[Dependabot] Update(deps): Bump transformers from 4.55.2 to 4.57.3

[dependabot]

Bumps transformers from 4.55.2 to 4.57.3.

Release notes

Sourced from transformers's releases.

Patch release 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: huggingface/transformers@b605555.

We are really sorry that this slipped through, our CIs just did not catch it.

As it affects a lot of users we are gonna yank the previous release

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:

• fix optax dep issue
• remove offload_state_dict from kwargs
• Fix bnb fsdp loading for pre-quantized checkpoint (#41415)
• Fix tests fsdp (#41422)
• Fix trainer for py3.9 (#41359)

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).

• Adding Support for Qwen3-Next by @​bozheng-hit in #40771

... (truncated)

Commits

• 47b0e47 4.57.3
• d3ee5e8 [Mistral Tokenizers] Fix tokenizer detection (#42389)
• 2915fb3 Release v4.57.2
• 2a59904 fix tekken pattern matching (#42363)
• 7e66db7 Auto convert tekken.json (#42299)
• 311807f Remove invalid @staticmethod from module-level get_device_and_memory_breakd...
• 804038f Add AutoTokenizer mapping for mistral3 and ministral (#42198)
• ede92a8 Check model inputs - hidden states (#40994)
• 8cb5963 Release: v4.57.1
• c6ae19e Fix trainer for py3.9 (#41359)
• Additional commits viewable in compare view

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[meta-codesync]

@huydhn has imported this pull request. If you are a Meta employee, you can view this in D88104417.

[dependabot]

OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version. You can also ignore all major, minor, or patch releases for a dependency by adding an ignore condition with the desired update_types to your config file.

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[meta-codesync]

@huydhn merged this pull request in 49758ea.