openvino-2026.1.0-bp160.1.1

- Update to 2026.1.0
- More GenAI coverage and framework integrations to minimize code
  changes
  * New models supported on CPUs & GPUs: Qwen3 VL
  * New models supported on CPUs: GPT-OSS 120B
  * Preview: Introducing the OpenVINO backend for llama.cpp,
    which enables optimized inference on Intel CPUs, GPUs, and
    NPUs. Validated on GGUF models such as
    Llama-3.2-1B-Instruct-GGUF, Phi-3-mini-4k-instruct-gguf,
    Qwen2.5-1.5B-Instruct-GGUF, and Mistral-7B-Instruct-v0.3.
  * New notebook: Unified VLM chatbot with video file support and
    interactive model switching across Qwen3-VL, Qwen2.5-VL, and
    LLaVa-NeXT-Video.
- Broader LLM model support and more model compression
  techniques
  * OpenVINO™ GenAI adds TaylorSeer Lite caching for image and
    video generation, accelerating diffusion-transformer inference
    across Flux, SD3, and LTX-Video pipelines, aligned with
    Hugging Face Diffusers.
  * LTX-Video generation on GPU achieves end-to-end acceleration
    through fusion of RMSNorm and RoPE operators, significantly
    improving video generation performance.
  * OpenVINO™ GenAI adds dynamic LoRA support for Qwen3-VL and
    VL models with LLM, allowing developers to swap adapters at
    runtime for efficient serving of multiple model variants in
    production without reloading the base model.
  * Preview: The release-weights API for ov::Model enables
    memory reclamation during model compilation on NPUs,
    delivering dramatically lower peak memory consumption for
    edge and client deployments. Users must set this property
    in ov::Model, and it will be applied during compilation.
- More portability and performance to run AI at the edge, in the
  cloud, or locally.
  * Introducing support for Intel® Core™ Series 3 processors
    (formerly codenamed Wildcat Lake) and Intel® Arc™ Pro B70
    Graphics with 32GB memory for single-GPU inference on 20-30B
    parameter LLMs
  * Prompt Lookup Decoding extended to vision-language pipelines,
    delivering significantly faster token generation for
    multimodal workloads on Intel CPUs and GPUs.
  * OpenVINO™ GenAI now has a smaller runtime footprint after
    eliminating ICU DLL dependencies from tokenization, leading
    to reduced memory usage, faster startup, and easier
    deployment.
  * OpenVINO GenAI introduces WhisperPipeline for Node.js via
    its NPM package, delivering production-ready speech
    recognition with word-level audio-to-text transcription.
  * OpenVINO™ Model Server enhances support for Qwen3-MOE and
    GPT-OSS-20b models, delivering improved performance,
    accuracy, and robust concurrent request handling with
    continuous batching. These pre-optimized models are
    available on Hugging Face for easy deployment.
    Additionally, the Model Server introduces image
    inpainting and outpainting capabilities via the
    /image endpoint for AI image editing.

- Update to 2026.0.0
- More GenAI coverage and framework integrations to minimize code
  changes
  * New models supported on CPUs & GPUs: GPT-OSS-20B,
    MiniCPM-V-4_5-8B, and MiniCPM-o-2.6.
  * New models supported on NPUs: MiniCPM-o-2.6. In addition, NPU
    support is now available on Qwen2.5-1.5B-Instruct,
    Qwen3-Embedding-0.6B, Qwen-2.5-coder-0.5B.
  * OpenVINO? GenAI now adds word-level timestamp functionality to
    the Whisper Pipeline on CPUs, GPUs, and NPUs, enabling more
    accurate transcriptions and subtitling in line with OpenAI and
    FasterWhisper implementations.
  * Phi-3-mini FastDraft model is now available on Hugging Face to
    accelerate LLM inference on NPUs. FastDraft optimizes
    speculative decoding for LLMs.
- Broader LLM model support and more model compression techniques
  * With the new int4 data-aware weight compression for 3D
    MatMuls, the Neural Network Compression Framework enables MoE
    LLMs to run with reduced memory, bandwidth, and improved
    accuracy compared to data-free schemes-delivering faster,
    more efficient deployment on resource-constrained devices.
  * Preview: the Neural Network Compression Framework now
    supports per-layer and per-group Look-Up Tables (LUT) for
    FP8-4BLUT quantization. This enables fine-grained,
    codebook-based compression that reduces model size and
    bandwidth while improving inference speed and accuracy for
    LLMs and transformer workloads.
- More portability and performance to run AI at the edge, in the
  cloud, or locally.
  * Preview: OpenVINO? GenAI adds VLM pipeline support to enhance
    Agentic AI framework integration.
  * OpenVINO GenAI now supports speculative decoding for NPUs,
    delivering improved performance and efficient text generation
    through a small draft model that is periodically validated by
    the full-size model.
  * Preview: NPU compiler integration with the NPU plugin enables
    ahead-of-time and on-device compilation without relying on
    OEM driver updates. Developers can enable this feature for a
    single, ready-to-ship package that reduces integration
    friction and accelerates time-to-value.
  * OpenVINO? Model Server adds enhanced support for audio
    endpoint plus agentic continuous batching and concurrent runs
    for improved LLM performance in agentic workflows on Intel
    CPUs and GPUs.
- Support Change and Deprecation Notices
  * Discontinued in 2026.0:
    + The deprecated openvino.runtime namespace has been removed.
    Please use the openvino namespace directly.
    + The deprecated openvino.Type.undefined has been removed.
    Please use openvino.Type.dynamic instead.
    + The PostponedConstant constructor signature has been
    updated for improved usability:
  - Old (removed): Callable[[Tensor], None]
  - New: Callable[[], Tensor]
    + The deprecated OpenVINO GenAI predefined generation
    configs were removed.
    + The deprecated OpenVINO GenAI support for whisper
    stateless decoder model has been removed. Please use a
    stateful model.
    + The deprecated OpenVINO GenAI StreamerBase put method, bool
    return type for callbacks, and ChunkStreamer class has been
    removed.
    + NNCF create_compressed_model() method is now deprecated and
    removed in 2026. Please use nncf.prune() method for
    unstructured pruning and nncf.quantize() for INT8
    quantization.
    + NNCF optimization methods for TensorFlow models and
    TensorFlow backend in NNCF are deprecated and removed in
    2026. It is recommended to use PyTorch analogous models for
    training-aware optimization methods and OpenVINO? IR,
    PyTorch, and ONNX models for post-training optimization
    methods from NNCF.
    + The following experimental NNCF methods are deprecated and
    removed: NAS, Structural Pruning, AutoML, Knowledge
    Distillation, Mixed-Precision Quantization, Movement
    Sparsity.
    + CPU plugin now requires support for the AVX2 instruction
    set as a minimum system requirement. The SSE instruction
    set will no longer be supported.
    + OpenVINO migrated builds based on RHEL 8 to RHEL 9.
    + manylinux2014 upgraded to manylinux_2_28. This aligns with
    modern toolchain requirements but also means that CentOS 7
    will no longer be supported due to glibc incompatibility.
    + MacOS x86 is no longer supported.
    + APT & YUM Repositories Restructure: Starting with release
    2025.1, users can switch to the new repository structure for
    APT and YUM, which no longer uses year-based subdirectories
    (like 2025). The old (legacy) structure is unavailable
    starting 2026.0. Detailed instructions are available on the
    relevant documentation pages:
  - Installation guide - yum
  - Installation guide - apt
    + OpenCV binaries removed from Docker images.
  * Deprecated and to be removed in the future:
    + Support for Ubuntu 20.04 has been discontinued due to the
    end of its standard support.
    + auto shape and auto batch size (reshaping a model in
    runtime) will be removed in the future. OpenVINO?s dynamic
    shape models are recommended instead.
    + With the release of Node.js v22, updated Node.js bindings
    are now available and compatible with the latest LTS
    version. These bindings do not support CentOS 7, as they
    rely on newer system libraries unavailable on legacy
    systems.
    + Starting with 2026.0 release major internal refactoring of
    the graph iteration mechanism has been implemented for
    improved performance and maintainability. The legacy path
    can be enabled by setting the ONNX_ITERATOR=0 environment
    variable. This legacy path is deprecated and will be
    removed in future releases.
    + OpenVINO Model Server:
  - The dedicated OpenVINO operator for Kubernetes and
    OpenShift is now deprecated in favor of the recommended
    KServe operator. The OpenVINO operator will remain
    functional in upcoming OpenVINO Model Server releases
    but will no longer be actively developed. Since KServe
    provides broader capabilities, no loss of functionality is
    expected. On the contrary, more functionalities will be
    accessible and migration between other serving solutions
    and OpenVINO Model Server will be much easier.
  - TensorFlow Serving (TFS) API support is planned for
    deprecation. With increasing adoption of the KServe API
    for classic models and the OpenAI API for generative
    workloads, usage of the TFS API has significantly
    declined. Dropping date is to be determined based on the
    feedback, with a tentative target of mid-2026.
  - Support for Stateful models will be deprecated. These
    capabilities were originally introduced for Kaldi audio
    models which is no longer relevant. Current audio models
    support relies on the OpenAI API, and pipelines
    implemented via OpenVINO GenAI library.
  - Directed Acyclic Graph Scheduler will be deprecated in
    favor of pipelines managed by MediaPipe scheduler and
    will be removed in 2026.3. That approach gives more
    flexibility, includes wider range of calculators and has
    support for using processing accelerators.

- Enable intel gpu option and add associated package.

- Update to 2025.4.0
  * Preview: Mixture of Experts (MoE) models optimized for CPUs and
  GPUs, validated for GPT-OSS 20B model. How to convert model:
  optimum-cli export openvino -m "openai/gpt-oss-20b" out_dir
  - -weight-format int4
  * Fixed issue ID 174531: Accuracy regression of
  Mistral-7b-instruct-v0.2 and Mistral-7b-instruct-v0.3
  on all devices when executed with OpenVINO GenAI.
  As a workaround, use the IR converted with OpenVINO 2025.3.
  * Fixed issue ID 176777: Using the callback parameter with the
  Python API call generate() in Text2ImagePipeline,
  Image2ImagePipeline, InpaintingPipeline may cause the process
  to hang. As a workaround, do not use the callback parameter.
  C++ implementations was not affected.
  * Resolved an issue in the NPU plugin where the Level Zero (L0)
  context was implemented as a static global object and only
  destroyed during DLL unload, even after unload_plugin()
  was called. This behavior prevented the driver from
  spawning threads required for certain optimizations and
  features.

- Update to 2025.4.0
- More GenAI coverage and framework integrations to minimize code
  changes
  * New models supported:
    + On CPUs & GPUs: Qwen3-Embedding-0.6B, Qwen3-Reranker-0.6B,
    Mistral-Small-24B-Instruct-2501.
    + On NPUs: Gemma-3-4b-it and Qwen2.5-VL-3B-Instruct.
  * Preview: Mixture of Experts (MoE) models optimized for CPUs
    and GPUs, validated for Qwen3-30B-A3B.
  * GenAI pipeline integrations: Qwen3-Embedding-0.6B and
    Qwen3-Reranker-0.6B for enhanced retrieval/ranking, and
    Qwen2.5VL-7B for video pipeline.
- Broader LLM model support and more model compression
  techniques
  * The Neural Network Compression Framework (NNCF) ONNX backend
    now supports INT8 static post-training quantization (PTQ)
    and INT8/INT4 weight-only compression to ensure accuracy
    parity with OpenVINO IR format models. SmoothQuant algorithm
    support added for INT8 quantization.
  * Accelerated multi-token generation for GenAI, leveraging
    optimized GPU kernels to deliver faster inference, smarter
    KV-cache reuse, and scalable LLM performance.
  * GPU plugin updates include improved performance with prefix
    caching for chat history scenarios and enhanced LLM accuracy
    with dynamic quantization support for INT8.
- More portability and performance to run AI at the edge, in the
  cloud, or locally.
  * Announcing support for Intel® Core Ultra Processor Series 3.
  * Encrypted blob format support added for secure model
    deployment with OpenVINO GenAI. Model weights and artifacts
    are stored and transmitted in an encrypted format, reducing
    risks of IP theft during deployment. Developers can deploy
    with minimal code changes using OpenVINO GenAI pipelines.
  * OpenVINO™ Model Server and OpenVINO™ GenAI now extend
    support for Agentic AI scenarios with new features such as
    output parsing and improved chat templates for reliable
    multi-turn interactions, and preview functionality for the
    Qwen3-30B-A3B model. OVMS also introduces a preview for
    audio endpoints.
  * NPU deployment is simplified with batch support, enabling
    seamless model execution across Intel® Core Ultra
    processors while eliminating driver dependencies. Models
    are reshaped to batch_size=1 before compilation.
  * The improved NVIDIA Triton Server* integration with
    OpenVINO backend now enables developers to utilize Intel
    GPUs or NPUs for deployment.

- Update to 2025.3.0
- More GenAI coverage and framework integrations to minimize code
  changes
  * New models supported: Phi-4-mini-reasoning, AFM-4.5B,
    Gemma-3-1B-it, Gemma-3-4B-it, and Gemma-3-12B,
  * NPU support added for: Qwen3-1.7B, Qwen3-4B, and Qwen3-8B.
  * LLMs optimized for NPU now available on OpenVINO Hugging
    Face collection.
- Broader LLM model support and more model compression techniques
  * The NPU plug-in adds support for longer contexts of up to
    8K tokens, dynamic prompts, and dynamic LoRA for improved
    LLM performance.
  * The NPU plug-in now supports dynamic batch sizes by reshaping
    the model to a batch size of 1 and concurrently managing
    multiple inference requests, enhancing performance and
    optimizing memory utilization.
  * Accuracy improvements for GenAI models on both built-in
    and discrete graphics achieved through the implementation
    of the key cache compression per channel technique, in
    addition to the existing KV cache per-token compression
    method.
  * OpenVINO™ GenAI introduces TextRerankPipeline for improved
    retrieval relevance and RAG pipeline accuracy, plus
    Structured Output for enhanced response reliability and
    function calling while ensuring adherence to predefined
    formats.
- More portability and performance to run AI at the edge,
  in the cloud, or locally.
  * Announcing support for Intel® Arc™ Pro B-Series
    (B50 and B60).
  * Preview: Hugging Face models that are GGUF-enabled for
    OpenVINO GenAI are now supported by the OpenVINO™ Model
    Server for popular LLM model architectures such as
    DeepSeek Distill, Qwen2, Qwen2.5, and Llama 3.
    This functionality reduces memory footprint and
    simplifies integration for GenAI workloads.
  * With improved reliability and tool call accuracy,
    the OpenVINO™ Model Server boosts support for
    agentic AI use cases on AI PCs, while enhancing
    performance on Intel CPUs, built-in GPUs, and NPUs.
  * int4 data-aware weights compression, now supported in the
    Neural Network Compression Framework (NNCF) for ONNX
    models, reduces memory footprint while maintaining
    accuracy and enables efficient deployment in
    resource-constrained environments.

- openSUSE Leap 16.0 compatibility

- Remove openvino-gcc5-compatibility.patch file

Summary of major features and improvements  
- More GenAI coverage and framework integrations to minimize code
  changes
  * New models supported on CPUs & GPUs: Phi-4,
    Mistral-7B-Instruct-v0.3, SD-XL Inpainting 0.1, Stable
    Diffusion 3.5 Large Turbo, Phi-4-reasoning, Qwen3, and
    Qwen2.5-VL-3B-Instruct. Mistral 7B Instruct v0.3 is also
    supported on NPUs.
  * Preview: OpenVINO ™ GenAI introduces a text-to-speech
    pipeline for the SpeechT5 TTS model, while the new RAG
    backend offers developers a simplified API that delivers
    reduced memory usage and improved performance.
  * Preview: OpenVINO™ GenAI offers a GGUF Reader for seamless
    integration of llama.cpp based LLMs, with Python and C++
    pipelines that load GGUF models, build OpenVINO graphs,
    and run GPU inference on-the-fly. Validated for popular models:
    DeepSeek-R1-Distill-Qwen (1.5B, 7B), Qwen2.5 Instruct
    (1.5B, 3B, 7B) & llama-3.2 Instruct (1B, 3B, 8B).
- Broader LLM model support and more model compression
  techniques
  * Further optimization of LoRA adapters in OpenVINO GenAI
    for improved LLM, VLM, and text-to-image model performance
    on built-in GPUs. Developers can use LoRA adapters to
    quickly customize models for specialized tasks.
  * KV cache compression for CPUs is enabled by default for
    INT8, providing a reduced memory footprint while maintaining
    accuracy compared to FP16. Additionally, it delivers
    substantial memory savings for LLMs with INT4 support compared
    to INT8.
  * Optimizations for Intel® Core™ Ultra Processor Series 2
    built-in GPUs and Intel® Arc™ B Series Graphics with the
    Intel® XMX systolic platform to enhance the performance of
    VLM models and hybrid quantized image generation models, as
    well as improve first-token latency for LLMs through dynamic
    quantization.
- More portability and performance to run AI at the edge, in the
  cloud, or locally.
  * Enhanced Linux* support with the latest GPU driver for
    built-in GPUs on Intel® Core™ Ultra Processor Series 2
    (formerly codenamed Arrow Lake H).
  * Support for INT4 data-free weights compression for ONNX
    models implemented in the Neural Network Compression
    Framework (NNCF).
  * NPU support for FP16-NF4 precision on Intel® Core™ 200V
    Series processors for models with up to 8B parameters is
    enabled through symmetrical and channel-wise quantization,
    improving accuracy while maintaining performance efficiency.
  Support Change and Deprecation Notices
- Discontinued in 2025:
  * Runtime components:
    + The OpenVINO property of Affinity API is no longer
    available. It has been replaced with CPU binding
    configurations (ov::hint::enable_cpu_pinning).
    + The openvino-nightly PyPI module has been discontinued.
    End-users should proceed with the Simple PyPI nightly repo
    instead. More information in Release Policy. The
    openvino-nightly PyPI module has been discontinued.
    End-users should proceed with the Simple PyPI nightly repo
    instead. More information in Release Policy.
  * Tools:
    + The OpenVINO™ Development Tools package (pip install
    openvino-dev) is no longer available for OpenVINO releases
    in 2025.
    + Model Optimizer is no longer available. Consider using the
    new conversion methods instead. For more details, see the
    model conversion transition guide.
    + Intel® Streaming SIMD Extensions (Intel® SSE) are currently
    not enabled in the binary package by default. They are
    still supported in the source code form.
    + Legacy prefixes: l_, w_, and m_ have been removed from
    OpenVINO archive names.
  * OpenVINO GenAI:
    + StreamerBase::put(int64_t token)
    + The Bool value for Callback streamer is no longer accepted.
    It must now return one of three values of StreamingStatus
    enum.
    + ChunkStreamerBase is deprecated. Use StreamerBase instead.
  * NNCF create_compressed_model() method is now deprecated.
    nncf.quantize() method is recommended for
    Quantization-Aware Training of PyTorch and TensorFlow models.
  * OpenVINO Model Server (OVMS) benchmark client in C++
    using TensorFlow Serving API.
- Deprecated and to be removed in the future:
  * Python 3.9 is now deprecated and will be unavailable after
    OpenVINO version 2025.4.
  * openvino.Type.undefined is now deprecated and will be removed
    with version 2026.0. openvino.Type.dynamic should be used
    instead.
  * APT & YUM Repositories Restructure: Starting with release
    2025.1, users can switch to the new repository structure
    for APT and YUM, which no longer uses year-based
    subdirectories (like “2025”). The old (legacy) structure
    will still be available until 2026, when the change will
    be finalized. Detailed instructions are available on the
    relevant documentation pages:
    + Installation guide - yum
    + Installation guide - apt
  * OpenCV binaries will be removed from Docker images in 2026.
  * Ubuntu 20.04 support will be deprecated in future OpenVINO
    releases due to the end of standard support.
  * “auto shape” and “auto batch size” (reshaping a model in
    runtime) will be removed in the future. OpenVINO’s dynamic
    shape models are recommended instead.
  * MacOS x86 is no longer recommended for use due to the
    discontinuation of validation. Full support will be removed
    later in 2025.
  * The openvino namespace of the OpenVINO Python API has been
    redesigned, removing the nested openvino.runtime module.
    The old namespace is now considered deprecated and will be
    discontinued in 2026.0.

- Fix file list for riscv64

- Do not force GCC15 on Tumblewed just yet: follow the distro
  default compiler, like any other package.

- openvino-gcc5-compatibility.patch to resolve incompatibility
  in gcc5

- Added gcc-14

- Update to 2025.1.0
- Downgrade from gcc13-c++ to 12 due to incompatibility in tbb
  compilation.  This was due to C++ libraries (using libstdc++)
  resulting in the error: libtbb.so.12: undefined reference to
  `__cxa_call_terminate@CXXABI_1.3.15'
- More GenAI coverage and framework integrations to minimize code
  changes
  * New models supported: Phi-4 Mini, Jina CLIP v1, and Bce
    Embedding Base v1.
  * OpenVINO™ Model Server now supports VLM models, including
    Qwen2-VL, Phi-3.5-Vision, and InternVL2.
  * OpenVINO GenAI now includes image-to-image and inpainting
    features for transformer-based pipelines, such as Flux.1 and
    Stable Diffusion 3 models, enhancing their ability to generate
    more realistic content.
  * Preview: AI Playground now utilizes the OpenVINO Gen AI backend
    to enable highly optimized inferencing performance on AI PCs.
- Broader LLM model support and more model compression techniques
  * Reduced binary size through optimization of the CPU plugin and
    removal of the GEMM kernel.
  * Optimization of new kernels for the GPU plugin significantly
    boosts the performance of Long Short-Term Memory (LSTM) models,
    used in many applications, including speech recognition,
    language modeling, and time series forecasting.
  * Preview: Token Eviction implemented in OpenVINO GenAI to reduce
    the memory consumption of KV Cache by eliminating unimportant
    tokens. This current Token Eviction implementation is
    beneficial for tasks where a long sequence is generated, such
    as chatbots and code generation.
  * NPU acceleration for text generation is now enabled in
    OpenVINO™ Runtime and OpenVINO™ Model Server to support the
    power-efficient deployment of VLM models on NPUs for AI PC use
    cases with low concurrency.
- More portability and performance to run AI at the edge, in the
  cloud, or locally.
  * Additional LLM performance optimizations on Intel® Core™ Ultra
    200H series processors for improved 2nd token latency on
    Windows and Linux.
  * Enhanced performance and efficient resource utilization with
    the implementation of Paged Attention and Continuous Batching
    by default in the GPU plugin.
  * Preview: The new OpenVINO backend for Executorch will enable
    accelerated inference and improved performance on Intel
    hardware, including CPUs, GPUs, and NPUs.

- Disabled JAX plugin beta.

- Update to 2025.0.0
- More GenAI coverage and framework integrations to minimize code
  changes
  * New models supported: Qwen 2.5, Deepseek-R1-Distill-Llama-8B,
    DeepSeek-R1-Distill-Qwen-7B, and DeepSeek-R1-Distill-Qwen-1.5B,
    FLUX.1 Schnell and FLUX.1 Dev
  * Whisper Model: Improved performance on CPUs, built-in GPUs,
    and discrete GPUs with GenAI API.
  * Preview: Introducing NPU support for torch.compile, giving
    developers the ability to use the OpenVINO backend to run the
    PyTorch API on NPUs. 300+ deep learning models enabled from
    the TorchVision, Timm, and TorchBench repositories..
- Broader Large Language Model (LLM) support and more model
  compression techniques.
  * Preview: Addition of Prompt Lookup to GenAI API improves 2nd
    token latency for LLMs by effectively utilizing predefined
    prompts that match the intended use case.
  * Preview: The GenAI API now offers image-to-image inpainting
    functionality. This feature enables models to generate
    realistic content by inpainting specified modifications and
    seamlessly integrating them with the original image.
  * Asymmetric KV Cache compression is now enabled for INT8 on
    CPUs, resulting in lower memory consumption and improved 2nd
    token latency, especially when dealing with long prompts that
    require significant memory. The option should be explicitly
    specified by the user.
- More portability and performance to run AI at the edge, in the
  cloud, or locally.
  * Support for the latest Intel® Core™ Ultra 200H series
    processors (formerly codenamed Arrow Lake-H)
  * Integration of the OpenVINO ™ backend with the Triton
    Inference Server allows developers to utilize the Triton
    server for enhanced model serving performance when deploying
    on Intel CPUs.
  * Preview: A new OpenVINO ™ backend integration allows
    developers to leverage OpenVINO performance optimizations
    directly within Keras 3 workflows for faster AI inference on
    CPUs, built-in GPUs, discrete GPUs, and NPUs. This feature is
    available with the latest Keras 3.8 release.
  * The OpenVINO Model Server now supports native Windows Server
    deployments, allowing developers to leverage better
    performance by eliminating container overhead and simplifying
    GPU deployment.
- Support Change and Deprecation Notices
  * Now deprecated:
    + Legacy prefixes l_, w_, and m_ have been removed from
    OpenVINO archive names.
    + The runtime namespace for Python API has been marked as
    deprecated and designated to be removed for 2026.0. The
    new namespace structure has been delivered, and migration
    is possible  immediately. Details will be communicated
    through warnings andvia documentation.
    + NNCF create_compressed_model() method is deprecated.
    nncf.quantize() method is now recommended for
    Quantization-Aware Training of PyTorch and
    TensorFlow models.