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<package pkgid="7d5434c117b07a206d3c45325ff8b8f83bb0eacf010a60d9fa59838591c1b756e2086e2117d4ff250277e22974c750b4f70f700bf2a08d6130dc9dfab3a1abf8" name="ollama" arch="src">
  <version epoch="0" ver="0.30.10" rel="9.1"/>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776686400">- disable vulkan build on Leap 16.0 and before
  16.1 is supposed to get a newer glslang stack to fix the support</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1777118400">- Update to version 0.21.2:
  * Improved reliability of the OpenClaw onboarding flow in
    ollama launch
  * Recommended models in ollama launch now appear in a fixed,
    canonical order
  * OpenClaw integration now bundles Ollama's web search plugin in
    OpenClaw
- Update to version 0.21.1:
  * You can now install and run the Kimi CLI through Ollama
  * GLM4 MoE Lite performance improvement with a fused sigmoid
    router head
  * Fixed structured outputs for Gemma 4 when think=false</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1777636800">- Update to version 0.22.1:
  * Updated the Gemma 4 renderer for thinking and tool calling improvements
  * Model recommendations are now updated without updating Ollama
  * Aligned the desktop app's launch page with ollama launch integrations
  * Fixed the Poolside integration title in ollama launch
- Update to version 0.22.0:
  * NVIDIA's Nemotron 3 Omni
  * Poolside's first open-weight coding model - Laguna XS.2</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1777896000">- Update to version 0.23.0:
  * Launch Claude Desktop with ollama launch claude-desktop
  * The Ollama app now surfaces featured models from
    server-driven recommendations
  * Fixed OpenClaw gateway timeout on Windows by enforcing
    IPv4 loopback
  * Hardened Metal initialization to gracefully handle ggml
    kernel compilation failures</changelog>
  <changelog author="Dirk M&#xFC;ller &lt;dmueller@suse.com&gt;" date="1778155200">- update to 0.23.1:
  * Update MLX and MLX-C with threading fixes
  * go: bump to 1.26
  * Add Gemma 4 MTP speculative decoding</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1778500800">- update to 0.23.2:
  * ollama launch no longer includes Claude Desktop due to the
    third-party integration being limited to Anthropic models.
  * Use ollama launch claude-desktop --restore to restore
    Claude Desktop to its normal state.
  * /api/show responses are now cached, improving median latency
    by ~6.7x which will increase load speed for integrations
    like VS Code.
  * Improved backup workflow when managing launch integrations</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1779192000">- update to 0.24.0:
  * Codex App support</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1779451200">- Update to version 0.30.0
  NOTE: quite some models are currently unsupported due to
    format changes.
- llama.cpp source is not bundled anymore. Instead a git url
  and tag is used. We vendor these sources via a _service.
- _multibuild is used to parallize the builds of the backend
  plugins of rocm, vulkan and cuda
- mlx cuda support adding is unfinished</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780488000">- update to 0.30.2:
  * feat(launch): show and auto-install Cline CLI
  * log template details to aid troubleshooting
  * cmd/launch: add Qwen code integration
  * launch: fix opencode local model limits
  * llm: include cached prompt tokens in llama-server counts
  * Harden app markdown URL handling
  * discover: allow Radeon 8060S iGPU
  * llm: detect llama-server load stalls from output
  * More harden app markdown URL handling
  * llama.cpp version update
  * launch: isolate Codex launch configuration
  * llama: add laguna (poolside) arch via a llama.cpp patch under llama/c…
  * docs: configure hermes desktop app
  * llm: ignore llama-server SSE ping comments
  * fix laguna patch build breakage
  * llama.cpp b9478 added a default 30s SSE ping that emits colon-only
    comment frames (":\n\n") while streamed requests are idle; Ollama
    treated non-data SSE lines as JSON, so skip SSE comments in completion
    and chat streams.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780660800">- update to 0.30.5:
  * Fix gemma4:12b floating point exception crash
  * llama.cpp version update
  * models: add support for gemma4-12b</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780833600">- Update to 0.30.6:
  * Gemma 4 QAT weights: the Gemma 4 family is now optimized with
    Quantization-Aware Training (QAT) to dramatically reduce memory
    requirements and maximize on-device performance.
  * ollama launch omp now integrates with Oh My Pi, an AI coding
    agent with IDE integration
  * Disable WebUI:
    disable-llama.cpp-ui.patch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781438400">- Update to 0.30.8:
  * Fixed ollama launch selecting the wrong provider in some cases
  * Improved prompt caching by decoupling it from context shift
    for better KV cache reuse
  * More stable MLX inference with hardened linear and embedding layers
  * MLX runner now creates snapshots during prompt processing and
    speculative decoding for improved reliability
  * Improved recurrent model support with per-boundary states from
    the gated-delta kernels</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781697600">- Update to 0.30.9:
  * Support for Cohere2Moe architecture
  * Fixed LFM2 parser/render for cases where thinking was not emitted
  * Fixed issue where ollama launch claude and other coding agent or assistant use cases would only output one token
  * Ollama will now return an error if a single message is larger than the current context window
- switch to tar balls because we use git lfs now</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781870400">- Update to 0.30.10:
  * Updated the underlying llama.cpp engine to build 9672</changelog>
</package>
<package pkgid="060201af40016df9899e1e9576cd2949f9d23f4e7de0c169c146cdefa7b682bd3868b325af9e185bef6a89c61ceb542664abaf71e763d851c04e23a027a9217f" name="ollama" arch="src">
  <version epoch="0" ver="0.32.1" rel="11.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1777118400">- Update to version 0.21.2:
  * Improved reliability of the OpenClaw onboarding flow in
    ollama launch
  * Recommended models in ollama launch now appear in a fixed,
    canonical order
  * OpenClaw integration now bundles Ollama's web search plugin in
    OpenClaw
- Update to version 0.21.1:
  * You can now install and run the Kimi CLI through Ollama
  * GLM4 MoE Lite performance improvement with a fused sigmoid
    router head
  * Fixed structured outputs for Gemma 4 when think=false</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1777636800">- Update to version 0.22.1:
  * Updated the Gemma 4 renderer for thinking and tool calling improvements
  * Model recommendations are now updated without updating Ollama
  * Aligned the desktop app's launch page with ollama launch integrations
  * Fixed the Poolside integration title in ollama launch
- Update to version 0.22.0:
  * NVIDIA's Nemotron 3 Omni
  * Poolside's first open-weight coding model - Laguna XS.2</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1777896000">- Update to version 0.23.0:
  * Launch Claude Desktop with ollama launch claude-desktop
  * The Ollama app now surfaces featured models from
    server-driven recommendations
  * Fixed OpenClaw gateway timeout on Windows by enforcing
    IPv4 loopback
  * Hardened Metal initialization to gracefully handle ggml
    kernel compilation failures</changelog>
  <changelog author="Dirk M&#xFC;ller &lt;dmueller@suse.com&gt;" date="1778155200">- update to 0.23.1:
  * Update MLX and MLX-C with threading fixes
  * go: bump to 1.26
  * Add Gemma 4 MTP speculative decoding</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1778500800">- update to 0.23.2:
  * ollama launch no longer includes Claude Desktop due to the
    third-party integration being limited to Anthropic models.
  * Use ollama launch claude-desktop --restore to restore
    Claude Desktop to its normal state.
  * /api/show responses are now cached, improving median latency
    by ~6.7x which will increase load speed for integrations
    like VS Code.
  * Improved backup workflow when managing launch integrations</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1779192000">- update to 0.24.0:
  * Codex App support</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1779451200">- Update to version 0.30.0
  NOTE: quite some models are currently unsupported due to
    format changes.
- llama.cpp source is not bundled anymore. Instead a git url
  and tag is used. We vendor these sources via a _service.
- _multibuild is used to parallize the builds of the backend
  plugins of rocm, vulkan and cuda
- mlx cuda support adding is unfinished</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780488000">- update to 0.30.2:
  * feat(launch): show and auto-install Cline CLI
  * log template details to aid troubleshooting
  * cmd/launch: add Qwen code integration
  * launch: fix opencode local model limits
  * llm: include cached prompt tokens in llama-server counts
  * Harden app markdown URL handling
  * discover: allow Radeon 8060S iGPU
  * llm: detect llama-server load stalls from output
  * More harden app markdown URL handling
  * llama.cpp version update
  * launch: isolate Codex launch configuration
  * llama: add laguna (poolside) arch via a llama.cpp patch under llama/c…
  * docs: configure hermes desktop app
  * llm: ignore llama-server SSE ping comments
  * fix laguna patch build breakage
  * llama.cpp b9478 added a default 30s SSE ping that emits colon-only
    comment frames (":\n\n") while streamed requests are idle; Ollama
    treated non-data SSE lines as JSON, so skip SSE comments in completion
    and chat streams.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780660800">- update to 0.30.5:
  * Fix gemma4:12b floating point exception crash
  * llama.cpp version update
  * models: add support for gemma4-12b</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780833600">- Update to 0.30.6:
  * Gemma 4 QAT weights: the Gemma 4 family is now optimized with
    Quantization-Aware Training (QAT) to dramatically reduce memory
    requirements and maximize on-device performance.
  * ollama launch omp now integrates with Oh My Pi, an AI coding
    agent with IDE integration
  * Disable WebUI:
    disable-llama.cpp-ui.patch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781438400">- Update to 0.30.8:
  * Fixed ollama launch selecting the wrong provider in some cases
  * Improved prompt caching by decoupling it from context shift
    for better KV cache reuse
  * More stable MLX inference with hardened linear and embedding layers
  * MLX runner now creates snapshots during prompt processing and
    speculative decoding for improved reliability
  * Improved recurrent model support with per-boundary states from
    the gated-delta kernels</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781697600">- Update to 0.30.9:
  * Support for Cohere2Moe architecture
  * Fixed LFM2 parser/render for cases where thinking was not emitted
  * Fixed issue where ollama launch claude and other coding agent or assistant use cases would only output one token
  * Ollama will now return an error if a single message is larger than the current context window
- switch to tar balls because we use git lfs now</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781870400">- Update to 0.30.10:
  * Updated the underlying llama.cpp engine to build 9672</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1782561600">- Update to 0.30.11:
  * launch: add thinking capability detection to opencode
  * launch: auto-install Claude Code
  * launch: auto-install opencode when missing
  * discover: fix inverted iGPU/dGPU Vulkan classification on Windows hybrid graphics
  * mlxrunner: unify and tune speculative decoding
  * launch/codex: detect model drift when Codex App UI switches
  * llama: add sm_86 architecture to cuda_v13_windows preset
  * llm: size mmproj offload
  * docs: document max think level
  * llm: preserve generation headroom for shifted prompts
  * llama: default qwen2.5vl window attention metadata
  * llm: use host Vulkan loader on Windows
  * mlx: update and fix CUDA JIT packaging
  * llm: fix ollama ps double-counting mmap'd weights on partial offload
  * docs: redesign docs landing and integrations overview
  * server: align generate with native chat templates
  * jetson: add CC 87 for CUDA v13
  * llama.cpp version update
- Updated generated using pbuild-ai
  * Helper script provided in tool-scripts/ directory
    Use    pbuild-ai --allow-tool-scripts     to enable it.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1782993600">- Updated to version 0.31.1
  * Tightened Gemma 4 MoE model loading in the MLX engine
  * Updated the MLX engine to the latest version, including a new small-batch matmul kernel
  * Updated the underlying llama.cpp engine to build 9840
  * Improved Gemma 4 multi-token prediction (MTP) performance
  * new versions of llama.cpp and mlx</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783425600">- Define a build counter sync tag, to ensure all flavors have
  the same build number</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783425601">- Define a build counter sync tag, to ensure all flavors have
  the same build number</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783598400">- Update to version 0.31.2
  * Enabled flash attention on older NVIDIA GPUs (compute capability 6.x)
  * iGPU can now offload vision models with padding to fit available memory
  * Fixed structured output for thinking models when thinking is disabled
  * Hardened GGUF model creation
  * ollama launch for Claude Code now disables telemetry by default
  * Fixed loading models on paths with non-UTF-8 characters
  * Updated the MLX and llama.cpp engines</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1784030400">- Update to version 0.32.0
  * New interactive agent experience: running ollama now launches
    an agent to help you code and delegate work
  * Renamed the Codex App integration to ChatGPT: use ollama launch
    chatgpt (and --restore to return to your usual ChatGPT profile)
  * Simplified integration selection: the ollama launch menu now
    only offers the most popular integrations (other integrations
    can be accessed through ollama launch
  * Warns before launching older agent models: CodeLlama, Qwen2.5(-coder),
    Llama 3.x, Mistral, StarCoder, and the base DeepSeek-R1 tags now
    prompt a deprecation warning before ollama launch continues</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1784289600">- Update to version 0.32.1
  * Improved Gemma 4 tool calling and multi-turn reasoning,
    including more reliable tool-response continuations
  * Fixed a recurrent MLX model cache leak that could increase
    memory use across requests, and improved cache snapshot performance
  * MLX text model loading now respects OLLAMA_LOAD_TIMEOUT
  * Agent web search and fetch now tell users to run ollama
    signin when authentication is required
  * The interactive agent now receives the current working directory
    for better project context
  * Fixed ollama launch so choosing Pick another model for a
    deprecated model passed with --model opens the model picker
  * Updated VS Code setup documentation for the official Ollama extension</changelog>
</package>
<package pkgid="2485ef8dee9f768cc4abf9b9b9cdfc96cfc1af35ee997165219b3afdfa02277d3296d18369bba6105533365c6fbeb8bcfe85db023c7e12b690193bc65fa2b5ef" name="ollama" arch="x86_64">
  <version epoch="0" ver="0.32.1" rel="11.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1777118400">- Update to version 0.21.2:
  * Improved reliability of the OpenClaw onboarding flow in
    ollama launch
  * Recommended models in ollama launch now appear in a fixed,
    canonical order
  * OpenClaw integration now bundles Ollama's web search plugin in
    OpenClaw
- Update to version 0.21.1:
  * You can now install and run the Kimi CLI through Ollama
  * GLM4 MoE Lite performance improvement with a fused sigmoid
    router head
  * Fixed structured outputs for Gemma 4 when think=false</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1777636800">- Update to version 0.22.1:
  * Updated the Gemma 4 renderer for thinking and tool calling improvements
  * Model recommendations are now updated without updating Ollama
  * Aligned the desktop app's launch page with ollama launch integrations
  * Fixed the Poolside integration title in ollama launch
- Update to version 0.22.0:
  * NVIDIA's Nemotron 3 Omni
  * Poolside's first open-weight coding model - Laguna XS.2</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1777896000">- Update to version 0.23.0:
  * Launch Claude Desktop with ollama launch claude-desktop
  * The Ollama app now surfaces featured models from
    server-driven recommendations
  * Fixed OpenClaw gateway timeout on Windows by enforcing
    IPv4 loopback
  * Hardened Metal initialization to gracefully handle ggml
    kernel compilation failures</changelog>
  <changelog author="Dirk M&#xFC;ller &lt;dmueller@suse.com&gt;" date="1778155200">- update to 0.23.1:
  * Update MLX and MLX-C with threading fixes
  * go: bump to 1.26
  * Add Gemma 4 MTP speculative decoding</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1778500800">- update to 0.23.2:
  * ollama launch no longer includes Claude Desktop due to the
    third-party integration being limited to Anthropic models.
  * Use ollama launch claude-desktop --restore to restore
    Claude Desktop to its normal state.
  * /api/show responses are now cached, improving median latency
    by ~6.7x which will increase load speed for integrations
    like VS Code.
  * Improved backup workflow when managing launch integrations</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1779192000">- update to 0.24.0:
  * Codex App support</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1779451200">- Update to version 0.30.0
  NOTE: quite some models are currently unsupported due to
    format changes.
- llama.cpp source is not bundled anymore. Instead a git url
  and tag is used. We vendor these sources via a _service.
- _multibuild is used to parallize the builds of the backend
  plugins of rocm, vulkan and cuda
- mlx cuda support adding is unfinished</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780488000">- update to 0.30.2:
  * feat(launch): show and auto-install Cline CLI
  * log template details to aid troubleshooting
  * cmd/launch: add Qwen code integration
  * launch: fix opencode local model limits
  * llm: include cached prompt tokens in llama-server counts
  * Harden app markdown URL handling
  * discover: allow Radeon 8060S iGPU
  * llm: detect llama-server load stalls from output
  * More harden app markdown URL handling
  * llama.cpp version update
  * launch: isolate Codex launch configuration
  * llama: add laguna (poolside) arch via a llama.cpp patch under llama/c…
  * docs: configure hermes desktop app
  * llm: ignore llama-server SSE ping comments
  * fix laguna patch build breakage
  * llama.cpp b9478 added a default 30s SSE ping that emits colon-only
    comment frames (":\n\n") while streamed requests are idle; Ollama
    treated non-data SSE lines as JSON, so skip SSE comments in completion
    and chat streams.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780660800">- update to 0.30.5:
  * Fix gemma4:12b floating point exception crash
  * llama.cpp version update
  * models: add support for gemma4-12b</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780833600">- Update to 0.30.6:
  * Gemma 4 QAT weights: the Gemma 4 family is now optimized with
    Quantization-Aware Training (QAT) to dramatically reduce memory
    requirements and maximize on-device performance.
  * ollama launch omp now integrates with Oh My Pi, an AI coding
    agent with IDE integration
  * Disable WebUI:
    disable-llama.cpp-ui.patch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781438400">- Update to 0.30.8:
  * Fixed ollama launch selecting the wrong provider in some cases
  * Improved prompt caching by decoupling it from context shift
    for better KV cache reuse
  * More stable MLX inference with hardened linear and embedding layers
  * MLX runner now creates snapshots during prompt processing and
    speculative decoding for improved reliability
  * Improved recurrent model support with per-boundary states from
    the gated-delta kernels</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781697600">- Update to 0.30.9:
  * Support for Cohere2Moe architecture
  * Fixed LFM2 parser/render for cases where thinking was not emitted
  * Fixed issue where ollama launch claude and other coding agent or assistant use cases would only output one token
  * Ollama will now return an error if a single message is larger than the current context window
- switch to tar balls because we use git lfs now</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781870400">- Update to 0.30.10:
  * Updated the underlying llama.cpp engine to build 9672</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1782561600">- Update to 0.30.11:
  * launch: add thinking capability detection to opencode
  * launch: auto-install Claude Code
  * launch: auto-install opencode when missing
  * discover: fix inverted iGPU/dGPU Vulkan classification on Windows hybrid graphics
  * mlxrunner: unify and tune speculative decoding
  * launch/codex: detect model drift when Codex App UI switches
  * llama: add sm_86 architecture to cuda_v13_windows preset
  * llm: size mmproj offload
  * docs: document max think level
  * llm: preserve generation headroom for shifted prompts
  * llama: default qwen2.5vl window attention metadata
  * llm: use host Vulkan loader on Windows
  * mlx: update and fix CUDA JIT packaging
  * llm: fix ollama ps double-counting mmap'd weights on partial offload
  * docs: redesign docs landing and integrations overview
  * server: align generate with native chat templates
  * jetson: add CC 87 for CUDA v13
  * llama.cpp version update
- Updated generated using pbuild-ai
  * Helper script provided in tool-scripts/ directory
    Use    pbuild-ai --allow-tool-scripts     to enable it.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1782993600">- Updated to version 0.31.1
  * Tightened Gemma 4 MoE model loading in the MLX engine
  * Updated the MLX engine to the latest version, including a new small-batch matmul kernel
  * Updated the underlying llama.cpp engine to build 9840
  * Improved Gemma 4 multi-token prediction (MTP) performance
  * new versions of llama.cpp and mlx</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783425600">- Define a build counter sync tag, to ensure all flavors have
  the same build number</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783425601">- Define a build counter sync tag, to ensure all flavors have
  the same build number</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783598400">- Update to version 0.31.2
  * Enabled flash attention on older NVIDIA GPUs (compute capability 6.x)
  * iGPU can now offload vision models with padding to fit available memory
  * Fixed structured output for thinking models when thinking is disabled
  * Hardened GGUF model creation
  * ollama launch for Claude Code now disables telemetry by default
  * Fixed loading models on paths with non-UTF-8 characters
  * Updated the MLX and llama.cpp engines</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1784030400">- Update to version 0.32.0
  * New interactive agent experience: running ollama now launches
    an agent to help you code and delegate work
  * Renamed the Codex App integration to ChatGPT: use ollama launch
    chatgpt (and --restore to return to your usual ChatGPT profile)
  * Simplified integration selection: the ollama launch menu now
    only offers the most popular integrations (other integrations
    can be accessed through ollama launch
  * Warns before launching older agent models: CodeLlama, Qwen2.5(-coder),
    Llama 3.x, Mistral, StarCoder, and the base DeepSeek-R1 tags now
    prompt a deprecation warning before ollama launch continues</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1784289600">- Update to version 0.32.1
  * Improved Gemma 4 tool calling and multi-turn reasoning,
    including more reliable tool-response continuations
  * Fixed a recurrent MLX model cache leak that could increase
    memory use across requests, and improved cache snapshot performance
  * MLX text model loading now respects OLLAMA_LOAD_TIMEOUT
  * Agent web search and fetch now tell users to run ollama
    signin when authentication is required
  * The interactive agent now receives the current working directory
    for better project context
  * Fixed ollama launch so choosing Pick another model for a
    deprecated model passed with --model opens the model picker
  * Updated VS Code setup documentation for the official Ollama extension</changelog>
</package>
<package pkgid="b1b198b8d8521e6500d99feece7744bf21f56a3f9664eba50eb1c4569e9061909a9b29b14f5306aba57872adf56e2a49c246747540bfccc5438d03c7caf12257" name="ollama-vulkan" arch="x86_64">
  <version epoch="0" ver="0.32.1" rel="11.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1777118400">- Update to version 0.21.2:
  * Improved reliability of the OpenClaw onboarding flow in
    ollama launch
  * Recommended models in ollama launch now appear in a fixed,
    canonical order
  * OpenClaw integration now bundles Ollama's web search plugin in
    OpenClaw
- Update to version 0.21.1:
  * You can now install and run the Kimi CLI through Ollama
  * GLM4 MoE Lite performance improvement with a fused sigmoid
    router head
  * Fixed structured outputs for Gemma 4 when think=false</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1777636800">- Update to version 0.22.1:
  * Updated the Gemma 4 renderer for thinking and tool calling improvements
  * Model recommendations are now updated without updating Ollama
  * Aligned the desktop app's launch page with ollama launch integrations
  * Fixed the Poolside integration title in ollama launch
- Update to version 0.22.0:
  * NVIDIA's Nemotron 3 Omni
  * Poolside's first open-weight coding model - Laguna XS.2</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1777896000">- Update to version 0.23.0:
  * Launch Claude Desktop with ollama launch claude-desktop
  * The Ollama app now surfaces featured models from
    server-driven recommendations
  * Fixed OpenClaw gateway timeout on Windows by enforcing
    IPv4 loopback
  * Hardened Metal initialization to gracefully handle ggml
    kernel compilation failures</changelog>
  <changelog author="Dirk M&#xFC;ller &lt;dmueller@suse.com&gt;" date="1778155200">- update to 0.23.1:
  * Update MLX and MLX-C with threading fixes
  * go: bump to 1.26
  * Add Gemma 4 MTP speculative decoding</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1778500800">- update to 0.23.2:
  * ollama launch no longer includes Claude Desktop due to the
    third-party integration being limited to Anthropic models.
  * Use ollama launch claude-desktop --restore to restore
    Claude Desktop to its normal state.
  * /api/show responses are now cached, improving median latency
    by ~6.7x which will increase load speed for integrations
    like VS Code.
  * Improved backup workflow when managing launch integrations</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1779192000">- update to 0.24.0:
  * Codex App support</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1779451200">- Update to version 0.30.0
  NOTE: quite some models are currently unsupported due to
    format changes.
- llama.cpp source is not bundled anymore. Instead a git url
  and tag is used. We vendor these sources via a _service.
- _multibuild is used to parallize the builds of the backend
  plugins of rocm, vulkan and cuda
- mlx cuda support adding is unfinished</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780488000">- update to 0.30.2:
  * feat(launch): show and auto-install Cline CLI
  * log template details to aid troubleshooting
  * cmd/launch: add Qwen code integration
  * launch: fix opencode local model limits
  * llm: include cached prompt tokens in llama-server counts
  * Harden app markdown URL handling
  * discover: allow Radeon 8060S iGPU
  * llm: detect llama-server load stalls from output
  * More harden app markdown URL handling
  * llama.cpp version update
  * launch: isolate Codex launch configuration
  * llama: add laguna (poolside) arch via a llama.cpp patch under llama/c…
  * docs: configure hermes desktop app
  * llm: ignore llama-server SSE ping comments
  * fix laguna patch build breakage
  * llama.cpp b9478 added a default 30s SSE ping that emits colon-only
    comment frames (":\n\n") while streamed requests are idle; Ollama
    treated non-data SSE lines as JSON, so skip SSE comments in completion
    and chat streams.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780660800">- update to 0.30.5:
  * Fix gemma4:12b floating point exception crash
  * llama.cpp version update
  * models: add support for gemma4-12b</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1780833600">- Update to 0.30.6:
  * Gemma 4 QAT weights: the Gemma 4 family is now optimized with
    Quantization-Aware Training (QAT) to dramatically reduce memory
    requirements and maximize on-device performance.
  * ollama launch omp now integrates with Oh My Pi, an AI coding
    agent with IDE integration
  * Disable WebUI:
    disable-llama.cpp-ui.patch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781438400">- Update to 0.30.8:
  * Fixed ollama launch selecting the wrong provider in some cases
  * Improved prompt caching by decoupling it from context shift
    for better KV cache reuse
  * More stable MLX inference with hardened linear and embedding layers
  * MLX runner now creates snapshots during prompt processing and
    speculative decoding for improved reliability
  * Improved recurrent model support with per-boundary states from
    the gated-delta kernels</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781697600">- Update to 0.30.9:
  * Support for Cohere2Moe architecture
  * Fixed LFM2 parser/render for cases where thinking was not emitted
  * Fixed issue where ollama launch claude and other coding agent or assistant use cases would only output one token
  * Ollama will now return an error if a single message is larger than the current context window
- switch to tar balls because we use git lfs now</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1781870400">- Update to 0.30.10:
  * Updated the underlying llama.cpp engine to build 9672</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1782561600">- Update to 0.30.11:
  * launch: add thinking capability detection to opencode
  * launch: auto-install Claude Code
  * launch: auto-install opencode when missing
  * discover: fix inverted iGPU/dGPU Vulkan classification on Windows hybrid graphics
  * mlxrunner: unify and tune speculative decoding
  * launch/codex: detect model drift when Codex App UI switches
  * llama: add sm_86 architecture to cuda_v13_windows preset
  * llm: size mmproj offload
  * docs: document max think level
  * llm: preserve generation headroom for shifted prompts
  * llama: default qwen2.5vl window attention metadata
  * llm: use host Vulkan loader on Windows
  * mlx: update and fix CUDA JIT packaging
  * llm: fix ollama ps double-counting mmap'd weights on partial offload
  * docs: redesign docs landing and integrations overview
  * server: align generate with native chat templates
  * jetson: add CC 87 for CUDA v13
  * llama.cpp version update
- Updated generated using pbuild-ai
  * Helper script provided in tool-scripts/ directory
    Use    pbuild-ai --allow-tool-scripts     to enable it.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1782993600">- Updated to version 0.31.1
  * Tightened Gemma 4 MoE model loading in the MLX engine
  * Updated the MLX engine to the latest version, including a new small-batch matmul kernel
  * Updated the underlying llama.cpp engine to build 9840
  * Improved Gemma 4 multi-token prediction (MTP) performance
  * new versions of llama.cpp and mlx</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783425600">- Define a build counter sync tag, to ensure all flavors have
  the same build number</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783425601">- Define a build counter sync tag, to ensure all flavors have
  the same build number</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1783598400">- Update to version 0.31.2
  * Enabled flash attention on older NVIDIA GPUs (compute capability 6.x)
  * iGPU can now offload vision models with padding to fit available memory
  * Fixed structured output for thinking models when thinking is disabled
  * Hardened GGUF model creation
  * ollama launch for Claude Code now disables telemetry by default
  * Fixed loading models on paths with non-UTF-8 characters
  * Updated the MLX and llama.cpp engines</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1784030400">- Update to version 0.32.0
  * New interactive agent experience: running ollama now launches
    an agent to help you code and delegate work
  * Renamed the Codex App integration to ChatGPT: use ollama launch
    chatgpt (and --restore to return to your usual ChatGPT profile)
  * Simplified integration selection: the ollama launch menu now
    only offers the most popular integrations (other integrations
    can be accessed through ollama launch
  * Warns before launching older agent models: CodeLlama, Qwen2.5(-coder),
    Llama 3.x, Mistral, StarCoder, and the base DeepSeek-R1 tags now
    prompt a deprecation warning before ollama launch continues</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1784289600">- Update to version 0.32.1
  * Improved Gemma 4 tool calling and multi-turn reasoning,
    including more reliable tool-response continuations
  * Fixed a recurrent MLX model cache leak that could increase
    memory use across requests, and improved cache snapshot performance
  * MLX text model loading now respects OLLAMA_LOAD_TIMEOUT
  * Agent web search and fetch now tell users to run ollama
    signin when authentication is required
  * The interactive agent now receives the current working directory
    for better project context
  * Fixed ollama launch so choosing Pick another model for a
    deprecated model passed with --model opens the model picker
  * Updated VS Code setup documentation for the official Ollama extension</changelog>
</package>
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  <version epoch="0" ver="3.6.0" rel="1.1"/>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1727697600">- Release v3.0.1
  * Modify add_column() to optionally accept a FeatureType as param by
  * Align filename prefix splitting with WebDataset library by
  * Support ndjson data files by
  * Support JSON lines with missing struct fields by
  * Support JSON lines with empty struct by
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  <version epoch="0" ver="0.34.0" rel="1.3"/>
  <changelog author="Oleksandr Ostrenko &lt;openbuildservice@ostrenko.dev&gt;" date="1754395200">- Initial package upload for version 0.34.0</changelog>
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  <version epoch="0" ver="0.4.5" rel="8.3"/>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720094400">- initial commit of python-safetensors</changelog>
  <changelog author="Yogalakshmi Arunachalam &lt;yarunachalam@suse.com&gt;" date="1724414400">- Fixed Summary description indentation and capitalization</changelog>
  <changelog author="Yogalakshmi Arunachalam &lt;yarunachalam@suse.com&gt;" date="1724414401">- switch to modern python on sle15</changelog>
  <changelog author="Steve Kowalik &lt;steven.kowalik@suse.com&gt;" date="1733486400">- Update to 0.4.5:
  * Use id rather than modelId
  * fix lifetimes, add tensors iterator
  * Fixing empty serialization (no tensor) with some metadata.
  * Upgrading Pyo3 0.22 (with gil-refs)
  * Improving the bf16 tests for PT+TF.
  * Add support for xla devices
  * Improving bf16 tests
  * Fixing zero-width tensor for in memory loading.
  * Respects torch.device(0) new behavior without breaking backward
    compatibility
  * Forcing empty list/empty tuple behavior.
- Run most of the testsuite.
- Switch to cargo-vendor to generate the vendor tarball.</changelog>
</package>
<package pkgid="5e1424e3890983a541f01566664e7276530dc6492280f13355ed6d606c47589023da2800089f7ad6e23c88a3c76f9c73a1c4352579ce6cb719081314552d2212" name="python-torch" arch="src">
  <version epoch="0" ver="2.7.1" rel="45.31"/>
  <changelog author="Tom&#xE1;&#x161; Chv&#xE1;tal &lt;tchvatal@suse.com&gt;" date="1587643200">- Do not pull in python2 only dependencies</changelog>
  <changelog author="Tom&#xE1;&#x161; Chv&#xE1;tal &lt;tchvatal@suse.com&gt;" date="1587643201">- Make sure to pull py2/py3 package from the devel pkg</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913600">- updated to bugfix release 1.4.1 and added _multibuild file so
  that cuda versions can be build on commandline</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913601">- updated to new stable release 1.5.1 which has following changes:
  This release includes several major new API additions and improvements. These
  include new APIs for autograd allowing for easy computation of hessians and
  jacobians, a significant update to the C++ frontend, ‘channels last’ memory
  format for more performant computer vision models, a stable release of the
  distributed RPC framework used for model parallel training, and a new API
  that allows for the creation of Custom C++ Classes that was inspired by
  PyBind. Additionally torch_xla 1.5 is now available and tested with the
  PyTorch 1.5 release providing a mature Cloud TPU experience.
  * see release.html for detailed information
- added patches:
  * fix-call-of-onnxInitGraph.patch for API mismatch in onnx
  * fix-mov-operand-for-gcc.patch for aarch64 operands
- removed sources:
  * cpuinfo-89fe1695edf9ee14c22f815f24bac45577a4f135.tar.gz
  * gloo-7c541247a6fa49e5938e304ab93b6da661823d0f.tar.gz
  * onnx-fea8568cac61a482ed208748fdc0e1a8e47f62f5.tar.gz
  * psimd-90a938f30ba414ada2f4b00674ee9631d7d85e19.tar.gz
  * pthreadpool-13da0b4c21d17f94150713366420baaf1b5a46f4.tar.gz
- added sources:
  * cpuinfo-0e6bde92b343c5fbcfe34ecd41abf9515d54b4a7.tar.gz
  * gloo-113bde13035594cafdca247be953610b53026553.tar.gz
  * onnx-9fdae4c68960a2d44cd1cc871c74a6a9d469fa1f.tar.gz
  * psimd-10b4ffc6ea9e2e11668f86969586f88bc82aaefa.tar.gz
  * pthreadpool-d465747660ecf9ebbaddf8c3db37e4a13d0c9103.tar.gz</changelog>
  <changelog author="Martin Li&#x161;ka &lt;mliska@suse.cz&gt;" date="1597924800">- Use memoryperjob constraint instead of %limit_build macro.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602072000">- Use GCC9 to build on aarch64 Tumbleweed to workaround SVE
  problem with GCC10 with sleef, see:
  https://github.com/pytorch/pytorch/issues/45971</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Matej Cepl &lt;mcepl@suse.com&gt;" date="1619179200">- Don't build python36-* package (missing pandas)</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update sleef to fix build on aarch64</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Add _service file to ease future update of deps</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955202">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955203">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="02b9dc4b550fe2cbcb46d2233fb6779317f5f575d36ce0766fd44baea10e99a36c363d38959d07e35ccb9a2b53e7ae2aeb5f4e9ab38e4c0f06f1df44edfc7442" name="python-torch-rocm-6-2" arch="src">
  <version epoch="0" ver="2.7.1" rel="45.8"/>
  <changelog author="Tom&#xE1;&#x161; Chv&#xE1;tal &lt;tchvatal@suse.com&gt;" date="1587643200">- Do not pull in python2 only dependencies</changelog>
  <changelog author="Tom&#xE1;&#x161; Chv&#xE1;tal &lt;tchvatal@suse.com&gt;" date="1587643201">- Make sure to pull py2/py3 package from the devel pkg</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913600">- updated to bugfix release 1.4.1 and added _multibuild file so
  that cuda versions can be build on commandline</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913601">- updated to new stable release 1.5.1 which has following changes:
  This release includes several major new API additions and improvements. These
  include new APIs for autograd allowing for easy computation of hessians and
  jacobians, a significant update to the C++ frontend, ‘channels last’ memory
  format for more performant computer vision models, a stable release of the
  distributed RPC framework used for model parallel training, and a new API
  that allows for the creation of Custom C++ Classes that was inspired by
  PyBind. Additionally torch_xla 1.5 is now available and tested with the
  PyTorch 1.5 release providing a mature Cloud TPU experience.
  * see release.html for detailed information
- added patches:
  * fix-call-of-onnxInitGraph.patch for API mismatch in onnx
  * fix-mov-operand-for-gcc.patch for aarch64 operands
- removed sources:
  * cpuinfo-89fe1695edf9ee14c22f815f24bac45577a4f135.tar.gz
  * gloo-7c541247a6fa49e5938e304ab93b6da661823d0f.tar.gz
  * onnx-fea8568cac61a482ed208748fdc0e1a8e47f62f5.tar.gz
  * psimd-90a938f30ba414ada2f4b00674ee9631d7d85e19.tar.gz
  * pthreadpool-13da0b4c21d17f94150713366420baaf1b5a46f4.tar.gz
- added sources:
  * cpuinfo-0e6bde92b343c5fbcfe34ecd41abf9515d54b4a7.tar.gz
  * gloo-113bde13035594cafdca247be953610b53026553.tar.gz
  * onnx-9fdae4c68960a2d44cd1cc871c74a6a9d469fa1f.tar.gz
  * psimd-10b4ffc6ea9e2e11668f86969586f88bc82aaefa.tar.gz
  * pthreadpool-d465747660ecf9ebbaddf8c3db37e4a13d0c9103.tar.gz</changelog>
  <changelog author="Martin Li&#x161;ka &lt;mliska@suse.cz&gt;" date="1597924800">- Use memoryperjob constraint instead of %limit_build macro.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602072000">- Use GCC9 to build on aarch64 Tumbleweed to workaround SVE
  problem with GCC10 with sleef, see:
  https://github.com/pytorch/pytorch/issues/45971</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Matej Cepl &lt;mcepl@suse.com&gt;" date="1619179200">- Don't build python36-* package (missing pandas)</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update sleef to fix build on aarch64</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Add _service file to ease future update of deps</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955202">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955203">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
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  <version epoch="0" ver="0.22.1" rel="3.5"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602244800">- Version 0.6.1</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Dominique Leuenberger &lt;dimstar@opensuse.org&gt;" date="1635940800">- Do not build for python 3.6: stack incomplete (nothing provides
  python36-torch-devel &gt;= 1.5.1).</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1721390400">- update to 0.18.1 with following highlights:
  * torchvision.transforms.v2 is now stable and support image classification,
    segmentation, detection, and video tasks.
  * support for the CutMix and MixUp augmentations
  * new Swin Transformer V2 architecture
  * a new Multi-weight support API for loading different weights to the
    existing model
  * Optical Flow support and RAFT model
  * RegNet and EfficientNet
- check the upstream changelog for more detailed descriptions</changelog>
</package>
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  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602244800">- Version 0.6.1</changelog>
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  <changelog author="Dominique Leuenberger &lt;dimstar@opensuse.org&gt;" date="1635940800">- Do not build for python 3.6: stack incomplete (nothing provides
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  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1721390400">- update to 0.18.1 with following highlights:
  * torchvision.transforms.v2 is now stable and support image classification,
    segmentation, detection, and video tasks.
  * support for the CutMix and MixUp augmentations
  * new Swin Transformer V2 architecture
  * a new Multi-weight support API for loading different weights to the
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  * Optical Flow support and RAFT model
  * RegNet and EfficientNet
- check the upstream changelog for more detailed descriptions</changelog>
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  * There was a hidden bug when loading models with local_files_only=True
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  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1727697600">- Release v3.0.1
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  * Align filename prefix splitting with WebDataset library by
  * Support ndjson data files by
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  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720094400">- initial commit of python-safetensors</changelog>
  <changelog author="Yogalakshmi Arunachalam &lt;yarunachalam@suse.com&gt;" date="1724414400">- Fixed Summary description indentation and capitalization</changelog>
  <changelog author="Yogalakshmi Arunachalam &lt;yarunachalam@suse.com&gt;" date="1724414401">- switch to modern python on sle15</changelog>
  <changelog author="Steve Kowalik &lt;steven.kowalik@suse.com&gt;" date="1733486400">- Update to 0.4.5:
  * Use id rather than modelId
  * fix lifetimes, add tensors iterator
  * Fixing empty serialization (no tensor) with some metadata.
  * Upgrading Pyo3 0.22 (with gil-refs)
  * Improving the bf16 tests for PT+TF.
  * Add support for xla devices
  * Improving bf16 tests
  * Fixing zero-width tensor for in memory loading.
  * Respects torch.device(0) new behavior without breaking backward
    compatibility
  * Forcing empty list/empty tuple behavior.
- Run most of the testsuite.
- Switch to cargo-vendor to generate the vendor tarball.</changelog>
</package>
<package pkgid="bb0af9788bfb7bead52c7b28f46c79ea06c44243fb5540823d0a869803fc063405b49198ecc94d208aaa2229d8d8d6b60727e678dcea7f7c5d9d8f2a3eb9aa81" name="python311-torch" arch="x86_64">
  <version epoch="0" ver="2.7.1" rel="45.31"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="05ebf81078b9eb8ba8abd368b6ea3533f1dcd4ae2a65a7da8349ab4690434c13216bb645a9ccd71cbe1d78ea92920b993f90171639c78cb53c6effa1e3c63ced" name="python311-torch-devel" arch="x86_64">
  <version epoch="0" ver="2.7.1" rel="45.31"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="dbd888d25da3d58fabc4c3d9887bb3133e62fdee14df4cc2e198ae33a32cb6b270b795eb5f9f40ffed530856473892d59643314a3e3b0450a32fd657d6cb50a9" name="python311-torch-rocm-6-2" arch="x86_64">
  <version epoch="0" ver="2.7.1" rel="45.8"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="c8929c6f1f2458b69322328c8cc48cb7b911f88d101410f2e9b8c8cc49af385b3a7e016a46bd1035c932975a3891ca92ad47ade7e2eff31b32bbd48bea9a2de0" name="python311-torch-rocm-6-2-devel" arch="x86_64">
  <version epoch="0" ver="2.7.1" rel="45.8"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="b5636e09f9b50ca9a54666b1dda7436dfeedcb019fd9b44d333912786ab54a75d79feaed625c3561ba9e0ca5b5a3616eae1f0cac47fe4059f5043d357a5477db" name="python311-torchvision-rocm-6-2" arch="x86_64">
  <version epoch="0" ver="0.22.1" rel="3.2"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602244800">- Version 0.6.1</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Dominique Leuenberger &lt;dimstar@opensuse.org&gt;" date="1635940800">- Do not build for python 3.6: stack incomplete (nothing provides
  python36-torch-devel &gt;= 1.5.1).</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1721390400">- update to 0.18.1 with following highlights:
  * torchvision.transforms.v2 is now stable and support image classification,
    segmentation, detection, and video tasks.
  * support for the CutMix and MixUp augmentations
  * new Swin Transformer V2 architecture
  * a new Multi-weight support API for loading different weights to the
    existing model
  * Optical Flow support and RAFT model
  * RegNet and EfficientNet
- check the upstream changelog for more detailed descriptions</changelog>
</package>
<package pkgid="966f1b5333c0e301de7f324c0a0bc6f1796e3da898d826b2f771c9f2e189ed2405813a7e685329f6426c9bf0b9b7fed9608fc894143e76b5f949e7cb1c9c7ec3" name="python311-transformers" arch="noarch">
  <version epoch="0" ver="4.57.3" rel="12.1"/>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724760000">- Enable sle15_python_module_pythons</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1725019200">- Update dependent package name 'huggingface_hub' to 'huggingface-hub'.</changelog>
  <changelog author="Yogalakshmi Arunachalam &lt;yarunachalam@suse.com&gt;" date="1726056000">- Updated to Version 4.44.2</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1728388800">- Add patch 0001-Remove-upper-bound-for-tokenizers.patch
  * Remove upper bound for tokenizers</changelog>
  <changelog author="Aline Werner &lt;aline.werner@suse.com&gt;" date="1753876800">- Update to version 4.51.1.
- Remove patch 0001-Remove-upper-bound-for-tokenizers.patch.</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1764763200">- Update to v4.57.3
  * There was a hidden bug when loading models with local_files_only=True
    and a typo related to the recent patch. The main fix is: b605555.</changelog>
</package>
<package pkgid="dc5a4bca76ba0a0bd4bb01b600986d91657664089bc3fa693da3465d3ffb9f9bd0391a9715d19382a79c603c1a9b00e22c2f52deb6e167fed7502faaca162312" name="python313-datasets" arch="noarch">
  <version epoch="0" ver="3.6.0" rel="1.1"/>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1727697600">- Release v3.0.1
  * Modify add_column() to optionally accept a FeatureType as param by
  * Align filename prefix splitting with WebDataset library by
  * Support ndjson data files by
  * Support JSON lines with missing struct fields by
  * Support JSON lines with empty struct by
  * fix increase_load_count by
  * fix docstring code example for distributed shuffle by
  * Support JSON lines with missing columns by
  * Add torchdata as a regular test dependency by</changelog>
</package>
<package pkgid="7e306e92a48134bef47b52dadabc55e75f8281897f3793a14fda612ac2229c8191c2d3be299910c45adf9e32204360b928038eeffe19288b2168b4c510dd2d0b" name="python313-diffusers" arch="noarch">
  <version epoch="0" ver="0.34.0" rel="1.3"/>
  <changelog author="Oleksandr Ostrenko &lt;openbuildservice@ostrenko.dev&gt;" date="1754395200">- Initial package upload for version 0.34.0</changelog>
</package>
<package pkgid="d56a5fb67d01fb054fc52c805fe37ff4ff3c093a3d3e259687bb7b135694b510b1cebae59b8aa05ce963290016cccba14499ae467f5b41fe89dee839887eda79" name="python313-safetensors" arch="x86_64">
  <version epoch="0" ver="0.4.5" rel="8.3"/>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720094400">- initial commit of python-safetensors</changelog>
  <changelog author="Yogalakshmi Arunachalam &lt;yarunachalam@suse.com&gt;" date="1724414400">- Fixed Summary description indentation and capitalization</changelog>
  <changelog author="Yogalakshmi Arunachalam &lt;yarunachalam@suse.com&gt;" date="1724414401">- switch to modern python on sle15</changelog>
  <changelog author="Steve Kowalik &lt;steven.kowalik@suse.com&gt;" date="1733486400">- Update to 0.4.5:
  * Use id rather than modelId
  * fix lifetimes, add tensors iterator
  * Fixing empty serialization (no tensor) with some metadata.
  * Upgrading Pyo3 0.22 (with gil-refs)
  * Improving the bf16 tests for PT+TF.
  * Add support for xla devices
  * Improving bf16 tests
  * Fixing zero-width tensor for in memory loading.
  * Respects torch.device(0) new behavior without breaking backward
    compatibility
  * Forcing empty list/empty tuple behavior.
- Run most of the testsuite.
- Switch to cargo-vendor to generate the vendor tarball.</changelog>
</package>
<package pkgid="3819308a14f8b3c610eb490172271bde0a75c36955fc9b6de3ab03cd4478567bd8a9e5090a906dd6ac4a72f895abc6f98746fe6e43ea065f85be753b90f2271c" name="python313-torch" arch="x86_64">
  <version epoch="0" ver="2.7.1" rel="45.31"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="970154dd3049ca4274dd741934e190dec5d8a00646f80cc55f7159652d49095b1831dfc142c3ff3d6612cfd81805ca066805e883dbdd3853a5a1c391eec6c5eb" name="python313-torch-devel" arch="x86_64">
  <version epoch="0" ver="2.7.1" rel="45.31"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="f01566f6d23eaad0de99224e1a5caffdffd455d6c8969ad1f079aa7d228104283feedd5ced431eac30723e94d170ffe1632913f8c3338c60552744bba12611ea" name="python313-torch-rocm-6-2" arch="x86_64">
  <version epoch="0" ver="2.7.1" rel="45.8"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="a1360a3e9845d6df8dbccb737d17fbe09628097050c60e5b0ed47dc192a3209c86c84c3f9d966db31ab71ea686fbcafe493e5762da7c44a3808862fe226b2deb" name="python313-torch-rocm-6-2-devel" arch="x86_64">
  <version epoch="0" ver="2.7.1" rel="45.8"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
</package>
<package pkgid="6d07c5df7578cf1b4898c5ec36574b0bbb00437e331538e0504d99f54c1d8f22166253cbaf1c015322511b8ed5452e72556158b6c4278d5332374b25910b56a5" name="python313-torchvision" arch="x86_64">
  <version epoch="0" ver="0.22.1" rel="3.5"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602244800">- Version 0.6.1</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Dominique Leuenberger &lt;dimstar@opensuse.org&gt;" date="1635940800">- Do not build for python 3.6: stack incomplete (nothing provides
  python36-torch-devel &gt;= 1.5.1).</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1721390400">- update to 0.18.1 with following highlights:
  * torchvision.transforms.v2 is now stable and support image classification,
    segmentation, detection, and video tasks.
  * support for the CutMix and MixUp augmentations
  * new Swin Transformer V2 architecture
  * a new Multi-weight support API for loading different weights to the
    existing model
  * Optical Flow support and RAFT model
  * RegNet and EfficientNet
- check the upstream changelog for more detailed descriptions</changelog>
</package>
<package pkgid="83ad9f12ef5cbddddc6479a5f764ca8aa193c6b4d622a48f7ca156c667a41d7ba7dfe9f83a9d1306604bc6af23aa93deb11ccdd320347722bf474afe719349df" name="python313-torchvision-rocm-6-2" arch="x86_64">
  <version epoch="0" ver="0.22.1" rel="3.2"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602244800">- Version 0.6.1</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Dominique Leuenberger &lt;dimstar@opensuse.org&gt;" date="1635940800">- Do not build for python 3.6: stack incomplete (nothing provides
  python36-torch-devel &gt;= 1.5.1).</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1721390400">- update to 0.18.1 with following highlights:
  * torchvision.transforms.v2 is now stable and support image classification,
    segmentation, detection, and video tasks.
  * support for the CutMix and MixUp augmentations
  * new Swin Transformer V2 architecture
  * a new Multi-weight support API for loading different weights to the
    existing model
  * Optical Flow support and RAFT model
  * RegNet and EfficientNet
- check the upstream changelog for more detailed descriptions</changelog>
</package>
<package pkgid="6eff44432d992ee61e7932f88007b414b4642cfa1a74f8e2b1cab8d4c8d23f12a9a3758172fe402f4992d3fbf55173c2cb3bb40c7e6c61189199e98c439511b4" name="python313-transformers" arch="noarch">
  <version epoch="0" ver="4.57.3" rel="12.1"/>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724760000">- Enable sle15_python_module_pythons</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1725019200">- Update dependent package name 'huggingface_hub' to 'huggingface-hub'.</changelog>
  <changelog author="Yogalakshmi Arunachalam &lt;yarunachalam@suse.com&gt;" date="1726056000">- Updated to Version 4.44.2</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1728388800">- Add patch 0001-Remove-upper-bound-for-tokenizers.patch
  * Remove upper bound for tokenizers</changelog>
  <changelog author="Aline Werner &lt;aline.werner@suse.com&gt;" date="1753876800">- Update to version 4.51.1.
- Remove patch 0001-Remove-upper-bound-for-tokenizers.patch.</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1764763200">- Update to v4.57.3
  * There was a hidden bug when loading models with local_files_only=True
    and a typo related to the recent patch. The main fix is: b605555.</changelog>
</package>
</otherdata>