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 (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 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).
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.
The Open-Llama model was proposed in Open-Llama project by community developer s-JoL.
The model is mainly based on LLaMA with some modifications, incorporating memory-efficient attention from Xformers, stable embedding from Bloom, and shared input-output embedding from PLAM. And the model is pre-trained on both Chinese and English, which gives it better performance on Chinese language tasks.
Assisted generation is a new technique that lets you speed up generation with large language models by using a smaller model as assistant. The assistant model will be the ones doing multiple forward pass while the LLM will merely validate the tokens proposed by the assistant. This can lead to speed-ups up to 10x!
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Commits
15f260a Render custom tool docs a bit better (#23269)
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Updates the requirements on transformers to permit the latest version.
Release notes
Sourced from transformers's releases.
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Commits
15f260a
Render custom tool docs a bit better (#23269)60fc8f8
[all-test]bcf9100
Fix new line bug in chat mode for agents (#23267)9dd6209
Refine documentation for Tools (#23266)9d5b0e5
Update Image segmentation description (#23261)7d415ba
Metadata update (#23259)bb57271
Improve Docs of Custom Tools and Agents (#23255)2a2be57
Test composition (#23214)d5e1c98
Fixfrom_config
(#23246)fee5b5e
Revert "[Doctests] Refactor doctests + add CI" (#23245)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
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