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Freshness: newest result fetched 24 d ago · median age of these results 24 d · 1 of 10 carry a fetch time
com/InternLM/InternLM"},"canonical_setting_json":{"kind":"string","value":"{\"higher_is_better\": true, \"metric_type\": \"raw\", \"multimodal_input\": false, \"notes\": \"Per InternLM3 GitHub README
from the top result below
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=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/teknium/OpenHermes-2.5
of Excess Demand":3,"Assumptions of Say’s Law":3,"What economics is all about":2,"Measures to Correct Excess and Deficient Demand":3,"Real Cost and Nominal Cost":4,"Distinction Between Micro and Macroeconomics":4,"Terms of Trade":5,"Precautions in the Estimation of Expenditure":4,"Difference between
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
Experimental results in terms of accuracy and subjective assessment demonstrate that RoleJudge outperforms various baseline models, validating the effectiveness of our multidimensional evaluation framework
https://huggingface.co/papers?q=persona
These 1 billion personas (~13% of the world's total population), acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, thereby facilitating the creation of diverse synthetic data at scale for various scenarios
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/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/docs/hub/repositories-github-actions
The official huggingface/hub-sync action supports syncing Models, Datasets, and Spaces
https://huggingface.co/docs/hub/storage-limits
co or when working locally, can be very annoying