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build(deps): bump the pip group across 2 directories with 3 updates #77

Closed dependabot[bot] closed 5 months ago

dependabot[bot] commented 5 months ago

Bumps the pip group with 2 updates in the /Experiments/CV/ocr_with_bert directory: transformers and pydantic. Bumps the pip group with 1 update in the /MachineLearning/GMM/GMM_phones directory: scikit-learn.

Updates transformers from 4.39.3 to 4.40.1

Release notes

Sourced from transformers's releases.

v4.40.1: fix EosTokenCriteria for Llama3 on mps

Kudos to @​pcuenca for the prompt fix in:

  • Make EosTokenCriteria compatible with mps #30376

To support EosTokenCriteria on MPS while pytorch adds this functionality.

v4.40.0: Llama 3, Idefics 2, Recurrent Gemma, Jamba, DBRX, OLMo, Qwen2MoE, Grounding Dino

New model additions

Llama 3

Llama 3 is supported in this release through the Llama 2 architecture and some fixes in the tokenizers library.

Idefics2

The Idefics2 model was created by the Hugging Face M4 team and authored by Léo Tronchon, Hugo Laurencon, Victor Sanh. The accompanying blog post can be found here.

Idefics2 is an open multimodal model that accepts arbitrary sequences of image and text inputs and produces text outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. It improves upon IDEFICS-1, notably on document understanding, OCR, or visual reasoning. Idefics2 is lightweight (8 billion parameters) and treats images in their native aspect ratio and resolution, which allows for varying inference efficiency.

Recurrent Gemma

Recurrent Gemma architecture. Taken from the original paper.

The Recurrent Gemma model was proposed in RecurrentGemma: Moving Past Transformers for Efficient Open Language Models by the Griffin, RLHF and Gemma Teams of Google.

The abstract from the paper is the following:

We introduce RecurrentGemma, an open language model which uses Google’s novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide a pre-trained model with 2B non-embedding parameters, and an instruction tuned variant. Both models achieve comparable performance to Gemma-2B despite being trained on fewer tokens.

Jamba

Jamba is a pretrained, mixture-of-experts (MoE) generative text model, with 12B active parameters and an overall of 52B parameters across all experts. It supports a 256K context length, and can fit up to 140K tokens on a single 80GB GPU.

As depicted in the diagram below, Jamba’s architecture features a blocks-and-layers approach that allows Jamba to successfully integrate Transformer and Mamba architectures altogether. Each Jamba block contains either an attention or a Mamba layer, followed by a multi-layer perceptron (MLP), producing an overall ratio of one Transformer layer out of every eight total layers.

image

Jamba introduces the first HybridCache object that allows it to natively support assisted generation, contrastive search, speculative decoding, beam search and all of the awesome features from the generate API!

... (truncated)

Commits


Updates pydantic from 1.10.11 to 1.10.13

Release notes

Sourced from pydantic's releases.

V1.10.13 2023-09-27

What's Changed

Full Changelog: https://github.com/pydantic/pydantic/compare/v1.10.12...v1.10.13

V1.10.12

What's Changed

New Contributors

Full Changelog: https://github.com/pydantic/pydantic/compare/v1.10.11...v1.10.12

Changelog

Sourced from pydantic's changelog.

v1.10.13 (2023-09-27)

v1.10.12 (2023-07-24)

  • Fixes the maxlen property being dropped on deque validation. Happened only if the deque item has been typed. Changes the _validate_sequence_like func, #6581 by @​maciekglowka
Commits


Updates scikit-learn from 0.19.1 to 0.23.1

Release notes

Sourced from scikit-learn's releases.

scikit-learn 0.23.1

We're happy to announce the 0.23.1 release which fixes a few issues affecting many users, namely: K-Means should be faster for small sample sizes, and the representation of third-party estimators was fixed.

You can check this version out using:

    pip install -U scikit-learn

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-1 The conda-forge builds will be available shortly, which you can then install using:

    conda install -c conda-forge scikit-learn

scikit-learn 0.23.0

We're happy to announce the 0.23 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_23_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v0.23.html#version-0-23-0

This version supports Python versions 3.6 to 3.8.

Scikit-learn 0.22.2.post1

We're happy to announce the 0.22.2.post1 bugfix release.

The 0.22.2.post1 release includes a packaging fix for the source distribution but the content of the packages is otherwise identical to the content of the wheels with the 0.22.2 version (without the .post1 suffix).

Change log under https://scikit-learn.org/stable/whats_new/v0.22.html#changes-0-22-2.

This version supports Python versions 3.5 to 3.8.

Scikit-learn 0.22.1

We're happy to announce the 0.22.1 bugfix release. Change log under https://scikit-learn.org/stable/whats_new/v0.22.html#changes-0-22-1.

This version supports Python versions 3.5 to 3.8.

Scikit-learn 0.22.0

We're happy to announce the 0.22 release. You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_22_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v0.22.html#changes-0-22.

This version supports Python versions 3.5 to 3.8.

Scikit-learn 0.21.3

A bug fix and documentation release, fixing regressions and other issues released in version 0.21. See change log at https://scikit-learn.org/0.21/whats_new/v0.21.html

Scikit-learn 0.21.2

This version fixes a few bugs released in 0.21.1.

Scikit-learn version 0.21.1

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Commits


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