Announcing Hugging Face Fundamentals: A New Learning Track on DataCamp
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sayakpaul
updated
a
dataset
about 9 hours ago
Post
61
Hugging Face MCP Server v0.2.35
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$HF_TOKEN is expanded in Jobs Secrets environment variables.
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$HF_TOKEN is expanded in Jobs Secrets environment variables.
Post
82
Ming-flash-omni Preview 🚀 Multimodal foundation model from AntGroup
inclusionAI/Ming-flash-omni-Preview
✨ Built on Ling-Flash-2.0: 10B total/6B active
✨ Generative segmentation-as-editing
✨ SOTA contextual & dialect ASR
✨ High-fidelity image generation
inclusionAI/Ming-flash-omni-Preview
✨ Built on Ling-Flash-2.0: 10B total/6B active
✨ Generative segmentation-as-editing
✨ SOTA contextual & dialect ASR
✨ High-fidelity image generation
Post
86
Glyph 🔥 a framework that scales context length by compressing text into images and processing them with vision–language models, released by Z.ai.
Paper:https://huggingface.co/papers/2510.17800
Model:https://huggingface.co/zai-org/Glyph
✨ Compresses long sequences visually to bypass token limits
✨ Reduces computational and memory costs
✨ Preserves meaning through multimodal encoding
✨ Built on GLM-4.1V-9B-Base
CharlieBoyer
updated
a
Space
about 16 hours ago
Post
2490
HunyuanWorld Mirror🔥a versatile feed forward model for universal 3D world reconstruction by Tencent
tencent/HunyuanWorld-Mirror
✨ Any prior in → 3D world out
✨ Mix camera, intrinsics, depth as priors
✨ Predict point clouds, normals, Gaussians & more in one pass
✨ Unified architecture for all 3D task
tencent/HunyuanWorld-Mirror
✨ Any prior in → 3D world out
✨ Mix camera, intrinsics, depth as priors
✨ Predict point clouds, normals, Gaussians & more in one pass
✨ Unified architecture for all 3D task
andito
authored
a
paper
5 days ago
Post
253
Hugging Face MCP Server v0.2.33
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Allow discovery of Product Documentation Library via the Search tool.
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Allow discovery of Product Documentation Library via the Search tool.
Post
1536
Finally, our new paper is out! "𝗙𝗶𝗻𝗲𝗩𝗶𝘀𝗶𝗼𝗻: 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗔𝗹𝗹 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱"! 🥳
FineVision: Open Data Is All You Need (2510.17269)
If you've ever trained a VLM, you know this problem: nobody shares their data mixtures. It's a black box, making replicating SOTA work impossible.
We wanted to change that.
FineVision unifies 200 sources into 24 million samples. With 17.3 million images and 9.5 billion answer tokens, it's the largest open resource of its kind.
In the paper, we share how we built it:
🔍 finding and cleaning data at scale
🧹 removing excessive duplicates across sources
🤗 decontaminating against 66 public benchmarks
My favorite part is Figure 6 (in the video!). It's our visual diversity analysis. It shows that FineVision isn't just bigger; it's more balanced and conceptually richer than other open datasets.
NVIDIA's Eagle 2 paper highlighted just how critical this visual diversity is, and our results confirm it: models trained on FineVision consistently outperform those trained on any other open dataset on 11 benchmarks!
🎉 To celebrate the paper, I’m also releasing a concatenated and shuffled version of the full dataset! 👉
It’s ready to stream, so you can start training your own models right away:
from datasets import load_dataset
d = load_dataset("HuggingFaceM4/FineVision_full_shuffled", split="train", streaming=True)
print(next(iter(d)))
A big shoutout to the first authors: Luis Wiedmann and Orr Zohar. They are rockstars!
FineVision: Open Data Is All You Need (2510.17269)
If you've ever trained a VLM, you know this problem: nobody shares their data mixtures. It's a black box, making replicating SOTA work impossible.
We wanted to change that.
FineVision unifies 200 sources into 24 million samples. With 17.3 million images and 9.5 billion answer tokens, it's the largest open resource of its kind.
In the paper, we share how we built it:
🔍 finding and cleaning data at scale
🧹 removing excessive duplicates across sources
🤗 decontaminating against 66 public benchmarks
My favorite part is Figure 6 (in the video!). It's our visual diversity analysis. It shows that FineVision isn't just bigger; it's more balanced and conceptually richer than other open datasets.
NVIDIA's Eagle 2 paper highlighted just how critical this visual diversity is, and our results confirm it: models trained on FineVision consistently outperform those trained on any other open dataset on 11 benchmarks!
🎉 To celebrate the paper, I’m also releasing a concatenated and shuffled version of the full dataset! 👉
HuggingFaceM4/FineVision_full_shuffled It’s ready to stream, so you can start training your own models right away:
from datasets import load_dataset
d = load_dataset("HuggingFaceM4/FineVision_full_shuffled", split="train", streaming=True)
print(next(iter(d)))
A big shoutout to the first authors: Luis Wiedmann and Orr Zohar. They are rockstars!
ariG23498
authored
a
paper
7 days ago
Post
585
PaddleOCR VL🔥 0.9B Multilingual VLM by Baidu
PaddlePaddle/PaddleOCR-VL
✨ Ultra-efficient NaViT + ERNIE-4.5 architecture
✨ Supports 109 languages 🤯
✨ Accurately recognizes text, tables, formulas & charts
✨ Fast inference and lightweight for deployment
PaddlePaddle/PaddleOCR-VL
✨ Ultra-efficient NaViT + ERNIE-4.5 architecture
✨ Supports 109 languages 🤯
✨ Accurately recognizes text, tables, formulas & charts
✨ Fast inference and lightweight for deployment
Post
1735
Bee-8B 🐝 open 8B Multimodal LLM built on high quality data, released by
TencentHunyuan
Paper: Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs (2510.13795)
Model: Open-Bee/bee-8b-68ecbf10417810d90fbd9995
✨ Trained on Honey-Data-15M, a 15M-sample SFT corpus with dual-level CoT reasoning
✨ Backed by HoneyPipe, a transparent & reproducible open data curation suite
TencentHunyuan
Paper: Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs (2510.13795)
Model: Open-Bee/bee-8b-68ecbf10417810d90fbd9995
✨ Trained on Honey-Data-15M, a 15M-sample SFT corpus with dual-level CoT reasoning
✨ Backed by HoneyPipe, a transparent & reproducible open data curation suite
multimodalart
posted
an
update
12 days ago
Post
1550
Want to iterate on a Hugging Face Space with an LLM?
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!
multimodalart/repo2txt
Post
283
Reflection ≠ self-correction
Interesting paper on long-chain reasoning 📑 from Miromind AI.
First Try Matters: Revisiting the Role of Reflection in Reasoning Models (2510.08308)
It dives into how LLMs think 🧠 Most reflections confirm, not fix, and true improvement comes from stronger initial reasoning.
Interesting paper on long-chain reasoning 📑 from Miromind AI.
First Try Matters: Revisiting the Role of Reflection in Reasoning Models (2510.08308)
It dives into how LLMs think 🧠 Most reflections confirm, not fix, and true improvement comes from stronger initial reasoning.
Post
220
Hugging Face MCP Server v0.2.31
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- OpenAI Apps SDK Support for Gradio Content Generation spaces
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- OpenAI Apps SDK Support for Gradio Content Generation spaces
lvwerra
authored
a
paper
14 days ago
Post
462
Ring-1T🔥 the trillion-parameter thinking model released by Ant group, the company behind Alipay
inclusionAI/Ring-1T
✨ 1T params (50B active)- MIT license
✨ 128K context (YaRN)
✨ RLVR, Icepop, and ASystem make trillion-scale RL stable
inclusionAI/Ring-1T
✨ 1T params (50B active)- MIT license
✨ 128K context (YaRN)
✨ RLVR, Icepop, and ASystem make trillion-scale RL stable
akhaliq
authored
a
paper
15 days ago
Post
490
KAT-Dev-72B-Exp🔥 Kuaishou's ( the company behind Kring AI ) new open model for software engineering
Kwaipilot/KAT-Dev-72B-Exp
✨ 72B - Apache2.0
✨ Redesigned attention kernel & training engine for efficient context-aware RL
✨ 74.6% accuracy on SWE-Bench Verified
Kwaipilot/KAT-Dev-72B-Exp
✨ 72B - Apache2.0
✨ Redesigned attention kernel & training engine for efficient context-aware RL
✨ 74.6% accuracy on SWE-Bench Verified
Post
4366
🌎 AI ethics and sustainability are two sides of the same coin.
In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.
Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.
We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:
📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.
🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.
AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.
Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability
AIEnergyScore/Leaderboard
sasha/environmental-transparency
In our new blog post with Dr. Sasha Luccioni, we argue that separating them (as is too often the case) means missing the bigger picture of how AI systems impact both people and the planet.
Ethical and sustainable AI development can’t be pursued in isolation. The same choices that affect who benefits or is harmed by AI systems also determine how much energy and resources they consume.
We explore how two key concepts, evaluation and transparency, can serve as bridges between these domains:
📊 Evaluation, by moving beyond accuracy or performance metrics to include environmental and social costs, as we’ve done with tools like the AI Energy Score.
🔍 Transparency, by enabling reproducibility, accountability, and environmental reporting through open tools like the Environmental Transparency Space.
AI systems mirror our priorities. If we separate ethics from sustainability, we risk building technologies that are efficient but unjust, or fair but unsustainable.
Read our blog post here: https://huggingface.co/blog/sasha/ethics-sustainability
AIEnergyScore/Leaderboard
sasha/environmental-transparency
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