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pcuenqย
updated
a
dataset
about 16 hours ago
sergiopaniegoย
posted
an
update
1 day ago
sergiopaniegoย
updated
a
dataset
1 day ago
Post
443
The #1 trending AI/ML dataset today ๐
Massive scale, diversity and end-to-end potential from nvidia !
nvidia/PhysicalAI-Autonomous-Vehicles
Massive scale, diversity and end-to-end potential from nvidia !
nvidia/PhysicalAI-Autonomous-Vehicles
Post
353
The new King ๐has arrived!
Moonshot AI now the top model on Hugging Face ๐ฅ
moonshotai/Kimi-K2-Thinking
Moonshot AI now the top model on Hugging Face ๐ฅ
moonshotai/Kimi-K2-Thinking
Post
2566
๐ธ๐คYou donโt need 100 GPUs to train something amazing!
Our Smol Training Playbook teaches you a better path to world-class LLMs, for free!
Check out the #1 trending space on ๐ค :
HuggingFaceTB/smol-training-playbook
Our Smol Training Playbook teaches you a better path to world-class LLMs, for free!
Check out the #1 trending space on ๐ค :
HuggingFaceTB/smol-training-playbook
Post
2873
Kimi K2 Thinking is now live on the hub ๐ฅ
moonshotai/Kimi-K2-Thinking
โจ 1T MoE for deep reasoning & tool use
โจ Native INT4 quantization = 2ร faster inference
โจ 256K context window
โจ Modified MIT license
moonshotai/Kimi-K2-Thinking
โจ 1T MoE for deep reasoning & tool use
โจ Native INT4 quantization = 2ร faster inference
โจ 256K context window
โจ Modified MIT license
Post
450
Chinese open source AI in October wasnโt about bigger models, it was about real world impact ๐ฅ
https://huggingface.co/collections/zh-ai-community/october-2025-china-open-source-highlights
โจ Vision-Language & OCR wave ๐
- DeepSeek-OCR : 3B
- PaddleOCR-VL : 0.9B
- Qwen3-VL : 2B / 4B / 8B / 32B /30B-A3B
- Open-Bee: Bee-8B-RL
- http://Z.ai Glyph :10B
OCR is industrializing, the real game now is understanding the (long context) document, not just reading it.
โจ Text generation: scale or innovation?
- MiniMax-M2: 229B
- Antgroup Ling-1T & Ring-1T
- Moonshot Kimi-Linear : linear-attention challenger
- Kwaipilot KAT-Dev
Efficiency is the key.
โจ Any-to-Any & World-Model : one step forward to the real world
- BAAI Emu 3.5
- Antgroup Ming-flash-omni
- HunyuanWorld-Mirror: 3D
Aligning with the โworld modelโ globally
โจ Audio & Speech + Video & Visual: released from entertainment labs to delivery platforms
- SoulX-Podcast TTS
- LongCat-Audio-Codec & LongCat-Video by Meituan delivery paltform
- xiabs DreamOmni 2
Looking forward to what's next ๐
https://huggingface.co/collections/zh-ai-community/october-2025-china-open-source-highlights
โจ Vision-Language & OCR wave ๐
- DeepSeek-OCR : 3B
- PaddleOCR-VL : 0.9B
- Qwen3-VL : 2B / 4B / 8B / 32B /30B-A3B
- Open-Bee: Bee-8B-RL
- http://Z.ai Glyph :10B
OCR is industrializing, the real game now is understanding the (long context) document, not just reading it.
โจ Text generation: scale or innovation?
- MiniMax-M2: 229B
- Antgroup Ling-1T & Ring-1T
- Moonshot Kimi-Linear : linear-attention challenger
- Kwaipilot KAT-Dev
Efficiency is the key.
โจ Any-to-Any & World-Model : one step forward to the real world
- BAAI Emu 3.5
- Antgroup Ming-flash-omni
- HunyuanWorld-Mirror: 3D
Aligning with the โworld modelโ globally
โจ Audio & Speech + Video & Visual: released from entertainment labs to delivery platforms
- SoulX-Podcast TTS
- LongCat-Audio-Codec & LongCat-Video by Meituan delivery paltform
- xiabs DreamOmni 2
Looking forward to what's next ๐
AdinaYย
authored
a
paper
13 days ago
sergiopaniegoย
posted
an
update
14 days ago
Post
5295
fine-tuning a 14B model with TRL + SFT on a free Colab (T4 GPU)?
thanks to the latest TRL optimizations, you actually can!
sharing a new notebook showing how to do it ๐
colab: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_trl_lora_qlora.ipynb
notebooks in TRL: https://github.com/huggingface/trl/tree/main/examples/notebooks
thanks to the latest TRL optimizations, you actually can!
sharing a new notebook showing how to do it ๐
colab: https://colab.research.google.com/github/huggingface/trl/blob/main/examples/notebooks/sft_trl_lora_qlora.ipynb
notebooks in TRL: https://github.com/huggingface/trl/tree/main/examples/notebooks
- 2 replies
sergiopaniegoย
posted
an
update
15 days ago
Post
405
Gave a smol ๐ค intro to Agents using smolagents last Monday!
Sharing the slides in case you're curious. They serve as a gentle first step into the Agents Course we developed at @huggingface ๐ซถ๐ซถ
Course: https://huggingface.co/learn/agents-course/unit0/introduction
Workshop material: https://github.com/sergiopaniego/talks/tree/main/intro_to_agents
Sharing the slides in case you're curious. They serve as a gentle first step into the Agents Course we developed at @huggingface ๐ซถ๐ซถ
Course: https://huggingface.co/learn/agents-course/unit0/introduction
Workshop material: https://github.com/sergiopaniego/talks/tree/main/intro_to_agents
Post
439
Kimi Linear๐ Hybrid linear attention model from Moonshot AI
https://huggingface.co/collections/moonshotai/kimi-linear-a3b
โจ 48B total/ 3B active - MIT license
โจ Up to 1M context
โจ 84.3 on RULER (128k) with 3.98ร speedup
โจ Hybrid KDA + MLA architecture for peak throughput & quality
https://huggingface.co/collections/moonshotai/kimi-linear-a3b
โจ 48B total/ 3B active - MIT license
โจ Up to 1M context
โจ 84.3 on RULER (128k) with 3.98ร speedup
โจ Hybrid KDA + MLA architecture for peak throughput & quality
sergiopaniegoย
posted
an
update
18 days ago
Post
3092
Sharing the slides from yesterday's talk about "Fine Tuning with TRL" from the
@TogetherAgent
x
@huggingface
workshop we hosted in our Paris office ๐!
Link: https://github.com/sergiopaniego/talks/blob/main/fine_tuning_with_trl/Fine%20tuning%20with%20TRL%20(Oct%2025).pdf
Link: https://github.com/sergiopaniego/talks/blob/main/fine_tuning_with_trl/Fine%20tuning%20with%20TRL%20(Oct%2025).pdf
sergiopaniegoย
posted
an
update
20 days ago
Post
1708
Ming-flash-omni Preview ๐ Multimodal foundation model from AntGroup
inclusionAI/Ming-flash-omni-Preview
โจ Built on Ling-Flash-2.0: 10B total/6B active
โจ Generative segmentation-as-editing
โจ SOTA contextual & dialect ASR
โจ High-fidelity image generation
inclusionAI/Ming-flash-omni-Preview
โจ Built on Ling-Flash-2.0: 10B total/6B active
โจ Generative segmentation-as-editing
โจ SOTA contextual & dialect ASR
โจ High-fidelity image generation
Post
1788
Glyph ๐ฅ a framework that scales context length by compressing text into images and processing them with visionโlanguage models, released by Z.ai.
Paper:https://huggingface.co/papers/2510.17800
Model:https://huggingface.co/zai-org/Glyph
โจ Compresses long sequences visually to bypass token limits
โจ Reduces computational and memory costs
โจ Preserves meaning through multimodal encoding
โจ Built on GLM-4.1V-9B-Base
sergiopaniegoย
posted
an
update
26 days ago
Post
2837
Meet OpenEnv ๐, an open ecosystem of environments for intelligent agents. Build, share, and test agents safely and consistently.
Ideal for training with TRL (we include examples๐ค), deployment, and community collaboration via the HF Hub
Blog: https://huggingface.co/blog/openenv
Hub for Environments:
openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv
Ideal for training with TRL (we include examples๐ค), deployment, and community collaboration via the HF Hub
Blog: https://huggingface.co/blog/openenv
Hub for Environments:
openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv
- 1 reply
Post
2621
HunyuanWorld Mirror๐ฅa versatile feed forward model for universal 3D world reconstruction by Tencent
tencent/HunyuanWorld-Mirror
โจ Any prior in โ 3D world out
โจ Mix camera, intrinsics, depth as priors
โจ Predict point clouds, normals, Gaussians & more in one pass
โจ Unified architecture for all 3D task
tencent/HunyuanWorld-Mirror
โจ Any prior in โ 3D world out
โจ Mix camera, intrinsics, depth as priors
โจ Predict point clouds, normals, Gaussians & more in one pass
โจ Unified architecture for all 3D task
anditoย
authored
a
paper
27 days ago
Post
1712
Finally, our new paper is out! "๐๐ถ๐ป๐ฒ๐ฉ๐ถ๐๐ถ๐ผ๐ป: ๐ข๐ฝ๐ฒ๐ป ๐๐ฎ๐๐ฎ ๐๐ ๐๐น๐น ๐ฌ๐ผ๐ ๐ก๐ฒ๐ฒ๐ฑ"! ๐ฅณ
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:
๐ finding and cleaning data at scale
๐งน removing excessive duplicates across sources
๐ค 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!
๐ To celebrate the paper, Iโm also releasing a concatenated and shuffled version of the full dataset! ๐
Itโs 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:
๐ finding and cleaning data at scale
๐งน removing excessive duplicates across sources
๐ค 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!
๐ To celebrate the paper, Iโm also releasing a concatenated and shuffled version of the full dataset! ๐
HuggingFaceM4/FineVision_full_shuffled Itโs 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!