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dame rajee

damerajee

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upvoted a paper 1 day ago

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

reacted to ginipick's post with 😎 4 days ago

Gini's AI Spaces: Everything You Need for Visual Content Creation! Hello! ✨ Let me introduce Gini’s 5 AI Spaces that effortlessly generate various styles of visual content. Each Space leverages Diffusers and Gradio, so you can create stunning images in just a few clicks! 1) Flowchart Features: Hand-drawn style flowcharts for workflows or business processes Use Cases: Software release pipelines, data pipelines, corporate workflows Benefits: Clear stage-by-stage structure, simple icon usage https://huggingface.co/spaces/ginigen/Flowchart 2) Infographic Features: Visually appealing infographics that communicate data or statistics Use Cases: Global energy charts, startup growth metrics, health tips and more Benefits: Eye-catching icons and layouts, perfect for storytelling at a glance https://huggingface.co/spaces/ginigen/Infographic 3) Mockup Features: Sketch-style wireframes or UX mockups for apps/websites Use Cases: Mobile login flows, dashboards, e-commerce site layouts Benefits: Rapid prototyping of early design ideas, perfect for storyboarding https://huggingface.co/spaces/ginigen/Mockup 4) Diagram Features: Educational diagrams (science, biology, geography, etc.) Use Cases: Water cycle, photosynthesis, chemical reactions, human anatomy Benefits: Vibrant, friendly illustrations, ideal for student-friendly materials https://huggingface.co/spaces/ginigen/Diagram 5) Design Features: Product/industrial design concepts (coffee machines, smartphones, etc.) Use Cases: Prototyping, concept car interiors, high-tech product sketches Benefits: From 3D render-like visuals to simple sketches, unleash your creativity! https://huggingface.co/spaces/ginigen/Design Click any link above and let AI spark your imagination. Enjoy a fun and productive creative process! 🚀✨

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On the 2nd of October a really cool paper was released called "Were RNNs all we need" https://arxiv.org/abs/2410.01201

This paper introduces the MinGRU model, a simplified version of the traditional Gated Recurrent Unit (GRU) designed to enhance efficiency by removing hidden state dependencies from its gates. This allows for parallel training, making it significantly faster than conventional GRUs. Additionally, MinGRU eliminates non-linear activations like tanh, streamlining computations.

So I read the paper and I tried training this model and it seems to be doing quite well , you could check out the pre-trained model on the huggingface spaces

- damerajee/mingru-stories

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