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Freshness: newest result fetched 25 d ago · median age of these results 25 d · 1 of 10 carry a fetch time
We will release our code on a "},{"type":"highlight","text":"companion"},{"type":"text","text":" link:\nhttps://github
from the top result below
https://huggingface.co/papers?q=companion
png","numComments":0,"upvoted":false,"isAuthorParticipating":false,"highlightedTitle":[{"type":"text","text":"The Binary Fraction of Red Supergiants in the Magellanic Clouds"}],"highlightedSummary":[{"type":"text","text":"Red supergiants (RSGs), as the descendants of OB-type stars and the\nprogenitors of supernovae
https://huggingface.co/datasets/microsoft/benchpress-score-matrix
00933 defines the full MCP-Atlas benchmark as 1,000 tasks over 36 real MCP servers and 220 tools; the ScaleAI/MCP-Atlas HF card and scaleapi/mcp-atlas GitHub repo expose the public release as 500 evaluation prompts/tasks, matching this MCPAtlas Public benchmark row
https://huggingface.co/datasets/teknium/OpenHermes-2.5
,"Taco Seasoning Recipe":1,"Invite collaborator privately.":1,"提高自律能力":1,"Stinky Doge Coin Rap":1,"エクセル：文字列の結合 (Excel: Concatenating Strings)":1}}}},{"name":"topic","align":"right","type":"null","statistics
https://huggingface.co/papers?q=reflective%20essays
","upvotes":3,"discussionId":"6679affcf926ce3cf48fd0d1","githubRepo":"https://github
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/papers?q=role-play
To optimize this See-Think-Speak process, EBM-RL integrates complementary rewards for scene--text alignment, perceptual--cognitive utility, answer faithfulness, and format consistency
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
Trained from scratch on approximately 560M unlabeled images and 100M unlabeled video frames, Mage-ViT matches or outperforms flagship encoders trained on billions of image-text pairs
https://huggingface.co/docs/hub/repositories-github-actions
For keyless publishing — no HF_TOKEN secret to store or rotate — see Trusted Publishers, which exchanges GitHub Actions’ built-in OIDC token for a short-lived, repo-scoped Hub token at the start of each run
https://huggingface.co/docs/hub/storage-limits
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