search

YeonwooSung / ai_book

AI book for everyone
23 stars 4 forks source link

build(deps): bump the pip group across 3 directories with 3 updates #87

Closed dependabot[bot] closed 3 days ago

dependabot[bot] commented 4 days ago

Bumps the pip group with 3 updates in the /Experiments/CV/ocr_with_bert directory: transformers, certifi and pydantic. Bumps the pip group with 1 update in the /LLMs/RAG/llama-index-milvus-example directory: certifi. Bumps the pip group with 1 update in the /LLMs/training/llama_peft_trl directory: certifi.

Updates transformers from 4.41.2 to 4.42.3

Release notes

Sourced from transformers's releases.

Patch release v4.42.3

Make sure we have attention softcapping for "eager" GEMMA2 model

After experimenting, we noticed that for the 27b model mostly, softcapping is a must. So adding it back (it should have been there, but an error on my side made it disappear) sorry all! 😭

  • Gemma capping is a must for big models (#31698)

Patch release v4.42.2

Patch release

Thanks to our 2 contributors for their prompt fixing mostly applies for training and FA2!

v4.42.1: Patch release

Patch release for commit:

  • [HybridCache] Fix get_seq_length method (#31661)

v4.42.0: Gemma 2, RTDETR, InstructBLIP, LLAVa Next, New Model Adder

New model additions

Gemma-2

The Gemma2 model was proposed in Gemma2: Open Models Based on Gemini Technology and Research by Gemma2 Team, Google. Gemma2 models are trained on 6T tokens, and released with 2 versions, 2b and 7b.

The abstract from the paper is the following:

This work introduces Gemma2, a new family of open language models demonstrating strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma2 outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of our model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations

image

RTDETR

The RT-DETR model was proposed in DETRs Beat YOLOs on Real-time Object Detection by Wenyu Lv, Yian Zhao, Shangliang Xu, Jinman Wei, Guanzhong Wang, Cheng Cui, Yuning Du, Qingqing Dang, Yi Liu.

RT-DETR is an object detection model that stands for “Real-Time DEtection Transformer.” This model is designed to perform object detection tasks with a focus on achieving real-time performance while maintaining high accuracy. Leveraging the transformer architecture, which has gained significant popularity in various fields of deep learning, RT-DETR processes images to identify and locate multiple objects within them.

image

InstructBlip

The InstructBLIP model was proposed in InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. InstructBLIP leverages the BLIP-2 architecture for visual instruction tuning.

... (truncated)

Commits


Updates certifi from 2023.7.22 to 2024.7.4

Commits


Updates pydantic from 1.10.16 to 1.10.17

Release notes

Sourced from pydantic's releases.

v1.10.17 (2024-06-20)

What's Changed

Full Changelog: https://github.com/pydantic/pydantic/compare/v1.10.16...v1.10.17

Changelog

Sourced from pydantic's changelog.

v1.10.17 (2024-06-20)

Commits


Updates certifi from 2023.7.22 to 2024.7.4

Commits


Updates certifi from 2023.7.22 to 2024.7.4

Commits


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 ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore major version` will close this group update PR and stop Dependabot creating any more for the specific dependency's major version (unless you unignore this specific dependency's major version or upgrade to it yourself) - `@dependabot ignore minor version` will close this group update PR and stop Dependabot creating any more for the specific dependency's minor version (unless you unignore this specific dependency's minor version or upgrade to it yourself) - `@dependabot ignore ` will close this group update PR and stop Dependabot creating any more for the specific dependency (unless you unignore this specific dependency or upgrade to it yourself) - `@dependabot unignore ` will remove all of the ignore conditions of the specified dependency - `@dependabot unignore ` will remove the ignore condition of the specified dependency and ignore conditions You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/YeonwooSung/ai_book/network/alerts).