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microsoft / mttl

Building modular LMs with parameter-efficient fine-tuning.
MIT License
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Bump transformers from 4.19.2 to 4.29.1 #31

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps transformers from 4.19.2 to 4.29.1.

Release notes

Sourced from transformers's releases.

V4.29.1: Patch release

Reverts a regression in the FSDP integration. Add pip install transformers["agent"] to have all dependencies agents rely on. Fixes the documentation about agents.

v4.29.0: Transformers Agents, SAM, RWKV, FocalNet, OpenLLaMa

Transformers Agents

Transformers Agent is a new API that lets you use the library and Diffusers by prompting an agent (which is a large language model) in natural language. That agent will then output code using a set of predefined tools, leveraging the appropriate (and state-of-the-art) models for the task the user wants to perform. It is fully multimodal and extensible by the community. Learn more in the docs

SAM

SAM (Segment Anything Model) was proposed in Segment Anything by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.

The model can be used to predict segmentation masks of any object of interest given an input image.

RWKV

RWKV suggests a tweak in the traditional Transformer attention to make it linear. This way, the model can be used as recurrent network: passing inputs for timestamp 0 and timestamp 1 together is the same as passing inputs at timestamp 0, then inputs at timestamp 1 along with the state of timestamp 0 (see example below).

This can be more efficient than a regular Transformer and can deal with sentence of any length (even if the model uses a fixed context length for training).

FocalNet

The FocalNet model was proposed in Focal Modulation Networks by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao. FocalNets completely replace self-attention (used in models like ViT and Swin) by a focal modulation mechanism for modeling token interactions in vision. The authors claim that FocalNets outperform self-attention based models with similar computational costs on the tasks of image classification, object detection, and segmentation.

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dependabot[bot] commented 1 year ago

Superseded by #32.