Demos#
Text Generation#
Demo |
Description |
|---|---|
Generate text with LLM models and continuous batching pipeline. |
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Generate text with VLM models and continuous batching pipeline. |
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OpenAI agents with MCP servers and serving LLM models. |
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Example how to use RAG with model server endpoints. |
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Recommendations for handling very long context in LLM models. |
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Generate structured (JSON) output from LLM models. |
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Speed up LLM inference with speculative decoding. |
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Generate text with LLM models and NPU acceleration. |
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Scale LLM serving across multiple CPUs and GPUs. |
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Serve GGUF models with OVMS. |
Image Generation#
Demo |
Description |
|---|---|
Generate images with diffusion models. |
Audio#
Demo |
Description |
|---|---|
Text-to-speech and automatic speech recognition demos. |
Text Embeddings#
Demo |
Description |
|---|---|
Get text embeddings via endpoint compatible with OpenAI API. |
Text Reranking#
Demo |
Description |
|---|---|
Rerank documents via endpoint compatible with Cohere. |
Classic Models#
Demo |
Description |
|---|---|
Run prediction on a JPEG image using image classification model via gRPC API. |
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Run prediction on a JPEG image using image classification ONNX model via gRPC API in two preprocessing variants. This demo uses pipeline with image_transformation custom node. |
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Run image classification using directly imported TensorFlow model. |
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Perform classification on an image with a PaddlePaddle model. |
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Run prediction on a JPEG image using age gender recognition model via gRPC API. |
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Run prediction on a JPEG image using face detection model via gRPC API. |
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Run prediction on a video file or camera stream using person, vehicle, bike detection model via gRPC API. |
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Handling AI model with text as the model input. |
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Handling AI model with string output. |
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Provide a knowledge source and a query and use BERT model for question answering via gRPC API. This demo uses dynamic shape feature. |
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Generate traffic and measure performance of the model served in OpenVINO Model Server. |
MediaPipe#
Demo |
Description |
|---|---|
A pipeline implementing object detection. |
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A pipeline implementing iris detection. |
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A complex pipeline linking several image analytical models and image transformations. |
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Analyze RTSP video stream in real time with generic application template for custom pre and post processing routines. |
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Basic example with a single inference node. |
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A chain of models in a graph. |
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Classify image according to provided labels using CLIP model embedded in a multi-node MediaPipe graph. |
Python Node#
Demo |
Description |
|---|---|
A pipeline implementing OpenClip classification in Python Node. |
Integrations#
Demo |
Description |
|---|---|
Using Open WebUI with OVMS as inference provider. Shows text and image generation as well as usage with RAG and tools. |
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Use Continue or Cline extension to Visual Studio Code with local OVMS serving. |