chore(deps): bump transformers from 4.18.0 to 4.30.0

[dependabot]

Bumps transformers from 4.18.0 to 4.30.0.

Release notes

Sourced from transformers's releases.

v4.30.0: 100k, Agents improvements, Safetensors core dependency, Swiftformer, Autoformer, MobileViTv2, timm-as-a-backbone

100k

Transformers has just reached 100k stars on GitHub, and to celebrate we wanted to highlight 100 projects in the vicinity of transformers and we have decided to create an awesome-transformers page to do just that.

We accept PRs to add projects to the list!

• Top 100 by @​LysandreJik in #22912
• Add LlamaIndex to awesome-transformers.md by @​ravi03071991 in #23484
• add cleanlab to awesome-transformers tools list by @​jwmueller in #23440

4-bit quantization and QLoRA

By leveraging the bitsandbytes library by @​TimDettmers, we add 4-bit support to transformers models!

• 4-bit QLoRA via bitsandbytes (4-bit base model + LoRA) by @​TimDettmers in #23479

Agents

The Agents framework has been improved and continues to be stabilized. Among bug fixes, here are the important new features that were added:

• Local agent capabilities, to load a generative model directly from transformers instead of relying on APIs.
• Prompts are now hosted on the Hub, which means that anyone can fork the prompts and update them with theirs, to let other community contributors re-use them
• We add an AzureOpenAiAgent class to support Azure OpenAI agents.

• Add local agent by @​sgugger in #23438
• Enable prompts on the Hub by @​sgugger in #23662
• Add AzureOpenAiAgent by @​sgugger in #24058

Safetensors

The safetensors library is a safe serialization framework for machine learning tensors. It has been audited and will become the default serialization framework for several organizations (Hugging Face, EleutherAI, Stability AI).

It has now become a core dependency of transformers.

• Making safetensors a core dependency. by @​Narsil in #23254

New models

Swiftformer

The SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called ‘SwiftFormer’ is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.

• Add swiftformer by @​shehanmunasinghe in #22686

Autoformer

This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.

• [Time-Series] Autoformer model by @​elisim in #21891

... (truncated)

Commits

• fe861e5 [GPT2] Add correct keys on _keys_to_ignore_on_load_unexpected on all chil...
• b3e27a8 Update the pin on Accelerate (#24110)
• 53e1f5c [Trainer] Correct behavior of _load_best_model for PEFT models (#24103)
• 17db177 reset accelerate env variables after each test (#24107)
• 905892f Release: v4.30.0
• c3572e6 Add AzureOpenAiAgent (#24058)
• 5eb3d3c Up pinned accelerate version (#24089)
• d1c039e fix accelerator prepare during eval only mode (#24014)
• 2c887cf Do not prepare lr scheduler as it as the right number of steps (#24088)
• 12298cb fix executable batch size issue (#24067)
• Additional commits viewable in compare view

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