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master/admin/kubernetes_deployment/index.html

@@ -1300,23 +1300,23 @@ <h2 id="3-install-kserve">3. Install KServe<a class="headerlink" href="#3-instal
 <div class="tabbed-content">
 <div class="tabbed-block">
 <p>I. Install KServe CRDs</p>
-<div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve-crd<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve-crd<span class="w"> </span>--version<span class="w"> </span>v0.14.0
+<div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve-crd<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve-crd<span class="w"> </span>--version<span class="w"> </span>v0.14.1
 </code></pre></div>
 <p>II. Install KServe Resources</p>
 <p>Set the <code>kserve.controller.deploymentMode</code> to <code>RawDeployment</code> and <code>kserve.controller.gateway.ingressGateway.className</code> to point to the <code>IngressClass</code>
 name created in <a href="#1-install-ingress-controller">step 1</a>.</p>
-<div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve<span class="w"> </span>--version<span class="w"> </span>v0.14.0<span class="w"> </span><span class="se">\</span>
+<div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve<span class="w"> </span>--version<span class="w"> </span>v0.14.1<span class="w"> </span><span class="se">\</span>
 <span class="w"> </span>--set<span class="w"> </span>kserve.controller.deploymentMode<span class="o">=</span>RawDeployment<span class="w"> </span><span class="se">\</span>
 <span class="w"> </span>--set<span class="w"> </span>kserve.controller.gateway.ingressGateway.className<span class="o">=</span>your-ingress-class
 </code></pre></div>
 </div>
 <div class="tabbed-block">
 <p>I. Install KServe:
 <code>--server-side</code> option is required as the InferenceService CRD is large, see <a href="https://github.com/kserve/kserve/issues/3487">this issue</a> for details.</p>
-<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>--server-side<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.14.0/kserve.yaml
+<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>--server-side<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.14.1/kserve.yaml
 </code></pre></div>
 <p>II. Install KServe default serving runtimes:</p>
-<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>--server-side<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.14.0/kserve-cluster-resources.yaml
+<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>--server-side<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.14.1/kserve-cluster-resources.yaml
 </code></pre></div>
 <p>III. Change default deployment mode and ingress option</p>
 <p>First in the ConfigMap <code>inferenceservice-config</code> modify the <code>defaultDeploymentMode</code> from the <code>deploy</code> section to <code>RawDeployment</code>,</p>

master/admin/serverless/serverless/index.html

@@ -1311,18 +1311,18 @@ <h2 id="4-install-kserve">4. Install KServe<a class="headerlink" href="#4-instal
 <div class="tabbed-content">
 <div class="tabbed-block">
 <p>Install KServe CRDs
-<div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve-crd<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve-crd<span class="w"> </span>--version<span class="w"> </span>v0.14.0
+<div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve-crd<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve-crd<span class="w"> </span>--version<span class="w"> </span>v0.14.1
 </code></pre></div></p>
 <p>Install KServe Resources
-<div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve<span class="w"> </span>--version<span class="w"> </span>v0.14.0
+<div class="highlight"><pre><span></span><code>helm<span class="w"> </span>install<span class="w"> </span>kserve<span class="w"> </span>oci://ghcr.io/kserve/charts/kserve<span class="w"> </span>--version<span class="w"> </span>v0.14.1
 </code></pre></div></p>
 </div>
 <div class="tabbed-block">
 <p>Install KServe CRDs and Controller, <code>--server-side</code> option is required as the InferenceService CRD is large, see <a href="https://github.com/kserve/kserve/issues/3487">this issue</a> for details.</p>
-<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>--server-side<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.14.0/kserve.yaml
+<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>--server-side<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.14.1/kserve.yaml
 </code></pre></div>
 <p>Install KServe Built-in ClusterServingRuntimes
-<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>--server-side<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.14.0/kserve-cluster-resources.yaml
+<div class="highlight"><pre><span></span><code>kubectl<span class="w"> </span>apply<span class="w"> </span>--server-side<span class="w"> </span>-f<span class="w"> </span>https://github.com/kserve/kserve/releases/download/v0.14.1/kserve-cluster-resources.yaml
 </code></pre></div></p>
 </div>
 </div>

master/get_started/index.html

@@ -1293,7 +1293,7 @@ <h2 id="install-the-kserve-quickstart-environment">Install the KServe "Quickstar
 </li>
 <li>
 <p>You can then get started with a local deployment of KServe by using <em>KServe Quick installation script on Kind</em>:</p>
-<div class="highlight"><pre><span></span><code>curl<span class="w"> </span>-s<span class="w"> </span><span class="s2">"https://raw.githubusercontent.com/kserve/kserve/release-0.14/hack/quick_install.sh"</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>bash
+<div class="highlight"><pre><span></span><code>curl<span class="w"> </span>-s<span class="w"> </span><span class="s2">"https://raw.githubusercontent.com/kserve/kserve/release-0.14.1/hack/quick_install.sh"</span><span class="w"> </span><span class="p">|</span><span class="w"> </span>bash
 </code></pre></div>
 </li>
 </ol>

master/modelserving/v1beta1/serving_runtime/index.html

@@ -1189,8 +1189,8 @@ <h1>
 <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href="#model-serving-runtimes" title="Permanent link">¶</a></h1>
 <p>KServe provides a simple Kubernetes CRD to enable deploying single or multiple trained models onto model serving runtimes such as <a href="https://www.tensorflow.org/tfx/guide/serving">TFServing</a>,
 <a href="https://pytorch.org/serve/server.html">TorchServe</a>, <a href="https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs">Triton Inference Server</a>.
-In addition <a href="https://github.com/kserve/kserve/tree/master/python/kserve/kserve">ModelServer</a> is the Python model serving runtime implemented in KServe itself with prediction v1 protocol,
-<a href="https://github.com/SeldonIO/MLServer">MLServer</a> implements the <a href="https://github.com/kserve/kserve/tree/master/docs/predict-api/v2">prediction v2 protocol</a> with both REST and gRPC.
+For Hugging Face models, KServe provides <a href="https://github.com/kserve/kserve/tree/master/python/huggingfaceserver">Hugging Face Server</a> for hosting the transformer based models with Open Inference and OpenAI Protocol.
+In addition <a href="https://github.com/kserve/kserve/tree/master/python/kserve/kserve">ModelServer</a> is the Python model serving runtime implemented in KServe itself with prediction v1 and Open Inference Protocol(v2),
 These model serving runtimes are able to provide out-of-the-box model serving, but you could also choose to build your own model server for more complex use case.
 KServe provides basic API primitives to allow you easily build custom model serving runtime, you can use other tools like <a href="https://docs.bentoml.org/en/latest">BentoML</a> to build your custom model serving image.</p>
 <p>After models are deployed with InferenceService, you get all the following serverless features provided by KServe.</p>
@@ -1239,20 +1239,11 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 <td><a href="../custom/custom_model/">Custom Model</a></td>
 </tr>
 <tr>
-<td><a href="https://mlserver.readthedocs.io/en/latest/runtimes/lightgbm.html">LightGBM MLServer</a></td>
-<td><a href="https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html#lightgbm.Booster.save_model">Saved LightGBM Model</a></td>
-<td>v2</td>
-<td>v2</td>
-<td>v1.5.0 (MLServer)</td>
-<td>4</td>
-<td><a href="../lightgbm/">LightGBM Iris V2</a></td>
-</tr>
-<tr>
 <td><a href="https://github.com/kserve/kserve/tree/master/python/lgbserver">LightGBM ModelServer</a></td>
 <td><a href="https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.Booster.html#lightgbm.Booster.save_model">Saved LightGBM Model</a></td>
 <td>v1, v2</td>
 <td>v2</td>
-<td>v0.14.0 (KServe)</td>
+<td>v0.14.1 (KServe)</td>
 <td>4</td>
 <td><a href="../lightgbm/">LightGBM Iris</a></td>
 </tr>
@@ -1270,25 +1261,16 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 <td><a href="http://dmg.org/pmml/v4-4-1/GeneralStructure.html">PMML</a></td>
 <td>v1, v2</td>
 <td>v2</td>
-<td>v0.14.0 (KServe)</td>
+<td>v0.14.1 (KServe)</td>
 <td>3, 4 (<a href="https://github.com/autodeployai/pypmml">PMML4.4.1</a>)</td>
 <td><a href="../pmml/">SKLearn PMML</a></td>
 </tr>
 <tr>
-<td><a href="https://github.com/SeldonIO/MLServer">SKLearn MLServer</a></td>
-<td><a href="https://scikit-learn.org/stable/modules/model_persistence.html">Pickled Model</a></td>
-<td>v2</td>
-<td>v2</td>
-<td>v1.5.0 (MLServer)</td>
-<td>1</td>
-<td><a href="../sklearn/v2/">SKLearn Iris V2</a></td>
-</tr>
-<tr>
 <td><a href="https://github.com/kserve/kserve/tree/master/python/sklearnserver">SKLearn ModelServer</a></td>
 <td><a href="https://scikit-learn.org/stable/modules/model_persistence.html">Pickled Model</a></td>
 <td>v1, v2</td>
 <td>v2</td>
-<td>v0.14.0 (KServe)</td>
+<td>v0.14.1 (KServe)</td>
 <td>1.5</td>
 <td><a href="../sklearn/v2/">SKLearn Iris</a></td>
 </tr>
@@ -1320,38 +1302,29 @@ <h1 id="model-serving-runtimes">Model Serving Runtimes<a class="headerlink" href
 <td><a href="../triton/torchscript/">Torchscript cifar</a></td>
 </tr>
 <tr>
-<td><a href="https://github.com/SeldonIO/MLServer">XGBoost MLServer</a></td>
-<td><a href="https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html">Saved Model</a></td>
-<td>v2</td>
-<td>v2</td>
-<td>v1.5.0 (MLServer)</td>
-<td>2</td>
-<td><a href="../xgboost/">XGBoost Iris V2</a></td>
-</tr>
-<tr>
 <td><a href="https://github.com/kserve/kserve/tree/master/python/xgbserver">XGBoost ModelServer</a></td>
 <td><a href="https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html">Saved Model</a></td>
 <td>v1, v2</td>
 <td>v2</td>
-<td>v0.14.0 (KServe)</td>
+<td>v0.14.1 (KServe)</td>
 <td>2</td>
 <td><a href="../xgboost/">XGBoost Iris</a></td>
 </tr>
 <tr>
 <td><a href="https://github.com/kserve/kserve/tree/master/python/huggingfaceserver">HuggingFace ModelServer</a></td>
 <td><a href="https://huggingface.co/docs/transformers/v4.39.2/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">Saved Model</a> / <a href="https://huggingface.co/models">Huggingface Hub Model_Id</a></td>
-<td>v1, v2</td>
+<td>v1, v2, OpenAI</td>
 <td>--</td>
-<td>v0.14.0 (KServe)</td>
+<td>v0.14.1 (KServe)</td>
 <td>4 (<a href="https://pypi.org/project/transformers/4.43.4/">Transformers</a>)</td>
 <td>--</td>
 </tr>
 <tr>
 <td><a href="https://github.com/kserve/kserve/tree/master/python/huggingfaceserver">HuggingFace VLLM ModelServer</a></td>
 <td><a href="https://huggingface.co/docs/transformers/v4.43.4/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">Saved Model</a> / <a href="https://huggingface.co/models">Huggingface Hub Model_Id</a></td>
-<td>v2</td>
+<td>v2, OpenAI</td>
 <td>--</td>
-<td>v0.14.0 (KServe)</td>
+<td>v0.14.1 (KServe)</td>
 <td>0 (<a href="https://pypi.org/project/vllm/0.6.1.post2/">Vllm</a>)</td>
 <td>--</td>
 </tr>

master/modelserving/v1beta1/triton/huggingface/index.html

@@ -1276,7 +1276,7 @@ <h2 id="deploy-inferenceservice-with-triton-and-hugging-face-runtime">Deploy Inf
 <span class="w">      </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">--model_id=bert-base-uncased</span>
 <span class="w">      </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">--predictor_protocol=v2</span>
 <span class="w">      </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">--tensor_input_names=input_ids</span>
-<span class="w">      </span><span class="nt">image</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">kserve/huggingfaceserver:v0.14.0</span>
+<span class="w">      </span><span class="nt">image</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">kserve/huggingfaceserver:v0.14.1</span>
 <span class="w">      </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">kserve-container</span>
 <span class="w">      </span><span class="nt">resources</span><span class="p">:</span>
 <span class="w">        </span><span class="nt">limits</span><span class="p">:</span>