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microsoft / Moonlit

This is a collection of our research on efficient AI, covering hardware-aware NAS and model compression.
MIT License
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Bump transformers from 4.25.1 to 4.30.0 in /Compresso/evaluation/lm-evaluation-harness #25

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps transformers from 4.25.1 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!

4-bit quantization and QLoRA

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

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.

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.

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.

Autoformer

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

... (truncated)

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dependabot[bot] commented 1 year ago

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.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.

JiahangXu commented 1 year ago

@dependabot ignore this minor version

dependabot[bot] commented 1 year ago

OK, I won't notify you about version 4.30.x again, unless you re-open this PR.