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.
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.
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.
This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.
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Bumps transformers from 4.7.0 to 4.30.0.
Release notes
Sourced from transformers's releases.
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Commits
fe861e5[GPT2] Add correct keys on_keys_to_ignore_on_load_unexpectedon all chil...b3e27a8Update the pin on Accelerate (#24110)53e1f5c[Trainer] Correct behavior of_load_best_modelfor PEFT models (#24103)17db177reset accelerate env variables after each test (#24107)905892fRelease: v4.30.0c3572e6Add AzureOpenAiAgent (#24058)5eb3d3cUp pinned accelerate version (#24089)d1c039efix accelerator prepare during eval only mode (#24014)2c887cfDo not prepare lr scheduler as it as the right number of steps (#24088)12298cbfix executable batch size issue (#24067)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase.Dependabot commands and options
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