Sourced fromtransformers'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 oftransformers and we have decided to create anawesome-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 thebitsandbytes library by@​TimDettmers, we add 4-bit support totransformers 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 fromtransformers 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 anAzureOpenAiAgent 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

Thesafetensors 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 oftransformers.

• Makingsafetensors 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)

• 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 incompare view

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