[release branch] Update links

client/go/kserve-api/Dockerfile

@@ -26,7 +26,7 @@ RUN go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34
 RUN go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@v1.4.0
 
 # Compile API
-RUN wget https://raw.githubusercontent.com/openvinotoolkit/model_server/main/src/kfserving_api/grpc_predict_v2.proto
+RUN wget https://raw.githubusercontent.com/openvinotoolkit/model_server/releases/2026/1/src/kfserving_api/grpc_predict_v2.proto
 RUN echo 'option go_package = "./grpc-client";' >> grpc_predict_v2.proto
 RUN protoc --go_out="./" --go-grpc_out="./" ./grpc_predict_v2.proto
 

client/java/kserve-api/pom.xml

@@ -84,7 +84,7 @@
             </goals>
             <configuration>
               <url>
-                https://raw.githubusercontent.com/openvinotoolkit/model_server/main/src/kfserving_api/grpc_predict_v2.proto</url>
+                https://raw.githubusercontent.com/openvinotoolkit/model_server/releases/2026/1/src/kfserving_api/grpc_predict_v2.proto</url>
               <outputFileName>grpc_predict_v2.proto</outputFileName>
               <outputDirectory>src/main/proto</outputDirectory>
             </configuration>

demos/README.md

@@ -52,7 +52,7 @@ OpenVINO Model Server demos have been created to showcase the usage of the model
 |[VLM Text Generation with continuous batching](continuous_batching/vlm/README.md)|Generate text with VLM models and continuous batching pipeline|
 |[OpenAI API text embeddings ](embeddings/README.md)|Get text embeddings via endpoint compatible with OpenAI API|
 |[Reranking with Cohere API](rerank/README.md)| Rerank documents via endpoint compatible with Cohere|
-|[RAG with OpenAI API endpoint and langchain](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/rag/rag_demo.ipynb)| Example how to use RAG with model server endpoints|
+|[RAG with OpenAI API endpoint and langchain](https://github.com/openvinotoolkit/model_server/blob/releases/2026/1/demos/continuous_batching/rag/rag_demo.ipynb)| Example how to use RAG with model server endpoints|
 |[LLM on NPU](./llm_npu/README.md)| Generate text with LLM models and NPU acceleration|
 |[VLM on NPU](./vlm_npu/README.md)| Generate text with VLM models and NPU acceleration|
 |[Long context LLMs](./continuous_batching/long_context/README.md)| Recommendations for handling very long context in LLM models|
@@ -67,7 +67,7 @@ Check out the list below to see complete step-by-step examples of using OpenVINO
 | Demo | Description |
 |---|---|
 |[Image Classification](image_classification/python/README.md)|Run prediction on a JPEG image using image classification model via gRPC API.|
-|[Using ONNX Model](using_onnx_model/python/README.md)|Run prediction on a JPEG image using image classification ONNX model via gRPC API in two preprocessing variants. This demo uses [pipeline](../docs/dag_scheduler.md) with [image_transformation custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/image_transformation). |
+|[Using ONNX Model](using_onnx_model/python/README.md)|Run prediction on a JPEG image using image classification ONNX model via gRPC API in two preprocessing variants. This demo uses [pipeline](../docs/dag_scheduler.md) with [image_transformation custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2026/1/src/custom_nodes/image_transformation). |
 |[Using TensorFlow Model](image_classification_using_tf_model/python/README.md)|Run image classification using directly imported TensorFlow model. |
 |[Age gender recognition](age_gender_recognition/python/README.md) | Run prediction on a JPEG image using age gender recognition model via gRPC API.|
 |[Face Detection](face_detection/python/README.md)|Run prediction on a JPEG image using face detection model via gRPC API.|
@@ -95,13 +95,13 @@ Check out the list below to see complete step-by-step examples of using OpenVINO
 ## With DAG Pipelines
 | Demo | Description |
 |---|---|
-|[Horizontal Text Detection in Real-Time](horizontal_text_detection/python/README.md) | Run prediction on camera stream using a horizontal text detection model via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [horizontal_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/horizontal_ocr) and [demultiplexer](../docs/demultiplexing.md). |
-|[Optical Character Recognition Pipeline](optical_character_recognition/python/README.md) | Run prediction on a JPEG image using a pipeline of text recognition and text detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [east_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). |
+|[Horizontal Text Detection in Real-Time](horizontal_text_detection/python/README.md) | Run prediction on camera stream using a horizontal text detection model via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [horizontal_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2026/1/src/custom_nodes/horizontal_ocr) and [demultiplexer](../docs/demultiplexing.md). |
+|[Optical Character Recognition Pipeline](optical_character_recognition/python/README.md) | Run prediction on a JPEG image using a pipeline of text recognition and text detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [east_ocr custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2026/1/src/custom_nodes/east_ocr) and [demultiplexer](../docs/demultiplexing.md). |
 |[Single Face Analysis Pipeline](single_face_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a simple pipeline of age-gender recognition and emotion recognition models via gRPC API to analyze image with a single face. This demo uses [pipeline](../docs/dag_scheduler.md) |
-|[Multi Faces Analysis Pipeline](multi_faces_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a pipeline of age-gender recognition and emotion recognition models via gRPC API to extract multiple faces from the image and analyze all of them. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/model_zoo_intel_object_detection) and [demultiplexer](../docs/demultiplexing.md) |
+|[Multi Faces Analysis Pipeline](multi_faces_analysis_pipeline/python/README.md)|Run prediction on a JPEG image using a pipeline of age-gender recognition and emotion recognition models via gRPC API to extract multiple faces from the image and analyze all of them. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2026/1/src/custom_nodes/model_zoo_intel_object_detection) and [demultiplexer](../docs/demultiplexing.md) |
 |[Model Ensemble Pipeline](model_ensemble/python/README.md)|Combine multiple image classification models into one [pipeline](../docs/dag_scheduler.md) and aggregate results to improve classification accuracy. |
-|[Face Blur Pipeline](face_blur/python/README.md)|Detect faces and blur image using a pipeline of object detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [face_blur custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/face_blur). |
-|[Vehicle Analysis Pipeline](vehicle_analysis_pipeline/python/README.md)|Detect vehicles and recognize their attributes using a pipeline of vehicle detection and vehicle attributes recognition models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/main/src/custom_nodes/model_zoo_intel_object_detection). |
+|[Face Blur Pipeline](face_blur/python/README.md)|Detect faces and blur image using a pipeline of object detection models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [face_blur custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2026/1/src/custom_nodes/face_blur). |
+|[Vehicle Analysis Pipeline](vehicle_analysis_pipeline/python/README.md)|Detect vehicles and recognize their attributes using a pipeline of vehicle detection and vehicle attributes recognition models with a custom node for intermediate results processing via gRPC API. This demo uses [pipeline](../docs/dag_scheduler.md) with [model_zoo_intel_object_detection custom node](https://github.com/openvinotoolkit/model_server/tree/releases/2026/1/src/custom_nodes/model_zoo_intel_object_detection). |
 
 ## With C++ Client
 | Demo | Description |

demos/age_gender_recognition/python/README.md

@@ -53,7 +53,7 @@ Install python dependencies:
 ```console
 pip3 install -r requirements.txt
 ```
-Run [age_gender_recognition.py](https://github.com/openvinotoolkit/model_server/blob/main/demos/age_gender_recognition/python/age_gender_recognition.py) script to make an inference:
+Run [age_gender_recognition.py](https://github.com/openvinotoolkit/model_server/blob/releases/2026/1/demos/age_gender_recognition/python/age_gender_recognition.py) script to make an inference:
 ```console
 python age_gender_recognition.py --image_input_path age-gender-recognition-retail-0001.jpg --rest_port 8000
 ```

demos/audio/README.md

@@ -19,12 +19,12 @@ Check supported [Speech Recognition Models](https://openvinotoolkit.github.io/op
 ### Prepare speaker embeddings
 When generating speech you can use default speaker voice or you can prepare your own speaker embedding file. Here you can see how to do it with downloaded file from online repository, but you can try with your own speech recording as well:
 ```bash
-pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/audio/requirements.txt
+pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2026/1/demos/audio/requirements.txt
 mkdir -p audio_samples
 curl --output audio_samples/audio.wav "https://www.voiptroubleshooter.com/open_speech/american/OSR_us_000_0032_8k.wav"
 mkdir -p models
 mkdir -p models/speakers
-curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/audio/create_speaker_embedding.py -o create_speaker_embedding.py
+curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2026/1/demos/audio/create_speaker_embedding.py -o create_speaker_embedding.py
 python create_speaker_embedding.py audio_samples/audio.wav models/speakers/voice1.bin
 ```
 
@@ -41,8 +41,8 @@ Execution parameters will be defined inside the `graph.pbtxt` file.
 
 Download export script, install it's dependencies and create directory for the models:
 ```console
-curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/common/export_models/export_model.py -o export_model.py
-pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/common/export_models/requirements.txt
+curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2026/1/demos/common/export_models/export_model.py -o export_model.py
+pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2026/1/demos/common/export_models/requirements.txt
 mkdir models
 ```
 
@@ -180,8 +180,8 @@ Execution parameters will be defined inside the `graph.pbtxt` file.
 
 Download export script, install it's dependencies and create directory for the models:
 ```console
-curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/common/export_models/export_model.py -o export_model.py
-pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/common/export_models/requirements.txt
+curl https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2026/1/demos/common/export_models/export_model.py -o export_model.py
+pip3 install -r https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2026/1/demos/common/export_models/requirements.txt
 mkdir models
 ```
 

demos/benchmark/python/README.md

@@ -379,4 +379,4 @@ docker run -v ${PWD}/workspace:/workspace --network host benchmark_client -a loc
 ```
 
 Many other client options together with benchmarking examples are presented in
-[an additional PDF document](https://github.com/openvinotoolkit/model_server/blob/main/docs/python-benchmarking-client-16feb.pdf).
+[an additional PDF document](https://github.com/openvinotoolkit/model_server/blob/releases/2026/1/docs/python-benchmarking-client-16feb.pdf).

demos/bert_question_answering/python/README.md

@@ -4,7 +4,7 @@
 
 This document demonstrates how to run inference requests for [BERT model](https://github.com/openvinotoolkit/open_model_zoo/tree/2022.1.0/models/intel/bert-small-uncased-whole-word-masking-squad-int8-0002) with OpenVINO Model Server. It provides questions answering functionality.
 
-In this example docker container with [bert-client image](https://github.com/openvinotoolkit/model_server/blob/main/demos/bert_question_answering/python/Dockerfile) runs the script [bert_question_answering.py](https://github.com/openvinotoolkit/model_server/blob/main/demos/bert_question_answering/python/bert_question_answering.py). It runs inference request for each paragraph on a given page in order to answer the provided question. Since each paragraph can have different size the functionality of dynamic shape is used.
+In this example docker container with [bert-client image](https://github.com/openvinotoolkit/model_server/blob/releases/2026/1/demos/bert_question_answering/python/Dockerfile) runs the script [bert_question_answering.py](https://github.com/openvinotoolkit/model_server/blob/releases/2026/1/demos/bert_question_answering/python/bert_question_answering.py). It runs inference request for each paragraph on a given page in order to answer the provided question. Since each paragraph can have different size the functionality of dynamic shape is used.
 
 NOTE: With `min_request_token_num` parameter you can specify the minimum size of the request. If the paragraph has too short, it is concatenated with the next one until it has required length. When there is no paragraphs left to concatenate request is created with the remaining content.
 

demos/code_local_assistant/README.md

@@ -123,7 +123,7 @@ Models which are not published in OpenVINO format can be exported and quantized
 ```
 mkdir models
 python export_model.py text_generation --source_model unsloth/Devstral-Small-2507 --weight-format int4 --config_file_path models/config_all.json --model_repository_path models --tool_parser devstral --target_device GPU
-curl -L -o models/unsloth/Devstral-Small-2507/chat_template.jinja https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/extras/chat_template_examples/chat_template_devstral.jinja
+curl -L -o models/unsloth/Devstral-Small-2507/chat_template.jinja https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2026/1/extras/chat_template_examples/chat_template_devstral.jinja
 
 ovms --model_repository_path models --source_model unsloth/Devstral-Small-2507 --task text_generation --target_device GPU --tool_parser devstral --rest_port 8000 --cache_dir .ovcache
 ```

demos/continuous_batching/README.md

@@ -271,11 +271,11 @@ Check the demo [AI agent with MCP server and OpenVINO acceleration](./agentic_ai
 
 The service deployed above can be used in RAG chain using `langchain` library with OpenAI endpoint as the LLM engine.
 
-Check the example in the [RAG notebook](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/rag/rag_demo.ipynb)
+Check the example in the [RAG notebook](https://github.com/openvinotoolkit/model_server/blob/releases/2026/1/demos/continuous_batching/rag/rag_demo.ipynb)
 
 ## Scaling the Model Server
 
-Check this simple [text generation scaling demo](https://github.com/openvinotoolkit/model_server/blob/main/demos/continuous_batching/scaling/README.md).
+Check this simple [text generation scaling demo](https://github.com/openvinotoolkit/model_server/blob/releases/2026/1/demos/continuous_batching/scaling/README.md).
 
 ## Use Speculative Decoding
 

demos/continuous_batching/accuracy/README.md

@@ -115,7 +115,7 @@ Use [Berkeley function call leaderboard ](https://github.com/ShishirPatil/gorill
 git clone https://github.com/ShishirPatil/gorilla
 cd gorilla/berkeley-function-call-leaderboard
 git checkout 9b8a5202544f49a846aced185a340361231ef3e1
-curl -s https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/continuous_batching/accuracy/gorilla.patch | git apply -v
+curl -s https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2026/1/demos/continuous_batching/accuracy/gorilla.patch | git apply -v
 pip install -e . --extra-index-url "https://download.pytorch.org/whl/cpu"
 ```
 The commands below assumes the models is deployed with the name `ovms-model`. It must match the name set in the `bfcl_eval/constants/model_config.py`.