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https://arxiv.org/abs/2406.01954

We are excited to share our recent work on MLLM architecture design titled "Ovis: Structural Embedding Alignment for Multimodal Large Language Model".

Paper: https://arxiv.org/abs/2405.20797
Github: https://github.com/AIDC-AI/Ovis
Model: https://huggingface.co/AIDC-AI/Ovis-Clip-Llama3-8B
Data: https://huggingface.co/datasets/AIDC-AI/Ovis-dataset

@Yiwen-ntu for now we support only videos as paper covers in the Daily.

we are excited to share our work titled "Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models" : https://arxiv.org/abs/2406.12644

Depth Any Video with Scalable Synthetic Data

Depth Any Video introduces a scalable synthetic data pipeline, capturing 40,000 video clips from diverse games, and leverages powerful priors of generative video diffusion models to advance video depth estimation. By incorporating rotary position encoding, flow matching, and a mixed-duration training strategy, it robustly handles varying video lengths and frame rates. Additionally, a novel depth interpolation method enables high-resolution depth inference, achieving superior spatial accuracy and temporal consistency over previous models.

Arxiv link: https://arxiv.org/abs/2410.10815
Project page: https://depthanyvideo.github.io
Code: https://github.com/Nightmare-n/DepthAnyVideo
Huggingface gradio demo: https://huggingface.co/spaces/hhyangcs/depth-any-video

We are excited to share our recent proposed code completion benchmark "Codev-Bench: How Do LLMs Understand Develop-Centric Code Completion?".

📑 https://arxiv.org/abs/2410.01353
🚀 https://github.com/LingmaTongyi/Codev-Bench
🤗 https://huggingface.co/datasets/TongyiLingma/CodevBench

📑 https://arxiv.org/abs/2410.18666
🚀 https://github.com/shallowdream204/DreamClear
🤗 https://huggingface.co/shallowdream204/DreamClear

Hi AK and HF team,

Our paper https://arxiv.org/abs/2411.00785 titled "IGOR: Image-GOal Representations Are the Atomic Control Units for Foundation Models in Embodied AI" is just be made public today, although being onhold by arxiv for more than 7 days. However, the Daily paper submission website shows that it is more than 7 days old. We appreciate your help if you could help post the paper on the Daily Paper.

Hi AK and HF team,

I am happy to introduce our MicroAdam optimizer, a low-memory variant of Adam optimizer that has a memory footprint of 0.9d bytes, compared to 2d bytes of AdamW-8bits. We achieve this result by only storing 99% sparse gradients and reconstructing the optimizer states at each step, which is a fast operation due to our optimized implementation using CUDA kernels. MicroAdam was mainly developed for finetuning tasks in mind. Please check out our work:

(Paper) 📑: https://arxiv.org/pdf/2405.15593
(Code) 🚀: https://github.com/IST-DASLab/MicroAdam

Hi everyone,

I would like to introduce GridSearcher, a tool we have been developing in our DAS-Lab @ ISTA to speed up the hyper-parameter tuning process. Grid searcher is a pure python project designed to bypass the bash scripts to run grids of parameters for the ML projects. It provides a more flexible and user friendly way to manage and execute multiple programs in parallel. It is designed for systems where users have direct SSH access to machines and can run their python scripts right away.

Do you have access to your GPUs via SLURM? no problem, you can run srun --gres=gpu:8 --partition=gpu100 --time=10-00:00:00 --mem=1000G --cpus-per-task=200 --pty bash to request a bash session to your cluster to be able to use direct ssh access on the node, then use GridSearcher.

I am sure our project will help you save time, please check out our code on GitHub:

(Code) 🚀: https://github.com/IST-DASLab/GridSearcher/