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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

microsoft/benchpress-score-matrix · Datasets at Hugging Face

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

Daily Papers - Hugging Face

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

GitHub Actions · Hugging Face

https://huggingface.co/docs/hub/repositories-github-actions
The official huggingface/hub-sync action supports syncing Models, Datasets, and Spaces

Daily Papers - Hugging Face

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

Daily Papers - Hugging Face

https://huggingface.co/papers?q=role-play
Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team

Daily Papers - Hugging Face

https://huggingface.co/papers?q=persona
While Persona prompting (PP) is increasingly used as a way to steer model towards user-specific generation, its effect on model rationales remains underexplored

Trending Papers - Hugging Face

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