Sourced from transformers's releases.

Release v4.53.0

Gemma3n

Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for pre-trained and instruction-tuned variants. These models were trained with data in over 140 spoken languages.

Gemma 3n models use selective parameter activation technology to reduce resource requirements. This technique allows the models to operate at an effective size of 2B and 4B parameters, which is lower than the total number of parameters they contain. For more information on Gemma 3n's efficient parameter management technology, see the Gemma 3n page.

from transformers import pipeline
import torch
pipe = pipeline(
"image-text-to-text",
torch_dtype=torch.bfloat16,
model="google/gemma-3n-e4b",
device="cuda",
)
output = pipe(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
text="<image_soft_token> in this image, there is"
)
print(output)

Dia

Dia is an opensource text-to-speech (TTS) model (1.6B parameters) developed by Nari Labs. It can generate highly realistic dialogue from transcript including nonverbal communications such as laughter and coughing. Furthermore, emotion and tone control is also possible via audio conditioning (voice cloning).

Model Architecture: Dia is an encoder-decoder transformer based on the original transformer architecture. However, some more modern features such as rotational positional embeddings (RoPE) are also included. For its text portion (encoder), a byte tokenizer is utilized while for the audio portion (decoder), a pretrained codec model DAC is used - DAC encodes speech into discrete codebook tokens and decodes them back into audio.

• Add Dia model by @​buttercrab in #38405

Kyutai Speech-to-Text

Kyutai STT is a speech-to-text model architecture based on the Mimi codec, which encodes audio into discrete tokens in a streaming fashion, and a Moshi-like autoregressive decoder. Kyutai’s lab has released two model checkpoints:

• kyutai/stt-1b-en_fr: a 1B-parameter model capable of transcribing both English and French

... (truncated)

• 67ddc82 Release: v4.53.0
• 0a8081b [Modeling] Fix encoder CPU offloading for whisper (#38994)
• c63cfd6 Gemma 3n (#39059)
• 3e5cc12 [tests] remove tests from libraries with deprecated support (flax, tensorflow...
• cfff7ca [Whisper] Pipeline: handle long form generation (#35750)
• 02ecdcf add _keep_in_fp32_modules_strict (#39058)
• d973e62 fix condition where torch_dtype auto collides with model_kwargs. (#39054)
• 44b2316 [qwen2-vl] fix vision attention scaling (#39043)
• ae15715 polishing docs: error fixes for clarity (#39042)
• 3abeaba Create test for #38916 (custom generate from local dir with imports) (#39015)
• Additional commits viewable in compare view

Sourced from torch's releases.

PyTorch 2.8.0 Release Notes

• Highlights
• Backwards Incompatible Changes
• Deprecations
• New Features
• Improvements
• Bug fixes
• Performance
• Documentation
• Developers

Highlights

... (truncated)

• ba56102 Cherrypick: Add the RunLLM widget to the website (#159592)
• c525a02 [dynamo, docs] cherry pick torch.compile programming model docs into 2.8 (#15...
• a1cb3cc [Release Only] Remove nvshmem from list of preload libraries (#158925)
• c76b235 Move out super large one off foreach_copy test (#158880)
• 20a0e22 Revert "[Dynamo] Allow inlining into AO quantization modules (#152934)" (#158...
• 9167ac8 [MPS] Switch Cholesky decomp to column wise (#158237)
• 5534685 [MPS] Reimplement tri[ul] as Metal shaders (#158867)
• d19e08d Cherry pick PR 158746 (#158801)
• a6c044a [cherry-pick] Unify torch.tensor and torch.ops.aten.scalar_tensor behavior (#...
• 620ebd0 [Dynamo] Use proper sources for constructing dataclass defaults (#158689)
• Additional commits viewable in compare view

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
• @dependabot ignore <dependency name> major version will close this group update PR and stop Dependabot creating any more for the specific dependency's major version (unless you unignore this specific dependency's major version or upgrade to it yourself)
• @dependabot ignore <dependency name> minor version will close this group update PR and stop Dependabot creating any more for the specific dependency's minor version (unless you unignore this specific dependency's minor version or upgrade to it yourself)
• @dependabot ignore <dependency name> will close this group update PR and stop Dependabot creating any more for the specific dependency (unless you unignore this specific dependency or upgrade to it yourself)
• @dependabot unignore <dependency name> will remove all of the ignore conditions of the specified dependency
• @dependabot unignore <dependency name> <ignore condition> will remove the ignore condition of the specified dependency and ignore conditions
  You can disable automated security fix PRs for this repo from the Security Alerts page.