Sourced from pytorch-lightning's releases.

Lightning v2.4

Lightning AI ⚡ is excited to announce the release of Lightning 2.4. This is mainly a compatibility upgrade for PyTorch 2.4 and Python 3.12, with a sprinkle of a few features and bug fixes.

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

Changes

PyTorch Lightning

• Made saving non-distributed checkpoints fully atomic (#20011)
• Added dump_stats flag to AdvancedProfiler (#19703)
• Added a flag verbose to the seed_everything() function (#20108)
• Added support for PyTorch 2.4 (#20010)
• Added support for Python 3.12 (20078)
• The TQDMProgressBar now provides an option to retain prior training epoch bars (#19578)
• Added the count of modules in train and eval mode to the printed ModelSummary table (#20159)

• Triggering KeyboardInterrupt (Ctrl+C) during .fit(), .evaluate(), .test() or .predict() now terminates all processes launched by the Trainer and exits the program (#19976)
• Changed the implementation of how seeds are chosen for dataloader workers when using seed_everything(..., workers=True) (#20055)
• NumPy is no longer a required dependency (#20090)

• Removed support for PyTorch 2.1 (#20009)
• Removed support for Python 3.8 (#20071)

• Avoid LightningCLI saving hyperparameters with class_path and init_args since this would be a breaking change (#20068)
• Fixed an issue that would cause too many printouts of the seed info when using seed_everything() (#20108)
• Fixed _LoggerConnector's _ResultMetric to move all registered keys to the device of the logged value if needed (#19814)
• Fixed _optimizer_to_device logic for special 'step' key in optimizer state causing performance regression (#20019)
• Fixed parameter counts in ModelSummary when model has distributed parameters (DTensor) (#20163)

Lightning Fabric

... (truncated)

• 2129fdf fix(ci): resolve input str -> num conversion (#20169)
• cf24a19 fix(docs): remove dead link from readme (#20170)
• a3e60ad ci/docs: disable optional cache pkg (#20168)
• 87ffd8c ci: fix cleaning caches (#20167)
• b3ee85d Prepare Lightning 2.4.0 release (#20154)
• 631911c Add special logic for 'step' in _optimizer_to_device (#20019)
• 345450b Fix parameter count in ModelSummary when parameters are DTensors (#20163)
• 3de60f4 docs: fix typo in linkcheck_ignore (#20164)
• e9d4ef8 Add diffusion example to README (#20161)
• d4de8e2 Count number of modules in train/eval mode in ModelSummary (#20159)
• Additional commits viewable in compare view

Sourced from transformers's releases.

Release v4.50.0

New Model Additions

Model-based releases

Starting with version v4.49.0, we have been doing model-based releases, additionally to our traditional, software-based monthly releases. These model-based releases provide a tag from which models may be installed.

Contrarily to our software-releases; these are not pushed to pypi and are kept on our GitHub. Each release has a tag attributed to it, such as:

• v4.49.0-Gemma-3
• v4.49.0-AyaVision

⚠️ As bugs are identified and fixed on each model, the release tags are updated so that installing from that tag always gives the best experience possible with that model.

Each new model release will always be based on the current state of the main branch at the time of its creation. This ensures that new models start with the latest features and fixes available.

For example, if two models—Gemma-3 and AyaVision—are released from main, and then a fix for gemma3 is merged, it will look something like this:

              o---- v4.49.0-Gemma-3 (includes AyaVision, plus main fixes)
            /                  \  
---o--o--o--o--o-- (fix for gemma3) --o--o--o main
       \          
        o---- v4.49.0-AyaVision

We strive to merge model specific fixes on their respective branches as fast as possible!

Gemma 3

Gemma 3 is heavily referenced in the following model-based release and we recommend reading these if you want all the information relative to that model.

The Gemma 3 model was proposed by Google. It is a vision-language model composed by a SigLIP vision encoder and a Gemma 2 language decoder linked by a multimodal linear projection.

It cuts an image into a fixed number of tokens same way as Siglip if the image does not exceed certain aspect ratio. For images that exceed the given aspect ratio, it crops the image into multiple smaller pacthes and concatenates them with the base image embedding.

One particularity is that the model uses bidirectional attention on all the image tokens. Also, the model interleaves sliding window local attention with full causal attention in the language backbone, where each sixth layer is a full causal attention layer.

• Gemma3 by @​RyanMullins in #36658

Shield Gemma2

ShieldGemma 2 is built on Gemma 3, is a 4 billion (4B) parameter model that checks the safety of both synthetic and natural images against key categories to help you build robust datasets and models. With this addition to the Gemma family of models, researchers and developers can now easily minimize the risk of harmful content in their models across key areas of harm as defined below:

• No Sexually Explicit content: The image shall not contain content that depicts explicit or graphic sexual acts (e.g., pornography, erotic nudity, depictions of rape or sexual assault).
• No Dangerous Content: The image shall not contain content that facilitates or encourages activities that could cause real-world harm (e.g., building firearms and explosive devices, promotion of terrorism, instructions for suicide).
• No Violence/Gore content: The image shall not contain content that depicts shocking, sensational, or gratuitous violence (e.g., excessive blood and gore, gratuitous violence against animals, extreme injury or moment of death).

We recommend using ShieldGemma 2 as an input filter to vision language models, or as an output filter of image generation systems. To train a robust image safety model, we curated training datasets of natural and synthetic images and instruction-tuned Gemma 3 to demonstrate strong performance.

... (truncated)

• 0b057e6 fix import issue
• 26fbd69 v 4.50.0
• 523f6e7 Fix: dtype cannot be str (#36262)
• 3f9ff19 Minor Gemma 3 fixes (#36884)
• f94b0c5 Use deformable_detr kernel from the Hub (#36853)
• 2638d54 Gemma 3 tests expect greedy decoding (#36882)
• b8aadc3 🔴 🔴 🔴 supersede paligemma forward to shift p...
• 6321876 add eustlb as an actor
• 94f4876 [generate] model defaults being inherited only happens for newer models (#36881)
• f19d018 Revert "Update deprecated Jax calls (#35919)" (#36880)
• Additional commits viewable in compare view

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