refresh configuration docs - #4414
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Jul 29, 2026
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| Traditional AI models perform data analysis in a single inference operation. They can be used over KServe API or TensorFlow API. | ||
| Classic models perform data analysis in a single inference operation. They can be served using the KServe API or TensorFlow API. |
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| Classic models perform data analysis in a single inference operation. They can be served using the KServe API or TensorFlow API. | |
| Classic models perform data analysis in a single inference operation. They can be served using the KServe API. |
| # OVMS Pull mode {#ovms_docs_pull} | ||
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| This document describes how to leverage OpenVINO Model Server (OVMS) pull feature to automate deployment configuration with Generative AI models. When pulling from [Hugging Face Hub](https://huggingface.co/) or when pulling GGUF model, no additional steps are required. However, when pulling models in Pytorch format, you have to install additional python dependencies when using baremetal execution so that optimum-cli is available for ovms executable or rely on the docker image `openvino/model_server:latest-py`. In summary you have 3 options: | ||
| This document describes how to use the OpenVINO Model Server (OVMS) pull feature to automate deployment configuration for Generative AI models. When pulling models from [Hugging Face Hub](https://huggingface.co/) in IR or GGUF format, no additional setup is required. However, when pulling models in PyTorch format, you need additional Python dependencies on a bare-metal host so that `optimum-cli` is available to the OVMS executable, or you can use the `openvino/model_server:latest-py` Docker image. In summary, you have three options: |
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Are we ensuring latest-py tag being up to date? In the last release we did not push latest-py.
Last -py image release was for 2026.1.
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| > **Note:** These models aren't tested properly and their accuracy or performance may be low. | ||
| > **Note:** These models are tested by OpenVINO team, and their accuracy or performance is not guaranteed. |
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Is that intentional? That would mean that models provided and tested by OV do not come with guaranteed quality. Shouldn't it be are not tested?
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| *Note:* Use relative or absolute paths. `config_path` is relative to the current folder. `model_path` is relative to the config file. With `model_repository_path`, `model_name` represents a folder relative to model repository path. | ||
| > **Note:** Use relative or absolute paths. `config_path` is relative to the current folder. `model_path` is relative to the config file. With `model_repository_path`, `model_name` represents a folder relative to the model repository path. |
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For model_path I guess it depends if we talk about model path in config.json, or model_path in a single model mode provided from the CLI right? But I guess it's fine if in that section we are talking about add_to_config case.
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