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Published 20 hours ago • verified publisher icon mudler

chore(deps): bump transformers from 4.48.3 to 4.57.2 in /backend/python/coqui

Open dependabot[bot] opened this issue 1 week ago • 1 comments

Bumps transformers from 4.48.3 to 4.57.2.

Release notes

Sourced from transformers's releases.

Patch Release v4.57.2

This patch most notably fixes an issue on some Mistral tokenizers. It contains the following commits:

  • Add AutoTokenizer mapping for mistral3 and ministral (#42198)
  • Auto convert tekken.json (#42299)
  • fix tekken pattern matching (#42363)
  • Check model inputs - hidden states (#40994)
  • Remove invalid @staticmethod from module-level get_device_and_memory_breakdown (#41747)

Patch release v4.57.1

This patch most notably fixes an issue with an optional dependency (optax), which resulted in parsing errors with poetry. It contains the following fixes:

v4.57.0: Qwen3-Next, Vault Gemma, Qwen3 VL, LongCat Flash, Flex OLMO, LFM2 VL, BLT, Qwen3 OMNI MoE, Parakeet, EdgeTAM, OLMO3

New model additions

Qwen3 Next

The Qwen3-Next series represents the Qwen team's next-generation foundation models, optimized for extreme context length and large-scale parameter efficiency. The series introduces a suite of architectural innovations designed to maximize performance while minimizing computational cost:

  • Hybrid Attention: Replaces standard attention with the combination of Gated DeltaNet and Gated Attention, enabling efficient context modeling.
  • High-Sparsity MoE: Achieves an extreme low activation ratio as 1:50 in MoE layers — drastically reducing FLOPs per token while preserving model capacity.
  • Multi-Token Prediction(MTP): Boosts pretraining model performance, and accelerates inference.
  • Other Optimizations: Includes techniques such as zero-centered and weight-decayed layernorm, Gated Attention, and other stabilizing enhancements for robust training.

Built on this architecture, they trained and open-sourced Qwen3-Next-80B-A3B — 80B total parameters, only 3B active — achieving extreme sparsity and efficiency.

Despite its ultra-efficiency, it outperforms Qwen3-32B on downstream tasks — while requiring less than 1/10 of the training cost. Moreover, it delivers over 10x higher inference throughput than Qwen3-32B when handling contexts longer than 32K tokens.

For more details, please visit their blog Qwen3-Next (blog post).

Vault Gemma

VaultGemma is a text-only decoder model derived from Gemma 2, notably it drops the norms after the Attention and MLP blocks, and uses full attention for all layers instead of alternating between full attention and local sliding attention. VaultGemma is available as a pretrained model with 1B parameters that uses a 1024 token sequence length.

VaultGemma was trained from scratch with sequence-level differential privacy (DP). Its training data includes the same mixture as the Gemma 2 models, consisting of a number of documents of varying lengths. Additionally, it is trained using DP stochastic gradient descent (DP-SGD) and provides a (ε ≤ 2.0, δ ≤ 1.1e-10)-sequence-level DP guarantee, where a sequence consists of 1024 consecutive tokens extracted from heterogeneous data sources. Specifically, the privacy unit of the guarantee is for the sequences after sampling and packing of the mixture.

... (truncated)

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dependabot[bot] avatar Nov 24 '25 18:11 dependabot[bot]

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netlify[bot] avatar Nov 24 '25 18:11 netlify[bot]

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dependabot[bot] avatar Nov 25 '25 16:11 dependabot[bot]