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com/nianqi-li/historical-analogy-of-llms","githubRepoAddedBy":"auto","ai_summary":"LLMs can acquire historical analogies through retrieval and generation methods, with performance enhanced by a self-reflection method to reduce hallucinations and stereotypes
from the top result below
https://huggingface.co/papers?q=reflective%20essays
","upvotes":3,"discussionId":"6679affcf926ce3cf48fd0d1","githubRepo":"https://github
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/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/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/docs/hub/storage-limits
Storage limits and policies apply to all types of repositories (models, datasets, buckets, …) on the Hub
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/microsoft
8/site-packages/transformers/processing_utils
https://huggingface.co/microsoft/models
8/site-packages/transformers/processing_utils
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/larryvrh/MiniMax-H3-Turbo-Lora
It still needs a ComfyUI checkout for the H3 model / VAE / text-encoder definitions:
# ComfyUI (pinned to the commit these weights were validated against)
git clone https://github
https://huggingface.co/docs/hub/repositories-pull-requests-discussions
At a high level, the aim is to build a simpler version of other git hosts’ (like GitHub’s) PRs and Issues:
no forks are involved: contributors push to a special ref branch directly on the source repo
https://huggingface.co/docs/hub/datasets-adding
A public dataset is visible to anyone, whereas a private dataset can only be viewed by you or members of your organization
https://huggingface.co/docs/hub/agents-local
Connect Your Agent
Pick one of the agents below and follow the setup instructions
https://huggingface.co/docs/hub/local-cache
Here is a partial list of libraries and applications that use this cache layout
https://huggingface.co/docs/hub/repositories-next-steps
For even more background on Git, you can take a look at GitHub’s Git Guides
https://huggingface.co/docs/hub/models-uploading
Models on the Hub are Git-based repositories, which give you versioning, branches, discoverability and sharing features, integration with dozens of libraries, and more
https://huggingface.co/docs/hub/xet/index
Discussions Notifications Collections Webhooks GitHub Actions Notebooks Next Steps Licenses
Models
The Model Hub Model Cards Eval Results Leaderboard Data Gated Models Uploading Models Downloading Models Integrated Libraries Model Widgets Model Inference Models Download Stats Model Release Checklist Hardware Local
https://huggingface.co/docs/hub/repositories
In these pages, you will go over the basics of getting started with Git and Xet and interacting with repositories on the Hub
https://huggingface.co/docs/hub/models-downloading
co:<MODEL ID> # example: git clone git@hf
https://huggingface.co/docs/hub/security
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https://huggingface.co/docs/hub/repositories-getting-started
If you do not have git available as a CLI command yet, you will need to install Git for your platform
https://huggingface.co/docs/hub/spaces-dev-mode
Spaces Dev Mode
Spaces Dev Mode is a feature that eases the debugging of your application and makes iterating on Spaces faster
https://huggingface.co/koboldcpp/music/tree/main
/llama-cli -hf koboldcpp/music:Q4_K_M
Build from source code
git clone https://github
https://huggingface.co/docs/hub/storage-buckets
Unlike Git-based repositories (models, datasets, Spaces), buckets are non-versioned and mutable, designed for use cases where you need simple, fast storage such as training checkpoints, logs, intermediate artifacts, or any large collection of files that doesn’t need version control
https://huggingface.co/Mozilla/static-embeddings
0 -m 'Model release description'
git push origin tag v1
https://huggingface.co/docs/hub/spaces-overview
Watch the following video for a quick introduction to Spaces:
In the following sections, you’ll learn the basics of creating a Space, configuring it, and deploying your code to it
https://huggingface.co/docs/hub/storage-buckets-s3
The S3 API is one of several ways to reach bucket data — for Hugging Face-native access (the hf CLI, hf:// paths, and filesystem mounts) without separate S3 credentials, see Access Patterns
https://huggingface.co/koboldcpp/tts/resolve/main/Qwen3-TTS-12Hz-1.7B-Base-q8_0.gguf
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https://huggingface.co/microsoft/spaces
8/site-packages/transformers/processing_utils
https://huggingface.co/koboldcpp/tts/resolve/main/Qwen3-TTS-12Hz-1.7B-VoiceDesign-Q8_0.gguf
Ġpro ;čĊ ĉĉĉĉ ter ain ĠW ĠE Ġcom Ġreturn art ĠH ack import ublic Ġor est ment ĠG able Ġ- ine ill ind ere :: ity Ġ+