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com/posts');\n\n // If the request is not successful, throw an error\n if
from the top result below
https://huggingface.co/datasets/teknium/OpenHermes-2.5
6,"Supply Curve":2,"Average Revenue":3,"Theory of Comparative Advantage–David Ricardo":3,"Circular Flow of Income":4,"Returns to Scale":3,"Demand for Foreign Exchange":4,"Supply of Money":5,"Basic Balance":4,"Difficulties of the Product Method":2,"Average Variable Cost":5,"Barter System":3,"Spot and Forward
https://huggingface.co/papers?q=reflective%20essays
com/rosewang2008/sight","githubRepoAddedBy":"auto","ai_summary":"A dataset and automated methods using large language models classify online student feedback efficiently, enhancing teacher improvement from lectures
https://huggingface.co/datasets/microsoft/benchpress-score-matrix
com/eval-sys/mcpmark"},"canonical_setting_json":{"kind":"string","value":"{\"higher_is_better\": true, \"judge\": \"programmatic verification scripts\", \"metric_type\": \"pct\", \"multimodal_input\": false, \"notes\": \"Official sources are arXiv:2509
https://huggingface.co/papers?q=companion
com/YutingHe-list/GEMINI","githubRepoAddedBy":"auto","ai_summary":"GEMINI improves dense contrastive representation learning for medical images by incorporating deformable homeomorphism learning and geometric semantic similarity to enhance correspondence discovery, performance, and learning efficiency
https://huggingface.co/papers?q=role-play
Extensive experiments show that EBM-RL substantially outperforms text-only role-playing baselines and larger-scale vision-language models on our immersive role-playing benchmark, improving both visual-atmosphere consistency and character authenticity
https://huggingface.co/papers?q=persona
To fully exploit this methodology at scale, we introduce Persona Hub -- a collection of 1 billion diverse personas automatically curated from web data
https://huggingface.co/papers/trending
8T scale, Kimi K3 is supported by infrastructure advances in multiple areas: algorithm-system co-design for KDA, perfectly balanced expert-parallel training with efficient memory management, million-token agentic RL with persistent rollout and sandbox states, and deployment innovations
https://huggingface.co/koboldcpp
com/LostRuins/koboldcpp/releases/latest\" rel=\"nofollow\">KoboldCpp Github releases</a>\nLinux, Windows and MacOS are supported - pick the correct binary for your OS
https://huggingface.co/docs/hub/storage-limits
We optimize our infrastructure continuously to scale our storage for the coming years of growth in AI and Machine learning
https://huggingface.co/docs/hub/repositories-github-actions
The official huggingface/hub-sync action supports syncing Models, Datasets, and Spaces