Data Science by ODS.ai 🦜

We followed on developing theme in Novemeber 2022. And it looks like we might have another attempt to renew our avatar, what do you think?

This is what we started with and results still look good for 2021. Back in a day we used neural networks for generation of the logo for the channel and it saved us quite some time on communication with designers.

🔥 Say Goodbye to LoRA, Hello to DoRA 🤩🤩 DoRA consistently outperforms LoRA with various tasks (LLM, LVLM, etc.) and backbones (LLaMA, LLaVA, etc.) [Paper] https://arxiv.org/abs/2402.09353 [Code] https://github.com/NVlabs/DoRA #Nvidia #icml #PEFT #lora #ML #ai @opendatascience

Discover, download, and run local LLMs LM Studio allows to run #LLM model of your choice locally Link: https://lmstudio.ai/

👑Llama 3 is here, with a brand new tokenizer! 🦙 Вышла Llama 3 Meta выпустила новую SOTA Llama 3 в двух версиях на 8B и 70B параметров. Длина контекста 8К, поддержка 30 языков. •HF: https://huggingface.co/spaces/ysharma/Chat_with_Meta_llama3_8b •Blog: https://ai.meta.com/blog/meta-llama-3/ Вы можете потестить 🦙 MetaLlama 3 70B и 🦙 Meta Llama 3 8B с помощью 🔥 бесплатного интерфейса: https://llama3.replicate.dev/ @ai_machinelearning_big_data

⚡️Map-relative Pose Regression🔥(#CVPR2024 highlight) For years absolute pose regression did not work. There was some success by massively synthesising scene-specific data. We train scene-agnostic APR and it works. Paper: https://arxiv.org/abs/2404.09884 Page: https://nianticlabs.github.io/marepo @opendatascience

🔥 ControlNet++: Improving Conditional Controls with Efficient Consistency Feedback Proposes an approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency proj: https://liming-ai.github.io/ControlNet_Plus_Plus/ abs: https://arxiv.org/abs/2404.07987 @opendatascience

🥔 YaART: Yet Another ART Rendering Technology 💚 This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences using Reinforcement Learning from Human Feedback (RLHF). 💜 During the development of YaART, Yandex especially focus on the choices of the model and training dataset sizes, the aspects that were not systematically investigated for text-to-image cascaded diffusion models before. 💖 In particular, researchers comprehensively analyze how these choices affect both the efficiency of the training process and the quality of the generated images, which are highly important in practice. ▪Paper page - https://ya.ru/ai/art/paper-yaart-v1 ▪Arxiv - https://arxiv.org/abs/2404.05666 ▪Habr - https://habr.com/ru/companies/yandex/articles/805745/ @opendatascience

⚡️ PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models Significantly improved finetuned perf by simply changing the initialization of LoRA's AB matrix from Gaussian/zero to principal components of W ▪Github: https://github.com/GraphPKU/PiSSA ▪Paper: https://arxiv.org/abs/2404.02948 @opendatascience

⚡️ Awesome CVPR 2024 Papers, Workshops, Challenges, and Tutorials! На конференцию 2024 года по компьютерному зрению и распознаванию образов (CVPR) поступило 11 532 статей, из которых только 2 719 были приняты, что составляет около 23,6% от общего числа. Ниже приведен список лучших докладов, гайдов, статей, семинаров и датасетов с CVPR 2024. ▪Github @ai_machinelearning_big_data