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xlangai /ubuntu_osworld_file_cache OSWorld File Cache This repository serves as a file cache for the OSWorld project, providing reliable and fast access to evaluation files that were previously hosted on Google Drive. Overview OSWorld is a scalable, real computer environment for multimodal agents, supporting task setup, execution-based evaluation, and interactive learning across various operating systems and applications. This cache repository ensures that all evaluation files are consistently… See the full description on the dataset page: https://huggingface.co/datasets/xlangai/ubuntu_osworld_file_cache.61 likes1m downloads2mo agoHugging Facearcinstitute /State-Parse-FilteredThe single cell RNA-seq dataset with human PBMC samples was sourced from Parse Biosciences [1]. [1] Performance of Evercode™ WT v3 in Human Immune Cells (PBMCs), https://www.parsebiosciences.com/datasets/performance-of-evercode-wt-v3-in-human-immune-cells-pbmcs/; Parse Biosciences, Seattle, USA; accessed 05/27/2025. Certain uses of this data may require a license from Parse Biosciences, Inc. textn<1K0 likes65k downloads4mo agoHugging FaceTIGER-Lab /OmniEdit-Filtered-1.2M OmniEdit In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality. 📃Paper | 🌐Website |… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M.image1M<n<10M132 likes62k downloads2y agoHugging Facehf-internal-testing /dataset_with_data_filestextn<1K0 likes51k downloads2y agoHugging Facebigcode /self-oss-instruct-sc2-exec-filter-50kFinal self-alignment training dataset for StarCoder2-Instruct. seed: Contains the seed Python function concepts: Contains the concepts generated from the seed instruction: Contains the instruction generated from the concepts response: Contains the execution-validated response to the instruction This dataset utilizes seed Python functions derived from the MultiPL-T pipeline. text10K<n<100K108 likes39k downloads2y agoHugging Facemuybuenacuentajaja2 /files0 likes36k downloads29d agoHugging Face

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