(math/p5-PDL) Updated 2.024 to 2.100
2.100 2025-03-27
- fix Math function prototypes for GCC 15 (#528) - thanks @jplesnik for report
- Basic::rvals to allow Centre as ndarray (#532) - thanks @wlmb
- now an error to qsort{,vec} complex data
- {and,or}over output type now same as input type (#533)
- add Ufunc::{b,}xorover (#533) - thanks @jo-37
- fix Math::polyroots crashing on [2 1 0 1] (#536) - thanks @pipatron for report
- fix Slices::indexND with an empty used as an lvalue (#535) - thanks @yelnats for report
- many fixes and improvements to the auto-generated POD docs (#534) - thanks @jo-37
2.099 2025-01-23
- fix test that assumed acosh(0)->byte, i.e. nan()->byte, was always 0 (#514) - thanks @eserte for report
- separate PDL::Type POD documentation
- partly restoring pre-2.096 xform type-selection: if xform given no typed outputs, and non-available (greater than last-given type) typed inputs, use last-given (#511, https://github.com/moocow-the-bovine/PDL-CCS/issues/18)
- fix Math::polyroots with native-complex input and supplied null output
- add Pars type-spec "!real" which makes it an error to supply real values (#511)
- now an error to call Ops::carg on a real value, or Ops::czip on complex inputs (#511)
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py-tokenizers: Import py-tokenizers-0.22.1 as wip/py-tokenizers
Provides an implementation of today's most used tokenizers, with a
focus on performance and versatility.
Bindings over the Rust implementation. If you are interested in the
High-level design, you can go check it there.
Main features:
* Train new vocabularies and tokenize using 4 pre-made tokenizers
(Bert WordPiece and the 3 most common BPE versions).
* Extremely fast (both training and tokenization), thanks to the Rust
implementation. Takes less than 20 seconds to tokenize a GB of text
on a server's CPU.
* Easy to use, but also extremely versatile.
* Designed for research and production.
* Normalization comes with alignments tracking. It's always possible
to get the part of the original sentence that corresponds to a given
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py-transformers: Import py-transformers-4.57.1 as wip/py-transformers
Transformers acts as the model-definition framework for
state-of-the-art machine learning with text, computer vision, audio,
video, and multimodal models, for both inference and training.
It centralizes the model definition so that this definition is agreed
upon across the ecosystem. transformers is the pivot across
frameworks: if a model definition is supported, it will be compatible
with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed,
FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI,
...), and adjacent modeling libraries (llama.cpp, mlx, ...) which
leverage the model definition from transformers.
We pledge to help support new state-of-the-art models and democratize
their usage by having their model definition be simple, customizable,
and efficient.
There are over 1M+ Transformers model checkpoints on the Hugging Face
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py-safetensors: Import py-safetensors-0.7.0 as wip/py-safetensors
New simple format for storing tensors safely (as opposed to pickle)
and that is still fast (zero-copy).
auto-admin: Update to 0.8.3.32
auto-select-sound-device: Display choices and prompt for default device
auto-sound-driver-setup: Call auto-select-sound-device
Import swiftshader
SwiftShader is a high-performance CPU-based implementation
of the Vulkan graphics API. Its goal is to provide hardware
independence for advanced 3D graphics.
bind920: update to BIND version 9.20.16.
Pkgsrc changes:
* Remove patch for problem now solved upstream.
* Version bump, checksums.
Upstream changes:
BIND 9.20.16
------------
Feature Changes
~~~~~~~~~~~~~~~
- Fix assertion failure from arc4random_uniform with invalid limit.
``1040282de7e``
When the arc4random_uniform() is called on NetBSD with upper_bound
that makes no sense statistically (0 or 1), the call crashes the
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nheko: add Nheko version 0.12.1
Nheko is a lightweight, Qt-based Matrix client that provides a clean, fast, and user-friendly interface for secure messaging.
Update Spotifyd to 0.4.2
From [the release notes][1]:
> This release fixes the recent (and not so recent) issues with
> playback, you should be able to enjoy your music again! 🎉
>
> Fixed
>
> fix multiple playback issues introduced by Spotify changes (#1362,
> #1374)
> fix crash on user switch (#1361)
>
> Added
>
> add suggestions for alsa mixer / control device names
> improve distribution support for Debian and Fedora (#1364, #1372)
[1]: https://github.com/Spotifyd/spotifyd/releases/tag/v0.4.2
