diff --git a/src/content/docs/service/models.mdx b/src/content/docs/service/models.mdx
index 409e0cb..9bc90c9 100644
--- a/src/content/docs/service/models.mdx
+++ b/src/content/docs/service/models.mdx
@@ -1,49 +1,84 @@
---
title: Models
-description: Manage and monitor Large Language Models.
+description: Manage and monitor AI/ML models and artifacts.
---
import { Steps, Aside } from '@astrojs/starlight/components';
-The Models section allows you to manage and monitor Large Language Models (LLMs) deployed in the OtterScale cluster. It provides real-time insights into model performance and resource usage.
+
+The Models page provides a unified view of all AI/ML models deployed in your OtterScale cluster. It aggregates information about model deployments and their artifacts, allowing you to monitor status, manage resources, and perform lifecycle operations.
+
## Introduction
-The Models page displays a list of deployed LLMs. The table includes the following information:
+The Models page displays a list of all models. The table includes the following columns (as shown in the UI):
-| Column | Description |
-| :---------------------- | :---------------------------------------------------------------------------------------- |
-| **Model** | The name of the application hosting the model. Links to the application details. |
-| **Name** | The specific name or identifier of the LLM (e.g., `llama3-8b`). |
-| **Replicas** | The number of desired replicas for the model service. |
-| **Healthies** | The number of currently healthy and ready replicas. |
-| **GPU Cache** | The percentage of GPU cache currently in use. |
-| **KV Cache** | The percentage of Key-Value (KV) cache usage, critical for transformer model performance. |
-| **Requests** | The total request latency or load metric for the model. |
-| **Time to First Token** | The average time taken to generate the first token of a response (latency metric). |
+| Column | Description |
+| :----------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Name** | The name of the model. |
+| **Model Name** | The unique identifier of the model (modelName/id). |
+| **Namespace** | The Kubernetes namespace where the model is deployed. |
+| **Status** | The current status of the model (e.g., Running, Pending). |
+| **Description** | The description of the model. |
+| **Prefill** | Prefill configuration: vGPU memory %, replica, tensor (if available). |
+| **Decode** | Decode configuration: vGPU memory %, replica, tensor (if available). |
+| **First Deployed** | Timestamp of first deployment. |
+| **Last Deployed** | Timestamp of last deployment. |
+| **GPU Relation** | GPU resource relation (shown only if status is 'deployed'). |
+| **Test** | Test button for model API (only available when the model is in the "ready" state; opens a dialog to test the model). |
+| **Actions** | Management actions (update, delete, etc.). |
-## Monitor Models
-The dashboard integrates with Prometheus to provide real-time metrics for each model:
-- **GPU & KV Cache**: Monitor these percentages to ensure your models are not running out of memory context, which could degrade performance or cause errors.
-- **Latency Metrics**: Track "Requests" and "Time to First Token" to ensure the models are responsive and meeting service level objectives (SLOs).
-## Manage Models
+### Pods Table
-You can perform basic lifecycle actions on the models:
-### Create a Model
+The **Pods Table** (in the details view) lists all pods managed by this model, if available.
+Click the expand icon at the beginning of a row to view detailed pod information.
-To deploy a new model:
+| Column | Description |
+| :---------------------- | :--------------------------------------------------------------------------------------- |
+| **Pod** | The unique name of the pod. |
+| **Phase** | The current lifecycle phase of the pod (e.g., Running, Pending). |
+| **Ready** | Number of ready containers vs total containers. |
+| **Restarts** | Number of times containers in the pod have restarted. |
+| **Conditions** | The most recent condition or error status for the pod. |
+| **Time to First Token** | The sum of time (in seconds) taken to generate the first token for requests to this pod. |
+| **Request Latency** | The 95th percentile end-to-end request latency (in seconds) for this pod. |
+| **Log** | **Clickable**: Opens the log view for the pod. |
+| **Create Time** | The timestamp when the pod was created. |
-1. Click the **Create** button (plus icon) at the top of the page.
-2. Follow the prompts to configure and deploy your LLM service.
-### Delete a Model
-To remove a model:
+## Manage Models
-1. Locate the model in the list.
-2. Click the **Delete** button (trash icon) in the actions menu.
-3. Confirm the action to remove the model deployment.
+You can manage the lifecycle of your models using the **Actions** menu.
+
+### Model Actions
+
+The **Actions** menu (three dots icon) for each model provides:
+
+#### Create
+Create a new model.
+
+1. Select **Create** from the actions menu or click the **Create** button.
+2. You can search for models using the cloud icon next to the input box, or select a model from your model artifacts by clicking the archive icon.
+3. Fill in the model configuration (name, namespace, prefill/decode, description, etc.).
+4. Confirm to deploy the model.
+
+
+#### Update
+Modify the configuration of an existing model (such as prefill/decode, description, etc.).
+
+1. Select **Update** from the actions menu.
+2. Edit the desired fields.
+3. Confirm to apply the changes.
+
+
+#### Delete
+Delete a model.
+
+1. Select **Delete** from the actions menu.
+2. Confirm deletion.
+
diff --git a/src/content/docs/service/settings/05-model-artifact.mdx b/src/content/docs/service/settings/05-model-artifact.mdx
new file mode 100644
index 0000000..eba4fbe
--- /dev/null
+++ b/src/content/docs/service/settings/05-model-artifact.mdx
@@ -0,0 +1,60 @@
+---
+title: Model Artifact
+description: Manage model files and weights for AI/ML models.
+---
+
+import { Steps, Aside } from '@astrojs/starlight/components';
+
+
+
+The Model Artifact page allows you to manage model artifacts for your AI/ML models. You can upload, view, and delete model artifacts associated with different models and namespaces.
+
+
+## Introduction
+
+The Model Artifact page displays a list of all model artifacts. The table includes the following columns:
+
+| Column | Description |
+| :-------------- | :----------------------------------------------------------------- |
+| **Name** | The name of the model artifact. |
+| **Namespace** | The Kubernetes namespace where the artifact is stored. |
+| **Model Name** | The name of the associated model. |
+| **Status** | The current job status for downloading or processing the artifact. |
+| **Phase** | The current phase/status of the artifact. |
+| **Size** | The size of the artifact (GB/TB). |
+| **Volume** | The volume name where the artifact is stored. |
+| **Create Time** | The timestamp when the artifact was created. |
+| **Actions** | Management actions (delete). |
+
+
+## Manage Model Artifacts
+
+You can create new model artifacts or manage existing ones using the **Actions** menu.
+
+### Create a New Model Artifact
+
+To upload a new model artifact:
+
+
+1. Click the **Create** button (plus icon) at the top of the page.
+2. A modal window titled "Create Model Artifact" will appear.
+3. **Configuration**:
+ - **Name**: Enter a unique name for the artifact.
+ - **Namespace**: Select the namespace (usually `llm-d`).
+ - **Model Name**: Select the model name (supports Hugging Face models).
+ - **Size**: Set the artifact size (GB/TB).
+4. Click **Confirm** to create the model artifact.
+
+
+
+### Model Artifact Actions
+
+The **Actions** menu (trash icon) for each model artifact provides the following option:
+
+#### Delete
+
+Permanently removes the model artifact.
+
+