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Package: libqt6shadertools6
Description-md5: ca473c82ae3e7e5f7d02c12b50bc7b2b
Description-en: Qt 6 shader tools module
 Qt is a cross-platform C++ application framework. Qt's primary feature
 is its rich set of widgets that provide standard GUI functionality.
 .
 The QTShaderTools module contains APIs and tools to provide functionality
 for the shader pipeline used by QtQuick.

Package: qt6-shader-baker
Description-md5: ae182f11da5d060bdca03adbab3a952b
Description-en: Qt 6 shader baker
 Qt is a cross-platform C++ application framework. Qt's primary feature
 is its rich set of widgets that provide standard GUI functionality.
 .
 This package contains the Qt Shader Baker tool.

Package: qt6-shadertools-dev
Description-md5: 3aa2fd319eb37150820cc8d968a15788
Description-en: Qt 6 shader tools module - development files
 Qt is a cross-platform C++ application framework. Qt's primary feature
 is its rich set of widgets that provide standard GUI functionality.
 .
 This package contains the development files needed to build Qt 6 applications
 using the QtShaderTools library.
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Package: qt6-image-formats-plugins
Description-md5: 4c5d0d6005abcb19e90c5de3985ded59
Description-en: Qt 6 Image Formats plugins
 Qt is a cross-platform C++ application framework. Qt's primary feature
 is its rich set of widgets that provide standard GUI functionality.
 .
 This package contains plugins for adding support for ICNS, MNG, TGA, TIFF,
 WBMP and WEBP image formats.
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Package: ovn-doc
Description-md5: 5d9314c4dbce49af1fc81539f5c26499
Description-en: OVN documentation
 OVN, the Open Virtual Network, is a system to support virtual network
 abstraction.  OVN complements the existing capabilities of OVS to add
 native support for virtual network abstractions, such as virtual L2 and L3
 overlays and security groups.
 .
 This package provides documentation for configuration and use
 of OVN.

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Package: libllama-dev
Description-md5: 4a8ad0e6daf8ef8c891ab3ef35259ce8
Description-en: LLM inference in C/C++ - headers and development files
 The main goal of llama.cpp is to enable LLM inference with minimal setup and
 state-of-the-art performance on a wide range of hardware - locally and in the
 cloud.
 .
  * Plain C/C++ implementation without any dependencies
  * Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate
    and Metal frameworks
  * AVX, AVX2, AVX512 and AMX support for x86 architectures
  * 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization
    for faster inference and reduced memory use
  * Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs
    via HIP and Moore Threads MTT GPUs via MUSA)
  * Vulkan and SYCL backend support
  * CPU+GPU hybrid inference to partially accelerate models larger than the
    total VRAM capacity
 .
 The compute functionality is provided by ggml. By default, ggml's CPU backend
 is installed, but there are many other backends for CPUs and GPUs.
 .
 This package provides the llama.cpp library headers and development files.
 Note that these libraries are not yet stable, so they are installed to
 private directories for now.

Package: libllama0
Description-md5: 5de991542cb2b0126a0d383452d4ff1c
Description-en: LLM inference in C/C++ - libraries
 The main goal of llama.cpp is to enable LLM inference with minimal setup and
 state-of-the-art performance on a wide range of hardware - locally and in the
 cloud.
 .
  * Plain C/C++ implementation without any dependencies
  * Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate
    and Metal frameworks
  * AVX, AVX2, AVX512 and AMX support for x86 architectures
  * 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization
    for faster inference and reduced memory use
  * Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs
    via HIP and Moore Threads MTT GPUs via MUSA)
  * Vulkan and SYCL backend support
  * CPU+GPU hybrid inference to partially accelerate models larger than the
    total VRAM capacity
 .
 The compute functionality is provided by ggml. By default, ggml's CPU backend
 is installed, but there are many other backends for CPUs and GPUs.
 .
 This package contains the libllama and libmtmd libraries. Note that these
 libraries are not yet stable, so they are installed to private directories
 for now.

Package: llama.cpp
Description-md5: bb3c3ce0e424bace95d4308d7ebe68fe
Description-en: LLM inference in C/C++ - metapackage
 The main goal of llama.cpp is to enable LLM inference with minimal setup and
 state-of-the-art performance on a wide range of hardware - locally and in the
 cloud.
 .
  * Plain C/C++ implementation without any dependencies
  * Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate
    and Metal frameworks
  * AVX, AVX2, AVX512 and AMX support for x86 architectures
  * 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization
    for faster inference and reduced memory use
  * Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs
    via HIP and Moore Threads MTT GPUs via MUSA)
  * Vulkan and SYCL backend support
  * CPU+GPU hybrid inference to partially accelerate models larger than the
    total VRAM capacity
 .
 The compute functionality is provided by ggml. By default, ggml's CPU backend
 is installed, but there are many other backends for CPUs and GPUs.
 .
 This is a metapackage that either depends on all of the relevant binary
 packages.

Package: llama.cpp-examples
Description-md5: 63518dfb2e708789b4716606c474c9eb
Description-en: LLM inference in C/C++ - example programs
 The main goal of llama.cpp is to enable LLM inference with minimal setup and
 state-of-the-art performance on a wide range of hardware - locally and in the
 cloud.
 .
  * Plain C/C++ implementation without any dependencies
  * Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate
    and Metal frameworks
  * AVX, AVX2, AVX512 and AMX support for x86 architectures
  * 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization
    for faster inference and reduced memory use
  * Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs
    via HIP and Moore Threads MTT GPUs via MUSA)
  * Vulkan and SYCL backend support
  * CPU+GPU hybrid inference to partially accelerate models larger than the
    total VRAM capacity
 .
 The compute functionality is provided by ggml. By default, ggml's CPU backend
 is installed, but there are many other backends for CPUs and GPUs.
 .
 This package contains utilities that usptream ships as examples.

Package: llama.cpp-services
Description-md5: 1e95b796e388edf3ac2129f04a56c2eb
Description-en: LLM inference in C/C++ - systemd service files for utilities
 The main goal of llama.cpp is to enable LLM inference with minimal setup and
 state-of-the-art performance on a wide range of hardware - locally and in the
 cloud.
 .
  * Plain C/C++ implementation without any dependencies
  * Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate
    and Metal frameworks
  * AVX, AVX2, AVX512 and AMX support for x86 architectures
  * 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization
    for faster inference and reduced memory use
  * Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs
    via HIP and Moore Threads MTT GPUs via MUSA)
  * Vulkan and SYCL backend support
  * CPU+GPU hybrid inference to partially accelerate models larger than the
    total VRAM capacity
 .
 The compute functionality is provided by ggml. By default, ggml's CPU backend
 is installed, but there are many other backends for CPUs and GPUs.
 .
 This package ships systemd service files for some of the utilities. Currently,
 only llama-server is supported.

Package: llama.cpp-tests
Description-md5: 37299ac3dc827bd52fdf5986a3ae9923
Description-en: LLM inference in C/C++ - tests
 The main goal of llama.cpp is to enable LLM inference with minimal setup and
 state-of-the-art performance on a wide range of hardware - locally and in the
 cloud.
 .
  * Plain C/C++ implementation without any dependencies
  * Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate
    and Metal frameworks
  * AVX, AVX2, AVX512 and AMX support for x86 architectures
  * 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization
    for faster inference and reduced memory use
  * Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs
    via HIP and Moore Threads MTT GPUs via MUSA)
  * Vulkan and SYCL backend support
  * CPU+GPU hybrid inference to partially accelerate models larger than the
    total VRAM capacity
 .
 The compute functionality is provided by ggml. By default, ggml's CPU backend
 is installed, but there are many other backends for CPUs and GPUs.
 .
 This package contains all of the test binaries, mainly for autopkgtests.

Package: llama.cpp-tools
Description-md5: 16a799122663eb0cb3e10fbbd7da581a
Description-en: LLM inference in C/C++ - main utilities
 The main goal of llama.cpp is to enable LLM inference with minimal setup and
 state-of-the-art performance on a wide range of hardware - locally and in the
 cloud.
 .
  * Plain C/C++ implementation without any dependencies
  * Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate
    and Metal frameworks
  * AVX, AVX2, AVX512 and AMX support for x86 architectures
  * 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization
    for faster inference and reduced memory use
  * Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs
    via HIP and Moore Threads MTT GPUs via MUSA)
  * Vulkan and SYCL backend support
  * CPU+GPU hybrid inference to partially accelerate models larger than the
    total VRAM capacity
 .
 The compute functionality is provided by ggml. By default, ggml's CPU backend
 is installed, but there are many other backends for CPUs and GPUs.
 .
 This package contains the subset of the most commonly used utilities:
 llama-cli, llama-server, llama-bench, and llama-quantize.

Package: llama.cpp-tools-extra
Description-md5: f233ff68bae43b82fe1172961f41a066
Description-en: LLM inference in C/C++ - extra utilities
 The main goal of llama.cpp is to enable LLM inference with minimal setup and
 state-of-the-art performance on a wide range of hardware - locally and in the
 cloud.
 .
  * Plain C/C++ implementation without any dependencies
  * Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate
    and Metal frameworks
  * AVX, AVX2, AVX512 and AMX support for x86 architectures
  * 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization
    for faster inference and reduced memory use
  * Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs
    via HIP and Moore Threads MTT GPUs via MUSA)
  * Vulkan and SYCL backend support
  * CPU+GPU hybrid inference to partially accelerate models larger than the
    total VRAM capacity
 .
 The compute functionality is provided by ggml. By default, ggml's CPU backend
 is installed, but there are many other backends for CPUs and GPUs.
 .
 This package contains all tools that are not already shipped in package
 llama.cpp-tools.

Package: python3-gguf
Description-md5: b964819711d7955fae65898b48734422
Description-en: Python library for working with GGUF files
 GGUF is a file format for storing models for inference with GGML and executors
 based on GGML. GGUF is a binary format that is designed for fast loading and
 saving of models, and for ease of reading. Models are traditionally developed
 using PyTorch or another framework, and then converted to GGUF for use in
 GGML.
 .
 This package provides a Python library for reading and writing files in the
 GGUF format, and exposes this to the CLI in the form of a few utilities.

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