             | v0.3.0+                   |
+| [phi4](https://github.com/kaito-project/kaito/tree/main/presets/workspace/models/phi4)             | v0.4.5+                   |
+| [qwen7b](https://github.com/kaito-project/kaito/tree/main/presets/workspace/models/qwen)           | v0.4.1+                   |
+| [qwen32b](https://github.com/kaito-project/kaito/tree/main/presets/workspace/models/qwen)          | v0.4.5+                   |
+| [llama3](https://github.com/kaito-project/kaito/tree/main/presets/workspace/models/llama3)         | v0.4.6+                   |
+
+## Validation
+
+Each preset model has its own hardware requirements in terms of GPU count and GPU memory defined in the respective `model.go` file. KAITO controller performs a validation check of whether the specified SKU and node count are sufficient to run the model or not. In case the provided SKU is not in the known list, the controller bypasses the validation check which means users need to ensure the model can run with the provided SKU.
+
+## Distributed inference
+
+For models that support distributed inference, when the node count is larger than one, [torch distributed elastic](https://pytorch.org/docs/stable/distributed.elastic.html) is configured with master/worker pods running in multiple nodes and the service endpoint is the master pod.
diff --git a/website/docs/proposals.md b/website/docs/proposals.md
new file mode 100644
index 0000000..f6bb65b
--- /dev/null
+++ b/website/docs/proposals.md
@@ -0,0 +1,56 @@
+---
+title: Proposals
+---
+
+This section contains proposals for adding new models to KAITO. Each proposal describes the process of evaluating and integrating new OSS models into the KAITO ecosystem.
+
+## Proposal Template
+
+Before creating a new model proposal, please use the following template: [Model Proposal Template](./proposals/YYYYMMDD-model-template)
+
+## Current Proposals
+
+Below are the current model proposals in various stages of integration:
+
+### Provisional Status
+- [Llama 3.3 70B Instruct](./proposals/20250529-llama-3.3-70b-instruct.md) - Meta's multilingual instruction-tuned 70B model
+- [Qwen2.5 Coder](./proposals/20250103-qwen2.5-coder.md) - Qwen2.5 series for code generation
+- [Phi-4 Instruct](./proposals/20241212-phi4-instruct.md) - Microsoft's latest Phi-4 instruction-tuned model
+- [Distributed Inference](./proposals/20250325-distributed-inference.md) - Support for distributed inference across multiple GPUs
+- [Model as OCI Artifacts](./proposals/20250609-model-as-oci-artifacts.md) - Packaging models as OCI artifacts
+
+### Integrated Status
+- [Mistral Instruct](./proposals/20240205-mistral-instruct.md) - Mistral AI's instruction-tuned model
+- [Mistral](./proposals/20240205-mistral.md) - Base Mistral model
+- [Phi-2](./proposals/20240206-phi-2.md) - Microsoft's Phi-2 small language model
+- [Phi-3 Instruct](./proposals/20240527-phi3-instruct.md) - Microsoft's Phi-3 instruction-tuned model
+
+## Proposal Process
+
+For detailed information about the model onboarding process, see the [Model Onboarding Guide](./preset-onboarding.md).
+
+### Step 1: Create a Proposal
+Use the model proposal template to describe the target OSS model, including licensing, usage statistics, and technical requirements.
+
+### Step 2: Model Validation
+KAITO maintainers validate and test the proposed model using the specified runtime.
+
+### Step 3: Image Publishing
+If licensing allows, model images are published to Microsoft Container Registry (MCR).
+
+### Step 4: Integration
+Implement preset configurations and inference interfaces for the model.
+
+### Step 5: Testing
+Add comprehensive E2E tests to ensure the model works correctly with KAITO.
+
+## Contributing a Proposal
+
+To contribute a new model proposal:
+
+1. Fork the KAITO repository
+2. Copy the [model template](./proposals/YYYYMMDD-model-template) to `website/docs/proposals/YYYYMMDD-<model-name>.md`
+3. Fill out all required sections
+4. Submit a pull request for review
+
+The proposal status will be updated as it progresses through the integration pipeline.
diff --git a/docs/proposals/20240205-mistral-instruct.md b/website/docs/proposals/20240205-mistral-instruct.md
similarity index 95%
rename from docs/proposals/20240205-mistral-instruct.md
rename to website/docs/proposals/20240205-mistral-instruct.md
index c42723a..1521ace 100644
--- a/docs/proposals/20240205-mistral-instruct.md
+++ b/website/docs/proposals/20240205-mistral-instruct.md
@@ -3,21 +3,21 @@ title: Proposal for Mistral model support
 authors:
   - "Ishaan Sehgal"
 reviewers:
-  - "Kaito contributor"
+  - "KAITO contributor"
 creation-date: 2024-02-02
 last-updated: 2024-02-02
 status: provisional
 ---
 # Title
-Add Mistral-7B-Instruct-v0.2 to Kaito supported model list.
+Add Mistral-7B-Instruct-v0.2 to KAITO supported model list.
 
 ## Glossary
 N/A
 
 ## Summary
 
-- **Model description**: Launched last September, Mistral-7B has quickly become a standout among large language models (LLMs) with its 7.3B parameters. It surpasses models nearly twice its size in benchmarks, showcasing its efficiency and cost-effectiveness. Mistral has also forged strategic alliances with major cloud platforms including Azure, AWS, and GCP. For more information, refer to the [Mistral-7B Documentation](https://docs.mistral.ai/) and access the model on [Hugging Face](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). The newly introduced Mistral-7B-Instruct-v0.1 is an instruct fine-tuned iteration of the original, optimized for understanding and executing specific instructions. This enhances mistral's utility in conversational applications.  
-- **Model usage statistics**: In the past month, Mistral-7B-Instruct-v0.1 has garnered 424,580 downloads on Hugging Face, reflecting its widespread popularity. Google Trends data shows a high level of search interest in <!-- markdown-link-check-disable --> ["mistral ai 7b"](https://trends.google.com/trends/explore?q=mistral%20ai%207b), <!-- markdown-link-check-enable --> indicating strong market curiosity. 
+- **Model description**: Launched last September, Mistral-7B has quickly become a standout among large language models (LLMs) with its 7.3B parameters. It surpasses models nearly twice its size in benchmarks, showcasing its efficiency and cost-effectiveness. Mistral has also forged strategic alliances with major cloud platforms including Azure, AWS, and GCP. For more information, refer to the [Mistral-7B Documentation](https://docs.mistral.ai/) and access the model on [Hugging Face](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). The newly introduced Mistral-7B-Instruct-v0.1 is an instruct fine-tuned iteration of the original, optimized for understanding and executing specific instructions. This enhances mistral's utility in conversational applications.
+- **Model usage statistics**: In the past month, Mistral-7B-Instruct-v0.1 has garnered 424,580 downloads on Hugging Face, reflecting its widespread popularity. Google Trends data shows a high level of search interest in <!-- markdown-link-check-disable --> ["mistral ai 7b"](https://trends.google.com/trends/explore?q=mistral%20ai%207b), <!-- markdown-link-check-enable --> indicating strong market curiosity.
 - **Model license**: Mistral-7B-Instruct-v0.1 is distributed under the Apache 2.0 license, ensuring broad usability and modification rights.
 
 ## Requirements
diff --git a/docs/proposals/20240205-mistral.md b/website/docs/proposals/20240205-mistral.md
similarity index 96%
rename from docs/proposals/20240205-mistral.md
rename to website/docs/proposals/20240205-mistral.md
index fac51b6..9add8b5 100644
--- a/docs/proposals/20240205-mistral.md
+++ b/website/docs/proposals/20240205-mistral.md
@@ -3,13 +3,13 @@ title: Proposal for Mistral model support
 authors:
   - "Ishaan Sehgal"
 reviewers:
-  - "Kaito contributor"
+  - "KAITO contributor"
 creation-date: 2024-02-02
 last-updated: 2024-02-02
 status: provisional
 ---
 # Title
-Add mistral-7b to Kaito supported model list.
+Add mistral-7b to KAITO supported model list.
 
 ## Glossary
 N/A
@@ -17,7 +17,7 @@ N/A
 ## Summary
 
 - **Model description**: Launched last September, Mistral-7B has quickly become a standout among large language models (LLMs) with its 7.3B parameters. It surpasses models nearly twice its size in benchmarks, showcasing its efficiency and cost-effectiveness. Mistral has also forged strategic alliances with major cloud platforms including Azure, AWS, and GCP. For more information, refer to the [Mistral-7B Documentation](https://docs.mistral.ai/) and access the model on [Hugging Face](https://huggingface.co/mistralai/Mistral-7B-v0.1).
-- **Model usage statistics**: In the past month, Mistral-7B has garnered 848,171 downloads on Hugging Face, reflecting its widespread popularity. Google Trends data shows a high level of search interest in <!-- markdown-link-check-disable --> ["mistral ai 7b"](https://trends.google.com/trends/explore?q=mistral%20ai%207b), <!-- markdown-link-check-enable -->indicating strong market curiosity. 
+- **Model usage statistics**: In the past month, Mistral-7B has garnered 848,171 downloads on Hugging Face, reflecting its widespread popularity. Google Trends data shows a high level of search interest in <!-- markdown-link-check-disable --> ["mistral ai 7b"](https://trends.google.com/trends/explore?q=mistral%20ai%207b), <!-- markdown-link-check-enable -->indicating strong market curiosity.
 - **Model license**: Mistral-7B is distributed under the Apache 2.0 license, ensuring broad usability and modification rights.
 
 ## Requirements
diff --git a/docs/proposals/20240206-phi-2.md b/website/docs/proposals/20240206-phi-2.md
similarity index 96%
rename from docs/proposals/20240206-phi-2.md
rename to website/docs/proposals/20240206-phi-2.md
index e0c8a2e..b1106f5 100644
--- a/docs/proposals/20240206-phi-2.md
+++ b/website/docs/proposals/20240206-phi-2.md
@@ -3,13 +3,13 @@ title: Proposal for Mistral model support
 authors:
   - "Ishaan Sehgal"
 reviewers:
-  - "Kaito contributor"
+  - "KAITO contributor"
 creation-date: 2024-02-06
 last-updated: 2024-02-06
 status: provisional
 ---
 # Title
-Add phi-2 to Kaito supported model list.
+Add phi-2 to KAITO supported model list.
 
 ## Glossary
 N/A
diff --git a/docs/proposals/20240527-phi3-instruct.md b/website/docs/proposals/20240527-phi3-instruct.md
similarity index 95%
rename from docs/proposals/20240527-phi3-instruct.md
rename to website/docs/proposals/20240527-phi3-instruct.md
index 0a7efe6..859d1fb 100644
--- a/docs/proposals/20240527-phi3-instruct.md
+++ b/website/docs/proposals/20240527-phi3-instruct.md
@@ -3,14 +3,14 @@ title: Proposal for new model support
 authors:
   - "Justin Joy"
 reviewers:
-  - "Kaito contributor"
+  - "KAITO contributor"
 creation-date: 2024-05-27
 last-updated: 2024-05-27
 status: provisional
 ---
 
 # Title
-Add Phi-3 Medium Models to Kaito supported model list
+Add Phi-3 Medium Models to KAITO supported model list
 
 ## Glossary
 N/A
diff --git a/docs/proposals/20241212-phi4-instruct.md b/website/docs/proposals/20241212-phi4-instruct.md
similarity index 92%
rename from docs/proposals/20241212-phi4-instruct.md
rename to website/docs/proposals/20241212-phi4-instruct.md
index db1cd2b..2d4ae2b 100644
--- a/docs/proposals/20241212-phi4-instruct.md
+++ b/website/docs/proposals/20241212-phi4-instruct.md
@@ -3,7 +3,7 @@ title: Proposal for support of Microsoft's Phi-4
 authors:
   - "Suraj Deshmukh"
 reviewers:
-  - "Kaito contributor"
+  - "KAITO contributor"
 creation-date: 2024-12-12
 last-updated: 2024-12-12
 status: provisional
@@ -11,7 +11,7 @@ status: provisional
 
 # Title
 
-Add official support for Microsoft's Phi-4 models in Kaito.
+Add official support for Microsoft's Phi-4 models in KAITO.
 
 ## Summary
 
@@ -27,7 +27,7 @@ The following table describes the basic model characteristics and the resource r
 |------------------|------------------------------------------|
 | Family name      | Phi-4                                    |
 | Type             | conversational                           |
-| Download site    | <https://huggingface.co/microsoft/phi-4> |
+| Download site    | [https://huggingface.co/microsoft/phi-4](https://huggingface.co/microsoft/phi-4) |
 | Version          | f957856cd926f9d681b14153374d755dd97e45ed |
 | Storage size     | 30 GB                                    |
 | GPU count        | 01 GPU                                   |
diff --git a/docs/proposals/20250103-qwen2.5-coder.md b/website/docs/proposals/20250103-qwen2.5-coder.md
similarity index 94%
rename from docs/proposals/20250103-qwen2.5-coder.md
rename to website/docs/proposals/20250103-qwen2.5-coder.md
index be49509..f6188fe 100644
--- a/docs/proposals/20250103-qwen2.5-coder.md
+++ b/website/docs/proposals/20250103-qwen2.5-coder.md
@@ -3,21 +3,21 @@ title: Proposal for new model support
 authors:
   - "Qinghui Zhuang"
 reviewers:
-  - "Kaito contributor"
+  - "KAITO contributor"
 creation-date: 2025-01-03
 last-updated: 2025-01-03
 status: provisional
 ---
 
 # Title
-Add Qwen 2.5 Coder to Kaito supported model list
+Add Qwen 2.5 Coder to KAITO supported model list
 
 ## Glossary
 N/A
 
 ## Summary
 - **Model description**: Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). It brings significantly improvements in code generation, code reasoning and code fixing with long-context support up to 128K tokens. For more information, refer to the [Qwen2.5 Documentation](https://qwenlm.github.io/blog/qwen2.5/) and access the model on [Hugging Face](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct).
-- **Model usage statistics**: In the past month, Qwen2.5-Coder-7B-Instruct has garnered 118,568 downloads on Hugging Face, reflecting its widespread popularity. Google Trends data shows a high level of search interest in <!-- markdown-link-check-disable --> ["qwen2.5"](https://trends.google.com/trends/explore?q=qwen2.5), <!-- markdown-link-check-enable --> indicating strong market curiosity. 
+- **Model usage statistics**: In the past month, Qwen2.5-Coder-7B-Instruct has garnered 118,568 downloads on Hugging Face, reflecting its widespread popularity. Google Trends data shows a high level of search interest in <!-- markdown-link-check-disable --> ["qwen2.5"](https://trends.google.com/trends/explore?q=qwen2.5), <!-- markdown-link-check-enable --> indicating strong market curiosity.
 - **Model license**: Qwen2.5-Coder series is distributed under the Apache 2.0 license, ensuring broad usability and modification rights.
 
 ## Requirements
diff --git a/docs/proposals/20250325-distributed-inference.md b/website/docs/proposals/20250325-distributed-inference.md
similarity index 94%
rename from docs/proposals/20250325-distributed-inference.md
rename to website/docs/proposals/20250325-distributed-inference.md
index d862c86..2a3562c 100644
--- a/docs/proposals/20250325-distributed-inference.md
+++ b/website/docs/proposals/20250325-distributed-inference.md
@@ -3,7 +3,7 @@ title: Distributed Inference
 authors:
   - chewong
 reviewers:
-  - Kaito contributor
+  - KAITO contributor
 creation-date: 2025-03-25
 last-updated: 2025-05-01
 status: provisional
@@ -15,18 +15,18 @@ Distributed Inference
 
 ## Summary
 
-While Kaito excels with single-GPU models, the increasing size of state-of-the-art models necessitates multi-node distributed inference capabilities. Models with hundreds of billions of parameters often exceed the memory capacity of even the largest single nodes available today.
+While KAITO excels with single-GPU models, the increasing size of state-of-the-art models necessitates multi-node distributed inference capabilities. Models with hundreds of billions of parameters often exceed the memory capacity of even the largest single nodes available today.
 
-The default vLLM runtime lacks multi-node support within Kaito since its adoption in January 2025 ([#823](https://github.com/kaito-project/kaito/pull/823)). This proposal aims to bridge this gap by implementing multi-node distributed inference, primarily focusing on the vLLM runtime. The goal is to enable  deployment of very large preset models across multiple nodes, ensuring Kaito remains capable of serving cutting-edge models while maintaining a consistent user experience.
+The default vLLM runtime lacks multi-node support within KAITO since its adoption in January 2025 ([#823](https://github.com/kaito-project/kaito/pull/823)). This proposal aims to bridge this gap by implementing multi-node distributed inference, primarily focusing on the vLLM runtime. The goal is to enable  deployment of very large preset models across multiple nodes, ensuring KAITO remains capable of serving cutting-edge models while maintaining a consistent user experience.
 
 ## What Is Distributed Inference Anyway?
 
-Distributed inference enables models too large for a single GPU to run across multiple GPUs or nodes. Key strategies relevant to Kaito include:
+Distributed inference enables models too large for a single GPU to run across multiple GPUs or nodes. Key strategies relevant to KAITO include:
 
 1.  **Single-Node Multi-GPU (Tensor Parallelism):** Splits a model across multiple GPUs *within* a single node. Used when a model fits on one node but exceeds a single GPU's memory. Both HuggingFace Transformers and vLLM support this.
-2.  **Multi-Node Multi-GPU (Pipeline and Tensor Parallelism):** Splits a model across multiple nodes, typically using pipeline parallelism between nodes and tensor parallelism within each node. Used for models exceeding a single node's capacity. HuggingFace Transformers supports this via Torch Elastic, but vLLM currently does not in Kaito.
+2.  **Multi-Node Multi-GPU (Pipeline and Tensor Parallelism):** Splits a model across multiple nodes, typically using pipeline parallelism between nodes and tensor parallelism within each node. Used for models exceeding a single node's capacity. HuggingFace Transformers supports this via Torch Elastic, but vLLM currently does not in KAITO.
 
-Kaito's current support for these strategies:
+KAITO's current support for these strategies:
 
 | Strategy              |        vLLM        |
 | --------------------- | :----------------: |
@@ -36,7 +36,7 @@ Kaito's current support for these strategies:
 
 ### Goals
 
-- Implement multi-node distributed inference for preset models using the vLLM inference runtime in Kaito.
+- Implement multi-node distributed inference for preset models using the vLLM inference runtime in KAITO.
 
 ### Non-Goals
 
@@ -75,8 +75,9 @@ resource:
 
 - [x] For models requiring more than one GPU, validate that `# GPUs/instance × workspace.resource.count ≥ Required # GPUs for a preset model`, and `GPU memory * # GPUs ≥ Required model memory`. This validation is performed in the API server when creating or updating a workspace. If the condition is not met, an error message will be returned to the user. This is already implemented in [`api/v1beta1/workspace_validation.go`](https://github.com/kaito-project/kaito/blob/1815428804593eaa94de0d6f78d82b53e85d0137/api/v1beta1/workspace_validation.go#L293-L380) and does not require any changes.
 
-> [!WARNING]
-> Kaito respects the user-defined `workspace.resource.count` and creates exactly that number of nodes in the Workspace through gpu-provisioner. However, based on the chosen instance type, Kaito may deploy the model on fewer nodes than `workspace.resource.count` if the model can fit in fewer nodes. This behavior is intentional to optimize GPU utilization and reduce overall inter-node communication overhead. Users should be mindful of the cost incurred when specifying a high `workspace.resource.count` if the model can fit in fewer nodes.
+:::warning
+KAITO respects the user-defined `workspace.resource.count` and creates exactly that number of nodes in the Workspace through gpu-provisioner. However, based on the chosen instance type, KAITO may deploy the model on fewer nodes than `workspace.resource.count` if the model can fit in fewer nodes. This behavior is intentional to optimize GPU utilization and reduce overall inter-node communication overhead. Users should be mindful of the cost incurred when specifying a high `workspace.resource.count` if the model can fit in fewer nodes.
+:::
 
 ### vLLM Runtime Parameters
 
diff --git a/docs/proposals/20250529-llama-3.3-70b-instruct.md b/website/docs/proposals/20250529-llama-3.3-70b-instruct.md
similarity index 96%
rename from docs/proposals/20250529-llama-3.3-70b-instruct.md
rename to website/docs/proposals/20250529-llama-3.3-70b-instruct.md
index 7671160..c3bb901 100644
--- a/docs/proposals/20250529-llama-3.3-70b-instruct.md
+++ b/website/docs/proposals/20250529-llama-3.3-70b-instruct.md
@@ -1,9 +1,9 @@
 ---
 title: Proposal for new model support
 authors:
-  - @chewong
+  - "@chewong"
 reviewers:
-  - "Kaito contributor"
+  - "KAITO contributor"
 creation-date: 2025-05-29
 last-updated: 2025-05-29
 status: provisional
@@ -11,7 +11,7 @@ status: provisional
 
 # Title
 
-Add [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) to Kaito supported model list
+Add [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) to KAITO supported model list
 
 ## Glossary
 
@@ -68,7 +68,7 @@ The following table describes the basic model characteristics and the resource r
 | Total GPU memory | 320GB (~16k context length, more GPUs required for longer context lengths)                                                                           |
 | Per GPU memory   | 80GB                                                                                                                                                 |
 
-![Requirements](../img/llama-3.3-70b-instruct.png)
+![Requirements](../../static/img/llama-3.3-70b-instruct.png)
 
 ## Runtimes
 
diff --git a/docs/proposals/20250609-model-as-oci-artifacts.md b/website/docs/proposals/20250609-model-as-oci-artifacts.md
similarity index 84%
rename from docs/proposals/20250609-model-as-oci-artifacts.md
rename to website/docs/proposals/20250609-model-as-oci-artifacts.md
index fa55bde..ef147e7 100644
--- a/docs/proposals/20250609-model-as-oci-artifacts.md
+++ b/website/docs/proposals/20250609-model-as-oci-artifacts.md
@@ -3,54 +3,19 @@ title: Distributing LLM Model Files as OCI Artifacts
 authors:
   - zhuangqh
 reviewers:
-  - Kaito contributor
+  - KAITO contributor
 creation-date: 2025-06-09
 last-updated: 2025-06-09
 status: provisional
 ---
 
-# Distributing LLM Model Files as OCI Artifacts
-
-## Table of Contents
-
-<!-- toc -->
-- [Summary](#summary)
-- [Motivation](#motivation)
-  - [Image Building Challenges](#image-building-challenges)
-  - [Image Pulling Inefficiency](#image-pulling-inefficiency)
-    - [Containerd Image Pull Process](#containerd-image-pull-process)
-  - [Goals](#goals)
-  - [Non-Goals](#non-goals)
-- [Proposal](#proposal)
-  - [Build Image Using ORAS Push](#build-image-using-oras-push)
-  - [Use Zstd Instead of Gzip for Compression](#use-zstd-instead-of-gzip-for-compression)
-  - [Shipping Model Files as OCI Artifacts](#shipping-model-files-as-oci-artifacts)
-- [Design Details](#design-details)
-  - [OCI Artifacts vs OCI Image](#oci-artifacts-vs-oci-image)
-  - [OCI Registries Compatibility](#oci-registries-compatibility)
-  - [Model Files downloading process](#model-files-downloading-process)
-- [Production Readiness Review Questionnaire](#production-readiness-review-questionnaire)
-  - [Feature Enablement and Rollback](#feature-enablement-and-rollback)
-  - [Rollout, Upgrade and Rollback Planning](#rollout-upgrade-and-rollback-planning)
-  - [Monitoring Requirements](#monitoring-requirements)
-  - [Dependencies](#dependencies)
-  - [Scalability](#scalability)
-  - [Troubleshooting](#troubleshooting)
-- [Implementation History](#implementation-history)
-- [Alternatives](#alternatives)
-  - [Split Files into Multiple Layers](#split-files-into-multiple-layers)
-  - [Live Download from Hugging Face](#live-download-from-hugging-face)
-- [Test Plan](#test-plan)
-  - [Experimental Setup and Results](#experimental-setup-and-results)
-<!-- /toc -->
-
 ## Summary
 
-The exponential growth and adoption of Large Language Models (LLMs) have revolutionized AI-driven applications across industries. However, distributing these models effectively remains a significant challenge.  
+The exponential growth and adoption of Large Language Models (LLMs) have revolutionized AI-driven applications across industries. However, distributing these models effectively remains a significant challenge.
 
-Currently, KAITO employs a solution where the runtime library and model files are packaged within a single image. This method ensures a reliable and self-contained environment, particularly effective for distributing small models.	 
+Currently, KAITO employs a solution where the runtime library and model files are packaged within a single image. This method ensures a reliable and self-contained environment, particularly effective for distributing small models.
 
-As large language models grow, bundling them within containerized images becomes impractical. Using Open Container Initiative (OCI) Artifacts offers a scalable and efficient alternative for packaging and deployment. 
+As large language models grow, bundling them within containerized images becomes impractical. Using Open Container Initiative (OCI) Artifacts offers a scalable and efficient alternative for packaging and deployment.
 
 ## Motivation
 
@@ -58,7 +23,7 @@ While containerized environments are easy to use, they face challenges in mainta
 
 ### Image Building Challenges
 
-With the ongoing development of KAITO, we now host 19 models. The base images are frequently updated due to vulnerability fixes and feature requests. Each time the base image is updated, we need to rebuild the image for every model. 
+With the ongoing development of KAITO, we now host 19 models. The base images are frequently updated due to vulnerability fixes and feature requests. Each time the base image is updated, we need to rebuild the image for every model.
 
 Although the model files remain unchanged, Docker image builds are still time-consuming because these large files are included in the build context unnecessarily. For instance, building the Falcon-40B image takes nearly 2 hours to complete.
 
@@ -70,12 +35,12 @@ Image pulling is also time-consuming, even for smaller models. For example, it t
 
 #### Containerd Image Pull Process
 
-When containerd pulls an image, the operation is divided into four distinct parts:  
+When containerd pulls an image, the operation is divided into four distinct parts:
 
 1. Downloading layer data from the registry
 2. Decompressing the layer if necessary
 3. Checking sha256 digest
-4. Unpacking the layer, applying changes to the snapshot 
+4. Unpacking the layer, applying changes to the snapshot
 
 Interestingly, testing reveals that the download phase accounts for only 30% of the total time spent during the process. This highlights the significance of optimizing subsequent phases to improve overall efficiency in handling large, containerized artifacts.
 
@@ -119,13 +84,13 @@ Key benefit: You only need to build model images once, then they can be reused a
 
 ## Design Details
 
-### OCI Artifacts vs OCI Image 
+### OCI Artifacts vs OCI Image
 
-The Open Container Initiative (OCI) defines the specifications and standards for container technologies. This includes the API for working with container registries, known formally as the OCI Distribution Specification. (a.k.a. the "distribution-spec"). 
+The Open Container Initiative (OCI) defines the specifications and standards for container technologies. This includes the API for working with container registries, known formally as the OCI Distribution Specification. (a.k.a. the "distribution-spec").
 
-Specifically, OCI Image Manifests have a required field known as config.mediaType. According to the guidelines provided by OCI Artifacts, this field provides the ability to differentiate between various types of artifacts. 
+Specifically, OCI Image Manifests have a required field known as config.mediaType. According to the guidelines provided by OCI Artifacts, this field provides the ability to differentiate between various types of artifacts.
 
-An OCI image is a subset of OCI artifacts, accepting only specific mediatypes. 
+An OCI image is a subset of OCI artifacts, accepting only specific mediatypes.
 
 ORAS is a tool for managing OCI artifacts, including pushing, pulling, and handling metadata in OCI registries. Note that OCI artifacts might not be fully compatible with container engines.
 Currently, cri-o supports general OCI artifacts, while [containerd does not](https://github.com/containerd/containerd/issues/11381#issuecomment-2917050414).
@@ -267,7 +232,7 @@ This approach maintains compatibility with standard container runtimes but still
 
 ### Live Download from Hugging Face
 
-Based on our testing, the download bandwidth from ACR is comparable to that of the Hugging Face repository. 
+Based on our testing, the download bandwidth from ACR is comparable to that of the Hugging Face repository.
 
 While Hugging Face provides comparable bandwidth, ACR is more reliable and better suited for production deployments due to its integration, access control, and enterprise-grade support.
 
@@ -287,6 +252,5 @@ While Hugging Face provides comparable bandwidth, ACR is more reliable and bette
 | multilayer-tar-zstd | aimodelsregistrytest.azurecr.io/phi-4-mini-instruct:phi4zstd | Split model files into individual layers. Each layer compressed using zstd. |
 | baseImage+OCI-artifacts | aimodelsregistrytest.azurecr.io/base:0.0.3 and aimodelsregistrytest.azurecr.io/phi-4-mini-instruct:model | Pull base image first. Then download model files using oras pull. |
 
-<div align="center">
-  <img src="../img/model-as-oci-artifacts-evaluation.png" width=50% title="Model as OCI Artifacts Evaluation" alt="Model as OCI Artifacts Evaluation">
-</div>
\ No newline at end of file
+
+![Model as OCI Artifacts Evaluation](../../static/img/model-as-oci-artifacts-evaluation.png)
\ No newline at end of file
diff --git a/docs/proposals/YYYYMMDD-model-template.md b/website/docs/proposals/YYYYMMDD-model-template.md
similarity index 87%
rename from docs/proposals/YYYYMMDD-model-template.md
rename to website/docs/proposals/YYYYMMDD-model-template.md
index b09b03b..ab47dd4 100644
--- a/docs/proposals/YYYYMMDD-model-template.md
+++ b/website/docs/proposals/YYYYMMDD-model-template.md
@@ -1,16 +1,16 @@
 ---
 title: Proposal for new model support
 authors:
-  - "Kaito contributor"
+  - "KAITO contributor"
 reviewers:
-  - "Kaito contributor"
+  - "KAITO contributor"
 creation-date: yyyy-mm-dd
 last-updated: yyyy-mm-dd
 status: provisional|ready to integrate|integrated
 ---
 
 # Title
-- Keep it simple and descriptive. E.g., Add XXXX (model name) to Kaito supported model list.
+- Keep it simple and descriptive. E.g., Add XXXX (model name) to KAITO supported model list.
 
 <!-- BEGIN Remove before PR -->
 To get started with this template:
@@ -23,7 +23,7 @@ To get started with this template:
 The `Metadata` section above is intended to support the creation of tooling around the proposal process.
 This will be a YAML section that is fenced as a code block.
 
-Note: if the intention is to add a model family that includes multiple models with different parameter sizes to Kaito, the PR author needs to create individual PR for **EACH** model, i.e., one proposal for one model specification.
+Note: if the intention is to add a model family that includes multiple models with different parameter sizes to KAITO, the PR author needs to create individual PR for **EACH** model, i.e., one proposal for one model specification.
 
 <!-- END Remove before PR -->
 
@@ -33,13 +33,13 @@ If this proposal uses terms that need clarifications, define and describe them h
 
 ## Summary
 
-The `Summary` section is important for justifying the need of adding the proposed inference model in Kaito. This section needs to provide the following information.
+The `Summary` section is important for justifying the need of adding the proposed inference model in KAITO. This section needs to provide the following information.
 - **Model description**: *What does the model do? Where are the official docs if any?*
 - **Model usage statistics**: *What is the current download count? (source: e.g., huggingface or model website), or any statistics that indicate the model popularity, e.g., huggingface trending.*
-- **Model license**: Note that for models with Apache 2 or MIT licenses, if the proposal is approved, the model images can be built by Kaito maintainers and hosted in public MCR. Otherwise, the Kaito users need to build the model images themselves in their private repositories.
+- **Model license**: Note that for models with Apache 2 or MIT licenses, if the proposal is approved, the model images can be built by KAITO maintainers and hosted in public MCR. Otherwise, the KAITO users need to build the model images themselves in their private repositories.
 
 <!-- BEGIN Remove before PR -->
-There is always a cost of maintaining preset configurations and model images in Kaito. Hence, we prioritize supporting models with high popularities or emerging community interests first.
+There is always a cost of maintaining preset configurations and model images in KAITO. Hence, we prioritize supporting models with high popularities or emerging community interests first.
 <!-- END Remove before PR -->
 
 ## Requirements
@@ -64,7 +64,7 @@ This section describes how to configure the runtime framework to support the inf
 
 | Options | Notes|
 |----|----|
-| Runtime | E.g., huggingface transformer, or onnx. Kaito can support multiple runtimes (details TBD). |
+| Runtime | E.g., huggingface transformer, or onnx. KAITO can support multiple runtimes (details TBD). |
 | Distributed Inference| True/False. This indicates whether torch elastic should be configured or not. |
 | Custom configurations| Describe custom configurations that will be used in the model deployment as defaults. For example, see [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch#custom-configurations) for customizing the huggingface accelerate library.|
 
diff --git a/website/docs/quick-start.md b/website/docs/quick-start.md
new file mode 100644
index 0000000..eb681c7
--- /dev/null
+++ b/website/docs/quick-start.md
@@ -0,0 +1,105 @@
+---
+title: Quick Start
+---
+
+After installing KAITO, you can quickly deploy a phi-3.5-mini-instruct inference service to get started.
+
+## Prerequisites
+
+- A Kubernetes cluster with KAITO installed (see [Installation](installation))
+- `kubectl` configured to access your cluster
+
+## Deploy Your First Model
+
+Let's start by deploying a phi-3.5-mini-instruct model using a workspace configuration:
+
+```yaml title="phi-3.5-workspace.yaml"
+apiVersion: kaito.sh/v1beta1
+kind: Workspace
+metadata:
+  name: workspace-phi-3-5-mini
+resource:
+  instanceType: "Standard_NC24ads_A100_v4"
+  labelSelector:
+    matchLabels:
+      apps: phi-3-5
+inference:
+  preset:
+    name: phi-3.5-mini-instruct
+```
+
+Apply this configuration to your cluster:
+
+```bash
+kubectl apply -f phi-3.5-workspace.yaml
+```
+
+## Monitor Deployment
+
+Track the workspace status to see when the model has been deployed successfully:
+
+```bash
+kubectl get workspace workspace-phi-3-5-mini
+```
+
+When the `WORKSPACEREADY` column becomes `True`, the model has been deployed successfully:
+
+```bash
+NAME                     INSTANCE                   RESOURCEREADY   INFERENCEREADY   JOBSTARTED   WORKSPACESUCCEEDED   AGE
+workspace-phi-3-5-mini   Standard_NC24ads_A100_v4   True            True                          True                 4h15m
+```
+
+## Test the Model
+
+Find the inference service's cluster IP and test it using a temporary curl pod:
+
+```bash
+# Get the service endpoint
+kubectl get svc workspace-phi-3-5-mini
+export CLUSTERIP=$(kubectl get svc workspace-phi-3-5-mini -o jsonpath="{.spec.clusterIPs[0]}")
+
+# List available models
+kubectl run -it --rm --restart=Never curl --image=curlimages/curl -- curl -s http://$CLUSTERIP/v1/models | jq
+```
+
+You should see output similar to:
+
+```json
+{
+  "object": "list",
+  "data": [
+    {
+      "id": "phi-3.5-mini-instruct",
+      "object": "model",
+      "created": 1733370094,
+      "owned_by": "vllm",
+      "root": "/workspace/vllm/weights",
+      "parent": null,
+      "max_model_len": 16384
+    }
+  ]
+}
+```
+
+## Make an Inference Call
+
+Now make an inference call using the model:
+
+```bash
+kubectl run -it --rm --restart=Never curl --image=curlimages/curl -- curl -X POST http://$CLUSTERIP/v1/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "phi-3.5-mini-instruct",
+    "prompt": "What is kubernetes?",
+    "max_tokens": 50,
+    "temperature": 0
+  }'
+```
+
+## Next Steps
+
+🎉 Congratulations! You've successfully deployed and tested your first model with KAITO.
+
+- **Learn More**: Explore the full range of supported models in the [presets documentation](https://github.com/kaito-project/kaito/tree/main/presets)
+- **Advanced Usage**: Learn about [workspace configurations](https://github.com/kaito-project/kaito/blob/main/api/v1alpha1/workspace_types.go)
+- **Contributing**: See how to [contribute new models](https://github.com/kaito-project/kaito/blob/main/docs/How-to-add-new-models.md)
diff --git a/docs/RAG/README.md b/website/docs/rag.md
similarity index 71%
rename from docs/RAG/README.md
rename to website/docs/rag.md
index f484b66..815737a 100644
--- a/docs/RAG/README.md
+++ b/website/docs/rag.md
@@ -1,79 +1,105 @@
-# Kaito RAGEngine
-
-This document presents how to use the Kaito `ragengine` Custom Resource Definition (CRD) for retrieval-augumented generatoin workflow. By creating a RAGEngine resource, you can quickly stand up a service that indexes documents and queries them in conjunction with an existing LLM inference endpoint—no need to custom-build pipelines. This enables your large language model to answer questions based on your own private content.
-
-## Installation
-
-Please check the installation guidance [here](./RAGEngine-installation.md) for deployment using Azure CLI.
-
-## Usage
-
-### Prerequisite
-Before creating a RAGEngine, ensure you have an accessible model inference endpoint. This endpoint can be:
-
-1.	A model deployed through Kaito Workspace CRD (e.g., a local Hugging Face model, a vLLM instance, etc.).
-2.	An external API (e.g., Huggingface service or other REST-based LLM providers).
-
-### Define the RAGEngine
-Create a YAML manifest defining your RAGEngine. Key fields under spec include:
-
-Embedding: how to generate vector embeddings for your documents. You may choose remote or local (one must be left unset if you pick the other):
-
-```yaml
-embedding:  
-    local:
-      modelID: "BAAI/bge-small-en-v1.5" 
-```
-InferenceService: points to the LLM endpoint that RAGEngine will call for final text generation.
-```yaml
-inferenceService:
-  url: "<inference-url>/v1/completions"
-```
-Users also need to specify the GPU SKU used for inference in the `compute` spec. For example,
-
-```yaml
-apiVersion: kaito.sh/v1alpha1
-kind: RAGEngine
-metadata:
-  name: ragengine-start
-spec:
-  compute:
-    instanceType: "Standard_NC6s_v3"  
-    labelSelector:
-      matchLabels:
-        apps: ragengine-example
-  embedding:  
-    local:
-      modelID: "BAAI/bge-small-en-v1.5"       
-  inferenceService:  
-    url: "<inference-url>/v1/completions" 
-```
-
-### Splitting Documents with CodeSplitter
-
-By default, RAGEngine splits documents into sentences. However, you can instruct the engine to split documents using the `CodeSplitter` (for code-aware chunking) by providing metadata in your API request.
-
-To use the `CodeSplitter`, set the `split_type` to `"code"` and specify the programming language in the `language` field of the document metadata. For example, when calling the RAGEngine API to index documents:
-
-```json
-{
-  "documents": [
-    {
-      "text": "def foo():\n    return 42\n\n# Another function\ndef bar():\n    pass",
-      "metadata": {
-        "split_type": "code",
-        "language": "python"
-      }
-    }
-  ]
-}
-```
-
-This instructs the RAGEngine to use code-aware splitting for the provided document. If `split_type` is not set or set to any other value, sentence splitting will be used by default.
-
-### Apply the Manifest
-After you create your YAML configuration, run:
-```sh
-kubectl apply -f examples/RAG/kaito_ragengine_phi_3.yaml
-```
-
+---
+title: Retrieval-Augmented Generation (RAG)
+---
+
+This document presents how to use the KAITO `ragengine` Custom Resource Definition (CRD) for retrieval-augumented generatoin workflow. By creating a RAGEngine resource, you can quickly stand up a service that indexes documents and queries them in conjunction with an existing LLM inference endpoint—no need to custom-build pipelines. This enables your large language model to answer questions based on your own private content.
+
+## Installation
+
+> Be sure you've cloned this repo and followed [kaito workspace installation](./installation.md)
+
+```bash
+helm install ragengine ./charts/kaito/ragengine --namespace kaito-ragengine --create-namespace
+```
+
+## Verify installation
+You can run the following commands to verify the installation of the controllers were successful.
+
+Check status of the Helm chart installations.
+
+```bash
+helm list -n kaito-ragengine
+```
+
+Check status of the `ragengine`.
+
+```bash
+kubectl describe deploy ragengine -n kaito-ragengine
+```
+
+## Clean up
+
+```bash
+helm uninstall kaito-ragengine
+```
+
+## Usage
+
+### Prerequisite
+Before creating a RAGEngine, ensure you have an accessible model inference endpoint. This endpoint can be:
+
+1.	A model deployed through KAITO Workspace CRD (e.g., a local Hugging Face model, a vLLM instance, etc.).
+2.	An external API (e.g., Huggingface service or other REST-based LLM providers).
+
+### Define the RAGEngine
+Create a YAML manifest defining your RAGEngine. Key fields under spec include:
+
+Embedding: how to generate vector embeddings for your documents. You may choose remote or local (one must be left unset if you pick the other):
+
+```yaml
+embedding:
+    local:
+      modelID: "BAAI/bge-small-en-v1.5"
+```
+InferenceService: points to the LLM endpoint that RAGEngine will call for final text generation.
+```yaml
+inferenceService:
+  url: "<inference-url>/v1/completions"
+```
+Users also need to specify the GPU SKU used for inference in the `compute` spec. For example,
+
+```yaml
+apiVersion: kaito.sh/v1alpha1
+kind: RAGEngine
+metadata:
+  name: ragengine-start
+spec:
+  compute:
+    instanceType: "Standard_NC6s_v3"
+    labelSelector:
+      matchLabels:
+        apps: ragengine-example
+  embedding:
+    local:
+      modelID: "BAAI/bge-small-en-v1.5"
+  inferenceService:
+    url: "<inference-url>/v1/completions"
+```
+
+### Splitting Documents with CodeSplitter
+
+By default, RAGEngine splits documents into sentences. However, you can instruct the engine to split documents using the `CodeSplitter` (for code-aware chunking) by providing metadata in your API request.
+
+To use the `CodeSplitter`, set the `split_type` to `"code"` and specify the programming language in the `language` field of the document metadata. For example, when calling the RAGEngine API to index documents:
+
+```json
+{
+  "documents": [
+    {
+      "text": "def foo():\n    return 42\n\n# Another function\ndef bar():\n    pass",
+      "metadata": {
+        "split_type": "code",
+        "language": "python"
+      }
+    }
+  ]
+}
+```
+
+This instructs the RAGEngine to use code-aware splitting for the provided document. If `split_type` is not set or set to any other value, sentence splitting will be used by default.
+
+### Apply the Manifest
+After you create your YAML configuration, run:
+```sh
+kubectl apply -f examples/RAG/kaito_ragengine_phi_3.yaml
+```
diff --git a/docs/tuning/README.md b/website/docs/tuning.md
similarity index 91%
rename from docs/tuning/README.md
rename to website/docs/tuning.md
index b444d34..446165a 100644
--- a/docs/tuning/README.md
+++ b/website/docs/tuning.md
@@ -1,13 +1,16 @@
-# Kaito fine tuning
-This document presents how to use the Kaito `workspace` Custom Resource Definition (CRD) for parameter-efficient fine-tuning (PEFT) of models, how a Kubernetes job is designed to automate the tuning workflow, and several best practices for troubleshooting.
+---
+title: Fine Tuning
+---
+
+This document presents how to use the KAITO `workspace` Custom Resource Definition (CRD) for parameter-efficient fine-tuning (PEFT) of models, how a Kubernetes job is designed to automate the tuning workflow, and several best practices for troubleshooting.
 
 ## Usage
-Kaito tuning APIs allow users to specify supported tuning methods like [LoRA or QLoRA](https://huggingface.co/docs/peft/main/en/conceptual_guides/lora), the input dataset and configuration settings, and the output destination for saving the tuning results. Currently, Kaito supports URL, image 
-and Kubernetes volume as the types of tuning input sources, and image, Kubernetes volume as the types of tuning output destination. 
+KAITO tuning APIs allow users to specify supported tuning methods like [LoRA or QLoRA](https://huggingface.co/docs/peft/main/en/conceptual_guides/lora), the input dataset and configuration settings, and the output destination for saving the tuning results. Currently, KAITO supports URL, image
+and Kubernetes volume as the types of tuning input sources, and image, Kubernetes volume as the types of tuning output destination.
 
 
 ### Tuning workspace
-Here are two examples of using Kaito workspace CRD to define workspaces for tuning different models:
+Here are two examples of using KAITO workspace CRD to define workspaces for tuning different models:
 
 Example 1: Tuning [`phi-3-mini`](../../examples/fine-tuning/kaito_workspace_tuning_phi_3.yaml). This example uses a public dataset specified by a URL in the input.
 
@@ -41,12 +44,12 @@ Example 3: Tuning [`phi-3-mini`](../../examples/fine-tuning/kaito_workspace_tuni
 The detailed `TuningSpec` API definitions can be found [here](https://github.com/kaito-project/kaito/blob/2ccc93daf9d5385649f3f219ff131ee7c9c47f3e/api/v1alpha1/workspace_types.go#L145).
 
 ### Tuning configurations
-Kaito provides default tuning configurations for different tuning methods. They are managed by Kubernetes configmaps.
+KAITO provides default tuning configurations for different tuning methods. They are managed by Kubernetes configmaps.
 - [default LoRA configmap](../../charts/kaito/workspace/templates/lora-params.yaml)
 - [default QLoRA configmap](../../charts/kaito/workspace/templates/qlora-params.yaml)
 
 ## Tuning configmaps
-User can specify a customized configmap via the `Config` field of the `TuningSpec`. The customized configmap should be structured based on the default configmaps provided by Kaito. Please read the following section carefully when attempting to change the default parameters used by Kaito.
+User can specify a customized configmap via the `Config` field of the `TuningSpec`. The customized configmap should be structured based on the default configmaps provided by KAITO. Please read the following section carefully when attempting to change the default parameters used by KAITO.
 
 ### Categorized key parameters
 Note that changing these parameters may largely impact the tuning result. In addition, users can add extra parameters that are not presented in the default configmaps. For a complete list of supported parameters, please refer to the provided huggingface documentation.
@@ -109,14 +112,14 @@ If your dataset is not in one of these formats, it will be passed directly to th
 Note: if you build a container image for the input dataset, please copy the dataset to the **`/data`** directory inside the container.
 
 # Tuning Job
-Kaito uses the Kubernetes **batchv1.job** workload to manage the tuning Pod. When a tuning workspace custom resource is created, the Kaito controller will create a job with the same name as the workspace in the same namespace. The pod structure will be different depending on whether the input and output are specified as URLs, images, or Kubernetes volumes.
+KAITO uses the Kubernetes **batchv1.job** workload to manage the tuning Pod. When a tuning workspace custom resource is created, the KAITO controller will create a job with the same name as the workspace in the same namespace. The pod structure will be different depending on whether the input and output are specified as URLs, images, or Kubernetes volumes.
 
 ## Pod structure when input and output are specified as URLs or images
-To streamline the tuning workflow, Kaito adds two containers in addition to the main container that runs the tuning process. The pod structure is illustrated in Figure 1.
-<div align="left">
-  <img src="../img/kaito-fine-tuning.png" width=40% title="Kaito fine tuning" alt="Kaito fine tuning">
-</div>
-Figure 1. Kaito tuning pod structure.
+To streamline the tuning workflow, KAITO adds two containers in addition to the main container that runs the tuning process. The pod structure is illustrated in Figure 1.
+
+![KAITO fine tuning](../static/img/kaito-fine-tuning.png)
+
+Figure 1. KAITO tuning pod structure.
 
 - Initcontainer `data-downloader`: It downloads the training input dataset from the URLs specified in the tuning spec if needed. If an image is specified in the input, the `data-downloader` container uses the specified image as the container image. This initcontainer ensures the training data is available locally before the training process starts.
 
@@ -127,21 +130,21 @@ Figure 1. Kaito tuning pod structure.
 All three containers use shared local volumes (by mounting the same `EmptyDir` volumes), hence file copies between containers are avoided.
 
 ## Pod structure when input and output are specified as Kubernetes volumes
-When the input and output are specified as Kubernetes volumes, the initcontainer and sidecar container are removed and only the main container is used. Since the input dataset and ouput destination are already available in the specified volumes, the main container can directly access them. 
+When the input and output are specified as Kubernetes volumes, the initcontainer and sidecar container are removed and only the main container is used. Since the input dataset and ouput destination are already available in the specified volumes, the main container can directly access them.
 
-Other than the absence of the init and sidecar containers, the main container is the same as described in the previous section. 
+Other than the absence of the init and sidecar containers, the main container is the same as described in the previous section.
 
 # Troubleshooting
 
 ### Job pod failures
 When the tuning job reaches the failed state, at least one of the above three containers has encountered errors. Users can check the logs of these containers using the `kubectl logs PODNAME -n NAMESPACE -c CONTAINERNAME` command.
 
-For the initcontainer and sidecar container, possible errors include invalid input/output URLs or invalid image pull secrets. Users can fix these problems by updating the workspace custom resource with corrections. The Kaito controller will create a new job using the updated spec.
+For the initcontainer and sidecar container, possible errors include invalid input/output URLs or invalid image pull secrets. Users can fix these problems by updating the workspace custom resource with corrections. The KAITO controller will create a new job using the updated spec.
 
-For the main container, errors may occur when CUDA reports out of GPU memory. Users should reduce the batch size (the default is 1) if it has been customized to a value larger than 1. If the batch size is already 1, the workspace must be recreated using a different GPU SKU with larger GPU memory. Note that Kaito has optimized the training memory usage by dropping the preallocated memory cache. Our internal tests show that the performance impact due to this change is negligible.
+For the main container, errors may occur when CUDA reports out of GPU memory. Users should reduce the batch size (the default is 1) if it has been customized to a value larger than 1. If the batch size is already 1, the workspace must be recreated using a different GPU SKU with larger GPU memory. Note that KAITO has optimized the training memory usage by dropping the preallocated memory cache. Our internal tests show that the performance impact due to this change is negligible.
 
 ### LoraConfig target modules errors
-If you encounter the error: `ValueError: Target modules {'target_module_here'} not found in the base model. Please check the target modules and try again.`, you need to manually specify the target_modules parameter in your Kaito configmap. This is a huggingface requirement.
+If you encounter the error: `ValueError: Target modules {'target_module_here'} not found in the base model. Please check the target modules and try again.`, you need to manually specify the target_modules parameter in your KAITO configmap. This is a huggingface requirement.
 
 This error occurs because the automatic module detection failed for your model. You must identify the specific target modules for your model and explicitly list them in the LoraConfig section. For example, a valid configuration for phi-4-mini-instruct would look like:
 ```yaml
diff --git a/website/docs/usage.md b/website/docs/usage.md
new file mode 100644
index 0000000..8df434a
--- /dev/null
+++ b/website/docs/usage.md
@@ -0,0 +1,9 @@
+---
+title: Usage
+---
+
+The detailed usage for Kaito supported models can be found in [**HERE**](./presets.md). In case users want to deploy their own containerized models, they can provide the pod template in the `inference` field of the workspace custom resource (please see [API definitions](../../api/v1alpha1/workspace_types.go) for details). The controller will create a deployment workload using all provisioned GPU nodes. Note that currently the controller does **NOT** handle automatic model upgrade. It only creates inference workloads based on the preset configurations if the workloads do not exist.
+
+The number of the supported models in Kaito is growing! Please check [this](./preset-onboarding.md) document to see how to add a new supported model.
+
+Starting with version v0.3.0, Kaito supports model fine-tuning and using fine-tuned adapters in the inference service. Refer to the [tuning document](./tuning.md) and [inference document](./inference.md) for more information.
diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js
new file mode 100644
index 0000000..1d61c39
--- /dev/null
+++ b/website/docusaurus.config.js
@@ -0,0 +1,127 @@
+// @ts-check
+// `@type` JSDoc annotations allow editor autocompletion and type checking
+// (when paired with `@ts-check`).
+// There are various equivalent ways to declare your Docusaurus config.
+// See: https://docusaurus.io/docs/api/docusaurus-config
+
+import { themes as prismThemes } from 'prism-react-renderer';
+
+/** @type {import('@docusaurus/types').Config} */
+const config = {
+    title: 'Kaito',
+    tagline: 'Kubernetes AI Toolchain Operator',
+    favicon: 'img/favicon.ico',
+
+    // Set the production url of your site here
+    url: 'https://kaito-project.github.io',
+    // Set the /<baseUrl>/ pathname under which your site is served
+    // For GitHub pages deployment, it is often '/<projectName>/'
+    baseUrl: '/kaito/docs/',
+
+    // GitHub pages deployment config.
+    // If you aren't using GitHub pages, you don't need these.
+    organizationName: 'kaito-project', // Usually your GitHub org/user name.
+    projectName: 'kaito', // Usually your repo name.
+
+    onBrokenLinks: 'throw',
+    onBrokenMarkdownLinks: 'warn',
+
+    // Even if you don't use internationalization, you can use this field to set
+    // useful metadata like html lang. For example, if your site is Chinese, you
+    // may want to replace "en" with "zh-Hans".
+    i18n: {
+        defaultLocale: 'en',
+        locales: ['en'],
+    },
+
+    presets: [
+        [
+            'classic',
+            /** @type {import('@docusaurus/preset-classic').Options} */
+            ({
+                docs: {
+                    routeBasePath: '/',
+                    sidebarPath: './sidebars.js',
+                    // Please change this to your repo.
+                    // Remove this to remove the "edit this page" links.
+                    editUrl:
+                        'https://github.com/kaito-project/kaito/tree/main/website/',
+                },
+                blog: false,
+                theme: {
+                    customCss: './src/css/custom.css',
+                },
+            }),
+        ],
+    ],
+
+    themeConfig:
+        /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
+        ({
+            // Replace with your project's social card
+            image: 'img/kaito-logo.png',
+            navbar: {
+                title: 'Kaito',
+                logo: {
+                    alt: 'Kaito Logo',
+                    src: 'img/kaito-logo.png',
+                },
+                items: [
+                    {
+                        href: 'https://github.com/kaito-project/kaito',
+                        position: 'right',
+                        className: 'header-github-link',
+                        'aria-label': 'GitHub repository',
+                    },
+                ],
+            },
+            footer: {
+                style: 'dark',
+                links: [
+                    {
+                        title: 'Documentation',
+                        items: [
+                            {
+                                label: 'Getting Started',
+                                to: '/',
+                            },
+                            {
+                                label: 'Installation',
+                                to: '/installation',
+                            },
+                        ],
+                    },
+                    {
+                        title: 'Community',
+                        items: [
+                            {
+                                label: 'GitHub',
+                                href: 'https://github.com/kaito-project/kaito',
+                            },
+                            {
+                                label: 'Slack',
+                                href: 'https://join.slack.com/t/kaito-z6a6575/shared_invite/zt-37gh89vw7-odHfqmPRc5oRnDG99SBJNA',
+                            },
+                        ],
+                    },
+                ],
+                copyright: `Copyright © ${new Date().getFullYear()} Kaito Project, Built with Docusaurus.`,
+            },
+            prism: {
+                theme: prismThemes.github,
+                darkTheme: prismThemes.dracula,
+                additionalLanguages: ['bash', 'json', 'yaml', 'go'],
+            },
+            colorMode: {
+                defaultMode: 'light',
+                disableSwitch: false,
+                respectPrefersColorScheme: true,
+            },
+            announcementBar: {
+                id: 'announcementBar-1', // Increment on change
+                content: `⭐️ If you like Kaito, please give it a star on <a target="_blank" rel="noopener noreferrer" href="https://github.com/kaito-project/kaito">GitHub</a>!</a>`,
+            },
+        }),
+};
+
+export default config;
diff --git a/website/package.json b/website/package.json
new file mode 100644
index 0000000..a592567
--- /dev/null
+++ b/website/package.json
@@ -0,0 +1,44 @@
+{
+    "name": "kaito-website",
+    "version": "0.0.0",
+    "private": true,
+    "scripts": {
+        "docusaurus": "docusaurus",
+        "start": "docusaurus start",
+        "build": "docusaurus build",
+        "swizzle": "docusaurus swizzle",
+        "deploy": "docusaurus deploy",
+        "clear": "docusaurus clear",
+        "serve": "docusaurus serve",
+        "write-translations": "docusaurus write-translations",
+        "write-heading-ids": "docusaurus write-heading-ids"
+    },
+    "dependencies": {
+        "@docusaurus/core": "3.1.0",
+        "@docusaurus/preset-classic": "3.1.0",
+        "@mdx-js/react": "^3.0.0",
+        "clsx": "^2.0.0",
+        "prism-react-renderer": "^2.3.0",
+        "react": "^18.0.0",
+        "react-dom": "^18.0.0"
+    },
+    "devDependencies": {
+        "@docusaurus/module-type-aliases": "3.1.0",
+        "@docusaurus/types": "3.1.0"
+    },
+    "browserslist": {
+        "production": [
+            ">0.5%",
+            "not dead",
+            "not op_mini all"
+        ],
+        "development": [
+            "last 3 chrome version",
+            "last 3 firefox version",
+            "last 5 safari version"
+        ]
+    },
+    "engines": {
+        "node": ">=18.0"
+    }
+}
\ No newline at end of file
diff --git a/website/sidebars.js b/website/sidebars.js
new file mode 100644
index 0000000..ad3bb2a
--- /dev/null
+++ b/website/sidebars.js
@@ -0,0 +1,72 @@
+/**
+ * Creating a sidebar enables you to:
+ - create an ordered group of docs
+ - render a sidebar for each doc of that group
+ - provide next/previous navigation
+
+ The sidebars can be generated from the filesystem, or explicitly defined here.
+
+ Create as many sidebars as you want.
+ */
+
+// @ts-check
+
+/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */
+const sidebars = {
+    sidebar: [
+        {
+            type: 'category',
+            label: 'Getting Started',
+            collapsed: false,
+            items: [
+                'intro',
+                'installation',
+                'quick-start',
+                'presets',
+                'usage',
+                'faq'
+            ],
+        },
+        {
+            type: 'category',
+            label: 'Features',
+            collapsed: false,
+            items: [
+                'inference',
+                'tuning',
+                'rag',
+                'custom-model',
+            ],
+        },
+        {
+            type: 'category',
+            label: 'Cloud Providers',
+            collapsed: false,
+            items: [
+                'aws',
+            ],
+        },
+        {
+            type: 'category',
+            label: 'Operations',
+            collapsed: false,
+            items: [
+                'monitoring',
+                'kaito-oom-prevention',
+                'kaito-on-byo-gpu-nodes',
+            ],
+        },
+        {
+            type: 'category',
+            label: 'Contributing',
+            collapsed: false,
+            items: [
+                'contributing',
+                'preset-onboarding',
+                'proposals',
+            ],
+        },
+    ],
+};
+
+export default sidebars;
diff --git a/website/src/css/custom.css b/website/src/css/custom.css
new file mode 100644
index 0000000..2d90b07
--- /dev/null
+++ b/website/src/css/custom.css
@@ -0,0 +1,42 @@
+/**
+ * Any CSS included here will be global. The classic template
+ * bundles Infima by default. Infima is a CSS framework designed to
+ * work well for content-centric websites.
+ */
+
+/* You can override the default Infima variables here. */
+:root {
+  --ifm-color-primary: #2e8555;
+  --ifm-color-primary-dark: #29784c;
+  --ifm-color-primary-darker: #277148;
+  --ifm-color-primary-darkest: #205d3b;
+  --ifm-color-primary-light: #33925d;
+  --ifm-color-primary-lighter: #359962;
+  --ifm-color-primary-lightest: #3cad6e;
+  --ifm-code-font-size: 95%;
+  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1);
+}
+
+/* For readability concerns, you should choose a lighter palette in dark mode. */
+[data-theme='dark'] {
+  --ifm-color-primary: #25c2a0;
+  --ifm-color-primary-dark: #21af90;
+  --ifm-color-primary-darker: #1fa588;
+  --ifm-color-primary-darkest: #1a8870;
+  --ifm-color-primary-light: #29d5b0;
+  --ifm-color-primary-lighter: #32d8b4;
+  --ifm-color-primary-lightest: #4fddbf;
+  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3);
+}
+
+.header-github-link::before {
+  content: '';
+  width: 24px;
+  height: 24px;
+  display: flex;
+  background-color: var(--ifm-navbar-link-color);
+  mask-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E");
+  mask-repeat: no-repeat;
+  mask-position: center;
+  mask-size: 100%;
+}
diff --git a/docs/img/aksarc_nodepool_creation_portal.png b/website/static/img/aksarc_nodepool_creation_portal.png
similarity index 100%
rename from docs/img/aksarc_nodepool_creation_portal.png
rename to website/static/img/aksarc_nodepool_creation_portal.png
diff --git a/website/static/img/arch.png b/website/static/img/arch.png
new file mode 100644
index 0000000..62b4fc3
Binary files /dev/null and b/website/static/img/arch.png differ
diff --git a/docs/img/bell.svg b/website/static/img/bell.svg
similarity index 100%
rename from docs/img/bell.svg
rename to website/static/img/bell.svg
diff --git a/website/static/img/favicon.ico b/website/static/img/favicon.ico
new file mode 100644
index 0000000..76e9310
Binary files /dev/null and b/website/static/img/favicon.ico differ
diff --git a/docs/img/gpumemlayout.png b/website/static/img/gpumemlayout.png
similarity index 100%
rename from docs/img/gpumemlayout.png
rename to website/static/img/gpumemlayout.png
diff --git a/docs/img/kaito-fine-tuning.png b/website/static/img/kaito-fine-tuning.png
similarity index 100%
rename from docs/img/kaito-fine-tuning.png
rename to website/static/img/kaito-fine-tuning.png
diff --git a/docs/img/kaito-inference-adapter.png b/website/static/img/kaito-inference-adapter.png
similarity index 100%
rename from docs/img/kaito-inference-adapter.png
rename to website/static/img/kaito-inference-adapter.png
diff --git a/website/static/img/kaito-logo.png b/website/static/img/kaito-logo.png
new file mode 100644
index 0000000..f71e833
Binary files /dev/null and b/website/static/img/kaito-logo.png differ
diff --git a/docs/img/llama-3.3-70b-instruct.png b/website/static/img/llama-3.3-70b-instruct.png
similarity index 100%
rename from docs/img/llama-3.3-70b-instruct.png
rename to website/static/img/llama-3.3-70b-instruct.png
diff --git a/docs/img/model-as-oci-artifacts-evaluation.png b/website/static/img/model-as-oci-artifacts-evaluation.png
similarity index 100%
rename from docs/img/model-as-oci-artifacts-evaluation.png
rename to website/static/img/model-as-oci-artifacts-evaluation.png
diff --git a/docs/img/tilt.png b/website/static/img/tilt.png
similarity index 100%
rename from docs/img/tilt.png
rename to website/static/img/tilt.png
diff --git a/website/yarn.lock b/website/yarn.lock
new file mode 100644
index 0000000..070a346
--- /dev/null
+++ b/website/yarn.lock
@@ -0,0 +1,8513 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+
+"@algolia/autocomplete-core@1.17.9":
+  version "1.17.9"
+  resolved "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.17.9.tgz"
+  integrity sha512-O7BxrpLDPJWWHv/DLA9DRFWs+iY1uOJZkqUwjS5HSZAGcl0hIVCQ97LTLewiZmZ402JYUrun+8NqFP+hCknlbQ==
+  dependencies:
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+  integrity sha512-0XavAZbNJ5sDrCbkpWL8mia0o5WPOd2YGtxrEiZkBK9FjLppIUK2TgxK6qGD2P3hUXTJNNPVibrerKcx5WkR1g==
+  dependencies:
+    "@types/bonjour" "^3.5.9"
+    "@types/connect-history-api-fallback" "^1.3.5"
+    "@types/express" "^4.17.13"
+    "@types/serve-index" "^1.9.1"
+    "@types/serve-static" "^1.13.10"
+    "@types/sockjs" "^0.3.33"
+    "@types/ws" "^8.5.5"
+    ansi-html-community "^0.0.8"
+    bonjour-service "^1.0.11"
+    chokidar "^3.5.3"
+    colorette "^2.0.10"
+    compression "^1.7.4"
+    connect-history-api-fallback "^2.0.0"
+    default-gateway "^6.0.3"
+    express "^4.17.3"
+    graceful-fs "^4.2.6"
+    html-entities "^2.3.2"
+    http-proxy-middleware "^2.0.3"
+    ipaddr.js "^2.0.1"
+    launch-editor "^2.6.0"
+    open "^8.0.9"
+    p-retry "^4.5.0"
+    rimraf "^3.0.2"
+    schema-utils "^4.0.0"
+    selfsigned "^2.1.1"
+    serve-index "^1.9.1"
+    sockjs "^0.3.24"
+    spdy "^4.0.2"
+    webpack-dev-middleware "^5.3.4"
+    ws "^8.13.0"
+
+webpack-merge@^5.9.0:
+  version "5.10.0"
+  resolved "https://registry.npmjs.org/webpack-merge/-/webpack-merge-5.10.0.tgz"
+  integrity sha512-+4zXKdx7UnO+1jaN4l2lHVD+mFvnlZQP/6ljaJVb4SZiwIKeUnrT5l0gkT8z+n4hKpC+jpOv6O9R+gLtag7pSA==
+  dependencies:
+    clone-deep "^4.0.1"
+    flat "^5.0.2"
+    wildcard "^2.0.0"
+
+webpack-sources@^3.2.2, webpack-sources@^3.2.3:
+  version "3.3.2"
+  resolved "https://registry.npmjs.org/webpack-sources/-/webpack-sources-3.3.2.tgz"
+  integrity sha512-ykKKus8lqlgXX/1WjudpIEjqsafjOTcOJqxnAbMLAu/KCsDCJ6GBtvscewvTkrn24HsnvFwrSCbenFrhtcCsAA==
+
+"webpack@^4.0.0 || ^5.0.0", "webpack@^4.37.0 || ^5.0.0", webpack@^5.0.0, webpack@^5.1.0, webpack@^5.20.0, webpack@^5.88.1, "webpack@>= 4", "webpack@>=4.41.1 || 5.x", webpack@>=5, "webpack@3 || 4 || 5":
+  version "5.99.9"
+  resolved "https://registry.npmjs.org/webpack/-/webpack-5.99.9.tgz"
+  integrity sha512-brOPwM3JnmOa+7kd3NsmOUOwbDAj8FT9xDsG3IW0MgbN9yZV7Oi/s/+MNQ/EcSMqw7qfoRyXPoeEWT8zLVdVGg==
+  dependencies:
+    "@types/eslint-scope" "^3.7.7"
+    "@types/estree" "^1.0.6"
+    "@types/json-schema" "^7.0.15"
+    "@webassemblyjs/ast" "^1.14.1"
+    "@webassemblyjs/wasm-edit" "^1.14.1"
+    "@webassemblyjs/wasm-parser" "^1.14.1"
+    acorn "^8.14.0"
+    browserslist "^4.24.0"
+    chrome-trace-event "^1.0.2"
+    enhanced-resolve "^5.17.1"
+    es-module-lexer "^1.2.1"
+    eslint-scope "5.1.1"
+    events "^3.2.0"
+    glob-to-regexp "^0.4.1"
+    graceful-fs "^4.2.11"
+    json-parse-even-better-errors "^2.3.1"
+    loader-runner "^4.2.0"
+    mime-types "^2.1.27"
+    neo-async "^2.6.2"
+    schema-utils "^4.3.2"
+    tapable "^2.1.1"
+    terser-webpack-plugin "^5.3.11"
+    watchpack "^2.4.1"
+    webpack-sources "^3.2.3"
+
+webpackbar@^5.0.2:
+  version "5.0.2"
+  resolved "https://registry.npmjs.org/webpackbar/-/webpackbar-5.0.2.tgz"
+  integrity sha512-BmFJo7veBDgQzfWXl/wwYXr/VFus0614qZ8i9znqcl9fnEdiVkdbi0TedLQ6xAK92HZHDJ0QmyQ0fmuZPAgCYQ==
+  dependencies:
+    chalk "^4.1.0"
+    consola "^2.15.3"
+    pretty-time "^1.1.0"
+    std-env "^3.0.1"
+
+websocket-driver@^0.7.4, websocket-driver@>=0.5.1:
+  version "0.7.4"
+  resolved "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz"
+  integrity sha512-b17KeDIQVjvb0ssuSDF2cYXSg2iztliJ4B9WdsuB6J952qCPKmnVq4DyW5motImXHDC1cBT/1UezrJVsKw5zjg==
+  dependencies:
+    http-parser-js ">=0.5.1"
+    safe-buffer ">=5.1.0"
+    websocket-extensions ">=0.1.1"
+
+websocket-extensions@>=0.1.1:
+  version "0.1.4"
+  resolved "https://registry.npmjs.org/websocket-extensions/-/websocket-extensions-0.1.4.tgz"
+  integrity sha512-OqedPIGOfsDlo31UNwYbCFMSaO9m9G/0faIHj5/dZFDMFqPTcx6UwqyOy3COEaEOg/9VsGIpdqn62W5KhoKSpg==
+
+whatwg-encoding@^3.1.1:
+  version "3.1.1"
+  resolved "https://registry.npmjs.org/whatwg-encoding/-/whatwg-encoding-3.1.1.tgz"
+  integrity sha512-6qN4hJdMwfYBtE3YBTTHhoeuUrDBPZmbQaxWAqSALV/MeEnR5z1xd8UKud2RAkFoPkmB+hli1TZSnyi84xz1vQ==
+  dependencies:
+    iconv-lite "0.6.3"
+
+whatwg-mimetype@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.npmjs.org/whatwg-mimetype/-/whatwg-mimetype-4.0.0.tgz"
+  integrity sha512-QaKxh0eNIi2mE9p2vEdzfagOKHCcj1pJ56EEHGQOVxp8r9/iszLUUV7v89x9O1p/T+NlTM5W7jW6+cz4Fq1YVg==
+
+which@^1.3.1:
+  version "1.3.1"
+  resolved "https://registry.npmjs.org/which/-/which-1.3.1.tgz"
+  integrity sha512-HxJdYWq1MTIQbJ3nw0cqssHoTNU267KlrDuGZ1WYlxDStUtKUhOaJmh112/TZmHxxUfuJqPXSOm7tDyas0OSIQ==
+  dependencies:
+    isexe "^2.0.0"
+
+which@^2.0.1:
+  version "2.0.2"
+  resolved "https://registry.npmjs.org/which/-/which-2.0.2.tgz"
+  integrity sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==
+  dependencies:
+    isexe "^2.0.0"
+
+widest-line@^4.0.1:
+  version "4.0.1"
+  resolved "https://registry.npmjs.org/widest-line/-/widest-line-4.0.1.tgz"
+  integrity sha512-o0cyEG0e8GPzT4iGHphIOh0cJOV8fivsXxddQasHPHfoZf1ZexrfeA21w2NaEN1RHE+fXlfISmOE8R9N3u3Qig==
+  dependencies:
+    string-width "^5.0.1"
+
+wildcard@^2.0.0:
+  version "2.0.1"
+  resolved "https://registry.npmjs.org/wildcard/-/wildcard-2.0.1.tgz"
+  integrity sha512-CC1bOL87PIWSBhDcTrdeLo6eGT7mCFtrg0uIJtqJUFyK+eJnzl8A1niH56uu7KMa5XFrtiV+AQuHO3n7DsHnLQ==
+
+wrap-ansi@^8.0.1, wrap-ansi@^8.1.0:
+  version "8.1.0"
+  resolved "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz"
+  integrity sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==
+  dependencies:
+    ansi-styles "^6.1.0"
+    string-width "^5.0.1"
+    strip-ansi "^7.0.1"
+
+wrappy@1:
+  version "1.0.2"
+  resolved "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz"
+  integrity sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==
+
+write-file-atomic@^3.0.3:
+  version "3.0.3"
+  resolved "https://registry.npmjs.org/write-file-atomic/-/write-file-atomic-3.0.3.tgz"
+  integrity sha512-AvHcyZ5JnSfq3ioSyjrBkH9yW4m7Ayk8/9My/DD9onKeu/94fwrMocemO2QAJFAlnnDN+ZDS+ZjAR5ua1/PV/Q==
+  dependencies:
+    imurmurhash "^0.1.4"
+    is-typedarray "^1.0.0"
+    signal-exit "^3.0.2"
+    typedarray-to-buffer "^3.1.5"
+
+ws@^7.3.1:
+  version "7.5.10"
+  resolved "https://registry.npmjs.org/ws/-/ws-7.5.10.tgz"
+  integrity sha512-+dbF1tHwZpXcbOJdVOkzLDxZP1ailvSxM6ZweXTegylPny803bFhA+vqBYw4s31NSAk4S2Qz+AKXK9a4wkdjcQ==
+
+ws@^8.13.0:
+  version "8.18.2"
+  resolved "https://registry.npmjs.org/ws/-/ws-8.18.2.tgz"
+  integrity sha512-DMricUmwGZUVr++AEAe2uiVM7UoO9MAVZMDu05UQOaUII0lp+zOzLLU4Xqh/JvTqklB1T4uELaaPBKyjE1r4fQ==
+
+xdg-basedir@^5.0.1, xdg-basedir@^5.1.0:
+  version "5.1.0"
+  resolved "https://registry.npmjs.org/xdg-basedir/-/xdg-basedir-5.1.0.tgz"
+  integrity sha512-GCPAHLvrIH13+c0SuacwvRYj2SxJXQ4kaVTT5xgL3kPrz56XxkF21IGhjSE1+W0aw7gpBWRGXLCPnPby6lSpmQ==
+
+xml-js@^1.6.11:
+  version "1.6.11"
+  resolved "https://registry.npmjs.org/xml-js/-/xml-js-1.6.11.tgz"
+  integrity sha512-7rVi2KMfwfWFl+GpPg6m80IVMWXLRjO+PxTq7V2CDhoGak0wzYzFgUY2m4XJ47OGdXd8eLE8EmwfAmdjw7lC1g==
+  dependencies:
+    sax "^1.2.4"
+
+yallist@^3.0.2:
+  version "3.1.1"
+  resolved "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz"
+  integrity sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==
+
+yaml@^1.10.0, yaml@^1.10.2, yaml@^1.7.2:
+  version "1.10.2"
+  resolved "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz"
+  integrity sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==
+
+yocto-queue@^0.1.0:
+  version "0.1.0"
+  resolved "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz"
+  integrity sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==
+
+yocto-queue@^1.0.0:
+  version "1.2.1"
+  resolved "https://registry.npmjs.org/yocto-queue/-/yocto-queue-1.2.1.tgz"
+  integrity sha512-AyeEbWOu/TAXdxlV9wmGcR0+yh2j3vYPGOECcIj2S7MkrLyC7ne+oye2BKTItt0ii2PHk4cDy+95+LshzbXnGg==
+
+zwitch@^2.0.0:
+  version "2.0.4"
+  resolved "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz"
+  integrity sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==



Test patch:
diff --git a/pkg/utils/test/testModel.go b/pkg/utils/test/testModel.go
index fd81116..15e3054 100644
--- a/pkg/utils/test/testModel.go
+++ b/pkg/utils/test/testModel.go
@@ -17,7 +17,8 @@ var emptyParams = map[string]string{}
 func (*baseTestModel) GetInferenceParameters() *model.PresetParam {
 	return &model.PresetParam{
 		Metadata: model.Metadata{
-			Tag: "base-test-model",
+			Name: "test-model",
+			Tag:  "1.0.0",
 		},
 		GPUCountRequirement:       "1",
 		TotalGPUMemoryRequirement: "8Gi",
@@ -64,7 +65,8 @@ type testDistributedModel struct {
 func (*testDistributedModel) GetInferenceParameters() *model.PresetParam {
 	return &model.PresetParam{
 		Metadata: model.Metadata{
-			Tag: "test-distributed-model",
+			Name: "test-distributed-model",
+			Tag:  "1.0.0",
 		},
 		GPUCountRequirement:       "2",
 		TotalGPUMemoryRequirement: "64Gi",
@@ -94,7 +96,8 @@ type testNoTensorParallelModel struct {
 func (*testNoTensorParallelModel) GetInferenceParameters() *model.PresetParam {
 	return &model.PresetParam{
 		Metadata: model.Metadata{
-			Tag: "test-no-tensor-parallel-model",
+			Name: "test-no-tensor-parallel-model",
+			Tag:  "1.0.0",
 		},
 		GPUCountRequirement:       "1",
 		TotalGPUMemoryRequirement: "8Gi",
@@ -131,6 +134,8 @@ func (*testModelDownload) SupportDistributedInference() bool {
 func (*testModelDownload) GetInferenceParameters() *model.PresetParam {
 	return &model.PresetParam{
 		Metadata: model.Metadata{
+			Name:              "test-model-download",
+			Tag:               "1.0.0",
 			Version:           "https://huggingface.co/test-repo/test-model/commit/test-revision",
 			DownloadAtRuntime: true,
 		},
@@ -155,7 +160,8 @@ func (*testModelDownload) GetInferenceParameters() *model.PresetParam {
 func (*testNoLoraSupportModel) GetInferenceParameters() *model.PresetParam {
 	return &model.PresetParam{
 		Metadata: model.Metadata{
-			Tag: "test-no-lora-support-model",
+			Name: "test-no-lora-support-model",
+			Tag:  "1.0.0",
 		},
 		GPUCountRequirement:       "1",
 		TotalGPUMemoryRequirement: "8Gi",
diff --git a/pkg/workspace/inference/preset-inferences_test.go b/pkg/workspace/inference/preset-inferences_test.go
index 534f622..c8972f7 100644
--- a/pkg/workspace/inference/preset-inferences_test.go
+++ b/pkg/workspace/inference/preset-inferences_test.go
@@ -17,6 +17,7 @@ import (
 	v1 "k8s.io/apimachinery/pkg/apis/meta/v1"
 
 	"github.com/kaito-project/kaito/api/v1beta1"
+	"github.com/kaito-project/kaito/pkg/utils"
 	"github.com/kaito-project/kaito/pkg/utils/consts"
 	"github.com/kaito-project/kaito/pkg/utils/plugin"
 	"github.com/kaito-project/kaito/pkg/utils/test"
@@ -27,17 +28,19 @@ var ValidStrength string = "0.5"
 
 func TestGeneratePresetInference(t *testing.T) {
 	test.RegisterTestModel()
+	baseImage := metadata.MustGet("base")
+	baseImageName := fmt.Sprintf("test-registry/kaito-base:%s", baseImage.Tag)
 	testcases := map[string]struct {
-		workspace       *v1beta1.Workspace
-		nodeCount       int
-		modelName       string
-		callMocks       func(c *test.MockClient)
-		workload        string
-		expectedCmd     string
-		hasAdapters     bool
-		expectedImage   string
-		expectedVolume  string
-		expectedEnvVars []corev1.EnvVar
+		workspace          *v1beta1.Workspace
+		nodeCount          int
+		modelName          string
+		callMocks          func(c *test.MockClient)
+		workload           string
+		expectedCmd        string
+		hasAdapters        bool
+		expectedModelImage string
+		expectedVolume     string
+		expectedEnvVars    []corev1.EnvVar
 	}{
 		"test-model/vllm": {
 			workspace: test.MockWorkspaceWithPresetVLLM,
@@ -46,8 +49,8 @@ func TestGeneratePresetInference(t *testing.T) {
 			callMocks: func(c *test.MockClient) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 			},
-			workload:      "Deployment",
-			expectedImage: "test-registry/kaito-test-model:base-test-model",
+			workload:           "Deployment",
+			expectedModelImage: "test-registry/kaito-test-model:1.0.0",
 			// No BaseCommand, AccelerateParams, or ModelRunParams
 			// So expected cmd consists of shell command and inference file
 			expectedCmd: "/bin/sh -c python3 /workspace/vllm/inference_api.py --tensor-parallel-size=2 --served-model-name=mymodel --gpu-memory-utilization=0.90 --kaito-config-file=/mnt/config/inference_config.yaml",
@@ -61,8 +64,8 @@ func TestGeneratePresetInference(t *testing.T) {
 			callMocks: func(c *test.MockClient) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 			},
-			workload:      "Deployment",
-			expectedImage: "test-registry/kaito-test-model:test-no-tensor-parallel-model",
+			workload:           "Deployment",
+			expectedModelImage: "test-registry/kaito-test-no-tensor-parallel-model:1.0.0",
 			// No BaseCommand, AccelerateParams, or ModelRunParams
 			// So expected cmd consists of shell command and inference file
 			expectedCmd: "/bin/sh -c python3 /workspace/vllm/inference_api.py --kaito-config-file=/mnt/config/inference_config.yaml --gpu-memory-utilization=0.90",
@@ -76,8 +79,8 @@ func TestGeneratePresetInference(t *testing.T) {
 			callMocks: func(c *test.MockClient) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 			},
-			workload:      "Deployment",
-			expectedImage: "test-registry/kaito-test-model:test-no-lora-support-model",
+			workload:           "Deployment",
+			expectedModelImage: "test-registry/kaito-test-no-lora-support-model:1.0.0",
 			// No BaseCommand, AccelerateParams, or ModelRunParams
 			// So expected cmd consists of shell command and inference file
 			expectedCmd: "/bin/sh -c python3 /workspace/vllm/inference_api.py --kaito-config-file=/mnt/config/inference_config.yaml --gpu-memory-utilization=0.90",
@@ -91,11 +94,11 @@ func TestGeneratePresetInference(t *testing.T) {
 			callMocks: func(c *test.MockClient) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 			},
-			workload:       "Deployment",
-			expectedImage:  "test-registry/kaito-test-model:base-test-model",
-			expectedCmd:    "/bin/sh -c python3 /workspace/vllm/inference_api.py --enable-lora --tensor-parallel-size=2 --served-model-name=mymodel --gpu-memory-utilization=0.90 --kaito-config-file=/mnt/config/inference_config.yaml",
-			hasAdapters:    true,
-			expectedVolume: "adapter-volume",
+			workload:           "Deployment",
+			expectedModelImage: "test-registry/kaito-test-model:1.0.0",
+			expectedCmd:        "/bin/sh -c python3 /workspace/vllm/inference_api.py --enable-lora --tensor-parallel-size=2 --served-model-name=mymodel --gpu-memory-utilization=0.90 --kaito-config-file=/mnt/config/inference_config.yaml",
+			hasAdapters:        true,
+			expectedVolume:     "adapter-volume",
 			expectedEnvVars: []corev1.EnvVar{{
 				Name:  "Adapter-1",
 				Value: "0.5",
@@ -109,8 +112,8 @@ func TestGeneratePresetInference(t *testing.T) {
 			callMocks: func(c *test.MockClient) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 			},
-			workload:      "Deployment",
-			expectedImage: "test-registry/kaito-test-model:base-test-model",
+			workload:           "Deployment",
+			expectedModelImage: "test-registry/kaito-test-model:1.0.0",
 			// No BaseCommand, AccelerateParams, or ModelRunParams
 			// So expected cmd consists of shell command and inference file
 			expectedCmd: "/bin/sh -c accelerate launch /workspace/tfs/inference_api.py",
@@ -124,11 +127,11 @@ func TestGeneratePresetInference(t *testing.T) {
 			callMocks: func(c *test.MockClient) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 			},
-			workload:       "Deployment",
-			expectedImage:  "test-registry/kaito-test-model:base-test-model",
-			expectedCmd:    "/bin/sh -c accelerate launch /workspace/tfs/inference_api.py",
-			hasAdapters:    true,
-			expectedVolume: "adapter-volume",
+			workload:           "Deployment",
+			expectedModelImage: "test-registry/kaito-test-model:1.0.0",
+			expectedCmd:        "/bin/sh -c accelerate launch /workspace/tfs/inference_api.py",
+			hasAdapters:        true,
+			expectedVolume:     "adapter-volume",
 			expectedEnvVars: []corev1.EnvVar{{
 				Name:  "Adapter-1",
 				Value: "0.5",
@@ -142,11 +145,7 @@ func TestGeneratePresetInference(t *testing.T) {
 			callMocks: func(c *test.MockClient) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 			},
-			workload: "Deployment",
-			expectedImage: func() string {
-				baseImage := metadata.MustGet("base")
-				return fmt.Sprintf("test-registry/kaito-base:%s", baseImage.Tag)
-			}(),
+			workload:    "Deployment",
 			expectedCmd: `/bin/sh -c python3 /workspace/vllm/inference_api.py --tensor-parallel-size=2 --model=test-repo/test-model --code-revision=test-revision --gpu-memory-utilization=0.90 --kaito-config-file=/mnt/config/inference_config.yaml`,
 			expectedEnvVars: []corev1.EnvVar{{
 				Name: "HF_TOKEN",
@@ -169,11 +168,7 @@ func TestGeneratePresetInference(t *testing.T) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.Service{}), mock.Anything).Return(nil)
 			},
-			workload: "StatefulSet",
-			expectedImage: func() string {
-				baseImage := metadata.MustGet("base")
-				return fmt.Sprintf("test-registry/kaito-base:%s", baseImage.Tag)
-			}(),
+			workload:    "StatefulSet",
 			expectedCmd: `/bin/sh -c if [ "${POD_INDEX}" = "0" ]; then  --ray_cluster_size=2 --ray_port=6379; python3 /workspace/vllm/inference_api.py --model=test-repo/test-model --code-revision=test-revision --gpu-memory-utilization=0.90 --kaito-config-file=/mnt/config/inference_config.yaml --pipeline-parallel-size=2 --tensor-parallel-size=2; else  --ray_address=testWorkspace-0.testWorkspace-headless.kaito.svc.cluster.local --ray_port=6379; fi`,
 			expectedEnvVars: []corev1.EnvVar{{
 				Name: "HF_TOKEN",
@@ -205,11 +200,7 @@ func TestGeneratePresetInference(t *testing.T) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.Service{}), mock.Anything).Return(nil)
 			},
-			workload: "StatefulSet",
-			expectedImage: func() string {
-				baseImage := metadata.MustGet("base")
-				return fmt.Sprintf("test-registry/kaito-base:%s", baseImage.Tag)
-			}(),
+			workload:    "StatefulSet",
 			expectedCmd: `/bin/sh -c if [ "${POD_INDEX}" = "0" ]; then  --ray_cluster_size=2 --ray_port=6379; python3 /workspace/vllm/inference_api.py --model=test-repo/test-model --code-revision=test-revision --gpu-memory-utilization=0.90 --kaito-config-file=/mnt/config/inference_config.yaml --pipeline-parallel-size=2 --tensor-parallel-size=2; else  --ray_address=testWorkspace-0.testWorkspace-headless.kaito.svc.cluster.local --ray_port=6379; fi`,
 			expectedEnvVars: []corev1.EnvVar{{
 				Name: "HF_TOKEN",
@@ -238,10 +229,7 @@ func TestGeneratePresetInference(t *testing.T) {
 			callMocks: func(c *test.MockClient) {
 				c.On("Get", mock.IsType(context.TODO()), mock.Anything, mock.IsType(&corev1.ConfigMap{}), mock.Anything).Return(nil)
 			},
-			workload: "Deployment",
-			expectedImage: func() string {
-				return fmt.Sprintf("test-registry/kaito-base:%s", metadata.MustGet("base").Tag)
-			}(),
+			workload:    "Deployment",
 			expectedCmd: "/bin/sh -c accelerate launch /workspace/tfs/inference_api.py --pretrained_model_name_or_path=test-repo/test-model --revision=test-revision",
 			expectedEnvVars: []corev1.EnvVar{{
 				Name: "HF_TOKEN",
@@ -300,22 +288,46 @@ func TestGeneratePresetInference(t *testing.T) {
 			createdWorkload := ""
 			image := ""
 			envVars := []corev1.EnvVar{}
+			var initContainer []corev1.Container
 			switch t := createdObject.(type) {
 			case *appsv1.Deployment:
 				createdWorkload = "Deployment"
 				image = t.Spec.Template.Spec.Containers[0].Image
 				envVars = t.Spec.Template.Spec.Containers[0].Env
+				initContainer = t.Spec.Template.Spec.InitContainers
 			case *appsv1.StatefulSet:
 				createdWorkload = "StatefulSet"
 				image = t.Spec.Template.Spec.Containers[0].Image
 				envVars = t.Spec.Template.Spec.Containers[0].Env
+				initContainer = t.Spec.Template.Spec.InitContainers
 			}
 			if tc.workload != createdWorkload {
 				t.Errorf("%s: returned workload type is wrong", k)
 			}
-			if image != tc.expectedImage {
-				t.Errorf("%s: Image is not expected, got %s, expect %s", k, image, tc.expectedImage)
+
+			if tc.expectedModelImage != "" {
+				var pullerContainer corev1.Container
+				for _, container := range initContainer {
+					if container.Name == "model-weights-downloader" {
+						pullerContainer = container
+						break
+					}
+				}
+				expectedPullerCmd := []string{
+					"oras",
+					"pull",
+					tc.expectedModelImage,
+					"-o",
+					utils.DefaultWeightsVolumePath,
+				}
+				if !reflect.DeepEqual(pullerContainer.Command, expectedPullerCmd) {
+					t.Errorf("%s: Puller command is not expected, got %v, expect %v", k, pullerContainer.Command, expectedPullerCmd)
+				}
 			}
+			if image != baseImageName {
+				t.Errorf("%s: image is not expected, got %s, expect %s", k, image, baseImageName)
+			}
+
 			if !reflect.DeepEqual(envVars, tc.expectedEnvVars) {
 				t.Errorf("%s: EnvVars are not expected, got %v, expect %v", k, envVars, tc.expectedEnvVars)
 			}
diff --git a/pkg/workspace/manifests/manifests_test.go b/pkg/workspace/manifests/manifests_test.go
index ad2f2b5..0c25c30 100644
--- a/pkg/workspace/manifests/manifests_test.go
+++ b/pkg/workspace/manifests/manifests_test.go
@@ -34,6 +34,7 @@ func TestGenerateStatefulSetManifest(t *testing.T) {
 			nil, //volumes
 			nil, //volumeMount
 			nil, //envVars
+			nil, //initContainers
 		)
 
 		assert.Contains(t, obj.GetAnnotations(), v1beta1.WorkspaceRevisionAnnotation)
@@ -89,6 +90,7 @@ func TestGenerateDeploymentManifest(t *testing.T) {
 			nil, //volumes
 			nil, //volumeMount
 			nil, //envVars
+			nil, //initContainers
 		)
 
 		assert.Contains(t, obj.GetAnnotations(), v1beta1.WorkspaceRevisionAnnotation)
diff --git a/pkg/workspace/tuning/preset-tuning_test.go b/pkg/workspace/tuning/preset-tuning_test.go
index be5c896..ea5bf8f 100644
--- a/pkg/workspace/tuning/preset-tuning_test.go
+++ b/pkg/workspace/tuning/preset-tuning_test.go
@@ -52,61 +52,6 @@ func TestGetInstanceGPUCount(t *testing.T) {
 	}
 }
 
-func TestGetTuningImageInfo(t *testing.T) {
-	testcases := map[string]struct {
-		registryName string
-		wObj         *kaitov1beta1.Workspace
-		presetObj    *model.PresetParam
-		expected     string
-	}{
-		"Valid Registry and Parameters": {
-			registryName: "testregistry",
-			wObj: &kaitov1beta1.Workspace{
-				Tuning: &kaitov1beta1.TuningSpec{
-					Preset: &kaitov1beta1.PresetSpec{
-						PresetMeta: kaitov1beta1.PresetMeta{
-							Name: "testpreset",
-						},
-					},
-				},
-			},
-			presetObj: &model.PresetParam{
-				Metadata: model.Metadata{
-					Tag: "latest",
-				},
-			},
-			expected: "testregistry/kaito-testpreset:latest",
-		},
-		"Empty Registry Name": {
-			registryName: "",
-			wObj: &kaitov1beta1.Workspace{
-				Tuning: &kaitov1beta1.TuningSpec{
-					Preset: &kaitov1beta1.PresetSpec{
-						PresetMeta: kaitov1beta1.PresetMeta{
-							Name: "testpreset",
-						},
-					},
-				},
-			},
-			presetObj: &model.PresetParam{
-				Metadata: model.Metadata{
-					Tag: "latest",
-				},
-			},
-			expected: "/kaito-testpreset:latest",
-		},
-	}
-
-	for name, tc := range testcases {
-		t.Run(name, func(t *testing.T) {
-			t.Setenv("PRESET_REGISTRY_NAME", tc.registryName)
-
-			result := GetTuningImageInfo(context.Background(), tc.wObj, tc.presetObj)
-			assert.Equal(t, tc.expected, result)
-		})
-	}
-}
-
 func TestGetDataSrcImageInfo(t *testing.T) {
 	testcases := map[string]struct {
 		wObj            *kaitov1beta1.Workspace
diff --git a/test/e2e/inference_with_adapters_test.go b/test/e2e/inference_with_adapters_test.go
index f7f9b7c..e479346 100644
--- a/test/e2e/inference_with_adapters_test.go
+++ b/test/e2e/inference_with_adapters_test.go
@@ -92,13 +92,21 @@ func validateInitContainers(workspaceObj *kaitov1beta1.Workspace, expectedInitCo
 				return false
 			}
 
-			if len(initContainers) != len(expectedInitContainers) {
-				return false
+			for _, initContainer := range expectedInitContainers {
+				found := false
+				for _, gotContainer := range initContainers {
+					if initContainer.Name == gotContainer.Name && initContainer.Image == gotContainer.Image {
+						// Found a matching init container
+						found = true
+						break
+					}
+				}
+				if !found {
+					return false
+				}
 			}
-			initContainer, expectedInitContainer := initContainers[0], expectedInitContainers[0]
 
-			// GinkgoWriter.Printf("Resource '%s' not ready. Ready replicas: %d\n", workspaceObj.Name, readyReplicas)
-			return initContainer.Image == expectedInitContainer.Image && initContainer.Name == expectedInitContainer.Name
+			return true
 		}, 20*time.Minute, utils.PollInterval).Should(BeTrue(), "Failed to wait for initContainers to be ready")
 	})
 }
diff --git a/test/e2e/preset_test.go b/test/e2e/preset_test.go
index 72f4f9b..db59529 100644
--- a/test/e2e/preset_test.go
+++ b/test/e2e/preset_test.go
@@ -256,10 +256,10 @@ func createPhi3TuningWorkspaceWithPresetPublicMode(configMapName string, numOfNo
 	outputRegistryUrl := fmt.Sprintf("%s.azurecr.io/%s:%s", azureClusterName, e2eOutputImageName, e2eOutputImageTag)
 	var uniqueID string
 	By("Creating a workspace Tuning CR with Phi-3 preset public mode", func() {
-		uniqueID = fmt.Sprint("preset-", rand.Intn(1000))
+		uniqueID = fmt.Sprint("preset-tuning-falcon-", rand.Intn(1000))
 		workspaceObj = utils.GenerateE2ETuningWorkspaceManifest(uniqueID, namespaceName, "",
 			fullDatasetImageName1, outputRegistryUrl, numOfNode, "Standard_NC6s_v3", &metav1.LabelSelector{
-				MatchLabels: map[string]string{"kaito-workspace": "public-preset-e2e-test-tuning-falcon"},
+				MatchLabels: map[string]string{"kaito-workspace": uniqueID},
 			}, nil, PresetPhi3Mini128kModel, []string{e2eACRSecret}, configMapName, intputVolume, outputVolume)
 
 		createAndValidateWorkspace(workspaceObj)
diff --git a/test/e2e/preset_vllm_test.go b/test/e2e/preset_vllm_test.go
index 2678e7e..fb7b9b7 100644
--- a/test/e2e/preset_vllm_test.go
+++ b/test/e2e/preset_vllm_test.go
@@ -336,7 +336,7 @@ func createPhi4WorkspaceWithAdapterAndVLLM(numOfNode int, validAdapters []kaitov
 	workspaceObj := &kaitov1beta1.Workspace{}
 	By("Creating a workspace CR with phi4 mini preset public mode and vLLM", func() {
 		uniqueID := fmt.Sprint("preset-phi4-", rand.Intn(1000))
-		workspaceObj = utils.GenerateInferenceWorkspaceManifestWithVLLM(uniqueID, namespaceName, "", numOfNode, "Standard_NC6s_v3",
+		workspaceObj = utils.GenerateInferenceWorkspaceManifestWithVLLM(uniqueID, namespaceName, "", numOfNode, "Standard_NC24ads_A100_v4",
 			&metav1.LabelSelector{
 				MatchLabels: map[string]string{"kaito-workspace": "public-preset-e2e-test-phi4-adapter-vllm"},
 			}, nil, PresetPhi4MiniModel, nil, nil, validAdapters, "")