Hugging Face's logo

Dataset Tools
community
Activity Feed Request to join this org

AI & ML interests

Tools for creating and exploring datasets

Recent Activity
gimmy256ย  authored a paper 11 days ago
CulturalRiddles: A Multicultural Benchmark of Riddles
emanuelaborosย  authored a paper 28 days ago
Beyond Single-Negative Preference: Multi-Negative DPO for LLM-Centric Historical Entity Linking
emanuelaborosย  authored a paper 28 days ago
When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations
View all activity
prithivMLmodsย 
posted an update about 5 hours ago
view post
Post
210
VIGOR (Vision Intelligence and Guardrail Oriented Reasoning) is now available on the Hugging Face Model Hub. It is a multimodal vision classification model based on Qwen3.5, designed for visual content-safety analysis and conservative classification, with a particular focus on identifying potentially sensitive content.

๐Ÿ“ฆ Models: 27B, 9B, 4B โ€”

prithivMLmods/VIGOR-27B,
prithivMLmods/VIGOR-flash,
prithivMLmods/VIGOR-mini

โ†—๏ธ Collection:
https://huggingface.co/collections/prithivMLmods/vigor

To learn more, visit the app page or the respective model pages.
Tonicย 
posted an update about 17 hours ago
view post
Post
107
๐Ÿ‘‹Hey there folks ,

I wanted to use a Jev model in a practical way , and I came up with this OCR pipeline using decision-models to evaluate OCR quality .

Check it out :

Tonic/OCR-with-JEV
  • 1 reply
ยท
prithivMLmodsย 
posted an update 9 days ago
view post
Post
994
Flux-3-Image-Klein-LoRAs-Fast is now live on Hugging Face Spaces.๐Ÿค—๐Ÿ”ฅ

Plug and play with any custom LoRAs based on flux-3-image-klein. 16-step distilled fast inference, with support for prompt upsampling. More LoRAs will be added in the coming days!

๐Ÿ”— Space: prithivMLmods/flux-3-image-klein-loras-fast

๐Ÿ”— Image Generation Apps Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection

๐Ÿ”— GitHub: https://github.com/PRITHIVSAKTHIUR/flux-3-image-klein-loras-fast

To learn more, visit the app page or the respective model pages.
gimmy256ย 
authored a paper 11 days ago
prithivMLmodsย 
posted an update 27 days ago
view post
Post
4,764
OneDecision-VisionGuard-Demo is now available on Hugging Face Spaces!

๐Ÿค— Space: prithivMLmods/OneDecision-VisionGuard-Demo

This demo showcases the OneDecision-VisionGuard family of multimodal image classification models for detecting NSFW and other sensitive visual content, with structured JSON reasoning, improved accuracy, and better handling of edge cases such as sensitive imagery, uncensored analysis, scene descriptions, and classification reasoning.

๐Ÿ“ฆ Models: 27B, 9B, 4B โ€” prithivMLmods/OneDecision-VisionGuard-27B-SFT, prithivMLmods/OneDecision-VisionGuard-9B-SFT, prithivMLmods/OneDecision-VisionGuard-4B-SFT

โ†—๏ธ Collection: https://huggingface.co/collections/prithivMLmods/onedecision-visionguard

To learn more, visit the app page or the respective model pages.
emanuelaborosย 
authored 2 papers 28 days ago
alielfilali01ย 
posted an update about 1 month ago
view post
Post
589
multi-agents orchestration is old news by now. Try multi-teams of agents ! that is a whole different nightmare ... This is the real signal about the sparks of AGI.
emanuelaborosย 
authored 2 papers about 1 month ago
prithivMLmodsย 
posted an update about 1 month ago
view post
Post
3,965
Qwen-Image-2.1 Plug and Play LoRA App is now live on Hugging Face Spaces.

๐Ÿ”— Space: prithivMLmods/Qwen-Image-2.1-LoRAs-PnP

It supports standard inference, 4-step Turbo inference, custom LoRA lazy repacks, and LoRA Plug and Play (PnP), all in one setting!

๐Ÿ”— Qwen-Image-2.1 Image-to-Image LoRAs: https://huggingface.co/collections/prithivMLmods/qwen-image-21-image-to-image-loras

๐Ÿ”— GitHub: https://github.com/PRITHIVSAKTHIUR/Qwen-Image-2.1-LoRAs-PnP

To learn more, visit the app page or the respective model pages.
prithivMLmodsย 
posted an update about 2 months ago
view post
Post
866
VisionGuardrail EVO-2, a multimodal image-classification content-safety model based on Qwen/Qwen3.8-27B, is now available on the Hub!

Stricter image classification than before, with a dense 27-billion-parameter multimodal model, more precise reasoning, and improved captions for classifying visual media.

โž  Models: prithivMLmods/VisionGuardrail-Evo2-27B, prithivMLmods/VisionGuardrail-Evo2-27B-GGUF

โž  Collection: https://huggingface.co/collections/prithivMLmods/visionguardrail-evo2

โž  Previous Models: https://huggingface.co/collections/prithivMLmods/visionguardrail-collection

โคท To learn more, visit the app page or the respective model pages.
prithivMLmodsย 
posted an update about 2 months ago
view post
Post
505
Scribble-Board-Fast is a sketch-to-image workspace powered by Klein-9B, transforming doodles, brush strokes, stickers, and uploaded images into high-fidelity visuals with 4-step distilled sampling.

> Space: prithivMLmods/Scribble-Board-Fast
> GitHub: https://github.com/PRITHIVSAKTHIUR/Scribble-Board-Fast

> To learn more, visit the app page or the respective model pages.
prithivMLmodsย 
posted an update about 2 months ago
view post
Post
3,895
VisionGuardrail, a multimodal content-safety classifier based on Qwen3.5, is now available on Hugging Face in 4B and 9B variants. It is a direct upgrade to ImageShield-MMCF, providing improved parental controls through conservative visual content-safety filtering.

More About:
โž  hf.co/blog โ€” https://huggingface.co/blog/prithivMLmods/vision-guardrail-mini-blog

โž  Models:
โœฆ VisionGuardrail-4B: prithivMLmods/VisionGuardrail-4B
โœฆ VisionGuardrail-9B: prithivMLmods/VisionGuardrail-9B

โž  Dataset:
โœฆ ImageShield-Guardrail-Pro: prithivMLmods/ImageShield-Guardrail-Pro

โคท To learn more, visit the app page or the respective model pages.
prithivMLmodsย 
posted an update 2 months ago
view post
Post
3,117
ImageShield-MMCF โ€” Multimodal Content Filter is a multimodal content-safety classifier built on top of Qwen3.5 and is now available on Hugging Face!

This is the preview initial version (v1.0) of the model, designed to classify visual content as Safe or Unsafe, with a particular focus on detecting Not Safe for Work (NSFW) and other potentially sensitive visual content.

The demo is implemented in the prithivMLmods/opencaption-4b-vl-sft Space, which serves as an active content-safety layer for computer vision tasks. It helps block Not Safe for Work (NSFW) content generation and paves the way for more meaningful and responsible creativity.

โŠน ImageShield-MMCF-0.8B: prithivMLmods/ImageShield-MMCF-0.8B
โŠน ImageShield-MMCF-2B: prithivMLmods/ImageShield-MMCF-2B
  • 2 replies
ยท
prithivMLmodsย 
posted an update 3 months ago
view post
Post
5,298
The Qwen3.8 27B demo for object grounding is now available on Hugging Face Spaces.

It features three tasks: Object Detection (Bounding Boxes), Point Localization (Keypoints), and Spatial Guidance (Path Mapping).

Try it now: prithivMLmods/Qwen3.8-27B-Object-Detection
alielfilali01ย 
authored a paper 3 months ago
prithivMLmodsย 
posted an update 3 months ago
view post
Post
5,582
Made a demo for Text/Image-to-3D Video and Image-to-3D Video asset generation using TRELLIS.2. It is paired with Z-Image-Turbo to accelerate the input image preprocessing pipeline, streamlining the Image-to-3D workflow. The generated GLB (GL Transmission Format) files are converted into MP4 (MPEG-4) videos, making them easy to preview and share. Try it now on Hugging Face Spaces.๐Ÿค—

โž  Image-to-3D-Video-Asset-Generator: prithivMLmods/Image-to-3D-Video-Asset-Generator
โž  collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
โž  github: https://github.com/PRITHIVSAKTHIUR/Image-to-3D-Video-Asset-Generator

โคท To learn more, visit the app page or the respective model pages.
emanuelaborosย 
authored a paper 4 months ago
mmhamdyย 
posted an update 4 months ago
view post
Post
252
Decades before the modern scaling laws, this paper showed that neural networks behavior under scale follows remarkably predictable laws.

In 1993, researchers at Bell Labs were grappling with a constraint that feels entirely familiar (and contemporary): datasets were outgrowing the available hardware, and training a model to the end was becoming too expensive. To evaluate an architectural tweak to a state-of-the-art model (at the time it was LeNet) on 60,000 samples meant burning up to three weeks of compute time.

To save compute, people would train candidate architectures on small subsets of the data, assuming that the top performer at small scale would remain the top performer at full scale. But with our future wisdom, we know this is not the case.

In "Learning Curves: Asymptotic Values and Rate of Convergence (NeurIPS 93)", using insights from statistical mechanics, they proposed a practical and principled method for predicting the performance of classifiers trained on large datasets (at the time, models were assumed to be large enough). The method was based on a simple power-law modeling of the expected training and test errors.

It is often noted that many of today's breakthroughs in AI and deep learning are actually decades-old concepts that simply lacked the computational power to be tested at the time. While there is some truth to that, it highlights a more valuable lesson: there is immense worth in revisiting early literature and reflecting on foundational ideas we may have prematurely left behind.

So, go explore and find your own inspiration. The current trend has enough champions already!
  • 2 replies
ยท