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Building interactive demos to scikit-learn examples 🧡

Recent Activity

EduardoPacheco authored a paper 25 days ago

RVQ Position Aware Speculative Decoding for On Device Text to Speech

EduardoPacheco authored a paper about 2 months ago

Taming Long-form Text-to-Speech

suayptalha submitted a paper 3 months ago

ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads

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Tonic

posted an update about 1 hour ago

Post

👋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

turhancan97

posted an update 6 days ago

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New blog post: Seeing ViT Registers 🔍

I reproduced the main observation of "Vision Transformers Need Registers" (Darcet et al.) on six DINOv2 checkpoints from the Hub: Base, Large and Giant, each with and without registers.

Three findings:

1️⃣ Model size matters. In DINOv2 Giant, ~1.6% of patch tokens form a separate high-norm group. In Large the effect is weak, and in Base I found none.

2️⃣ Where you measure matters. The artifact tokens have very high norms before the final LayerNorm (hidden_states[-1]), but the lowest norms after it (last_hidden_state).

3️⃣ Registers take over. With registers, patch maps are smooth, and one register token carries a norm of ~1,346 instead.

Everything is inference-only and reproducible with one command. The post also includes practical tips for people who use DINOv2 features for dense tasks or probing.

📝 Blog: https://huggingface.co/blog/turhancan97/seeing-vit-registers
💻 Code: https://github.com/turhancan97/seeing-vit-registers

turhancan97

authored a paper 25 days ago

WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification

Paper • 2610.07384 • Published 28 days ago • 3

turhancan97

submitted a paper to Daily Papers 26 days ago

WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification

Paper • 2610.07384 • Published 28 days ago • 3

suayptalha

submitted a paper to Daily Papers 3 months ago

ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads

Paper • 2608.02703 • Published Aug 3 • 6

suayptalha

authored a paper 3 months ago

ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads

Paper • 2608.02703 • Published Aug 3 • 6

suayptalha

authored a paper 4 months ago

Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees

Paper • 2606.29331 • Published Jun 28

Shrijanagain

posted an update 4 months ago

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531

Welcome Researcher and Developers!

SKT AI Labs, we are pushing the boundaries of AI architecture and research—and today, we are thrilled to open our doors to the global research community!

We warmly welcome researchers, developers, and AI enthusiasts to join us and contribute to our R&D efforts.

🧪 What You Can Explore:

We invite you to experiment with our WMF (Weight Manifold Fusion) technology. You can test this high-dimensional fusion technique on smaller models to gain a deeper understanding of its behavior and token convergence.

---------- CHECK OUT:

SPACE : SKT-NRS/RD
EXPERIMENT : https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H
DIRECT TO MAIN DISCUSSION : SKT-NRS/RD#1

🤝 Your Feedback Shapes the Future :

If it works: Fantastic! Share your results with us and contribute directly to the core vision of SKT AI Labs.

If it doesn't work: No problem at all! Your critical feedback is just as valuable to us. Every experiment and anomaly helps us refine this architecture to make it more stable and robust.

We firmly believe that true innovation stems from community collaboration and transparent testing. Let's build the future of advanced AI together. Your ideas, test results, and feedback are always welcome!

You Can Still Research and Development On WMF Only SKT-SURYA-H Model is Dismissed.

Let's innovate and build together! 💡

Shrijanagain

posted an update 4 months ago

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481

🚀 Big News for the AI Community! 🔥

We’re excited to release NRS_QWEN_MYTHOS_1M — a powerful reasoning model built on Qwen 3.5 9B!
At SKT AI LABS, we’ve supercharged this 9B model with our proprietary Neural Reasoning System (NRS) to deliver next-level performance.

🔥 Why This Model is a Game-Changer:
✅ 100x Reasoning Capacity — Exceptional deep logical thinking and complex problem-solving
✅ 1 Million Token Context — Perfect for massive codebases, long documents, and multi-turn agentic workflows
✅ Advanced Thinking Mode — Native <think> tags for true step-by-step Chain-of-Thought reasoning
✅ Tool-Use Ready — Optimized for Python execution, Web Search, and self-correction
✅ Blazing Fast — Runs smoothly on consumer GPUs like RTX 3090/4090

Technical Highlights:

Base: Qwen 3.5 9B
Tuning: NRS-specific high-quality reasoning data
Context: 1M Tokens (YaRN Scaling)
License: NRS DOCS

Whether you’re a developer building coding agents, a researcher working with long-context data, or someone who loves powerful reasoning — this model is built for you.

👉 Try it now on Hugging Face:
SKT-NRS/NRS_QWEN_MYTHOS_1M

Drop a comment: What will you build with it first? 👇
#AI #OpenSource #LLM #Qwen #ReasoningModel #HuggingFace #NewModel #AICommunity

eienmojiki

posted an update 4 months ago

Post

342

Hi everyone,

I've created a Gradio space for embedding and extracting invisible watermarks in images:
👉 eienmojiki/blind-watermark-studio

It supports hiding text, images, and bit arrays using the DWT-DCT-SVD algorithm.

Credits:
- Original library: https://github.com/guofei9987/blind_watermark
- Author: Guo Fei

:).

Shrijanagain

posted an update 5 months ago

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2,685

We are pleased to announce that the W-IMG Vision Dataset infrastructure is officially live.

The complete asset infrastructure is now accessible on Hugging Face for internal validation and architecture scaling targets.

Dataset Endpoint - sKT-Ai-Labs/W-IMG

#SovereignAI #ComputerVision #MachineLearning #OpenSource

Tonic

posted an update 6 months ago

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3,551

🙋🏻‍♂️ Hey there folks ,

Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.

Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.

meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response .

I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.

At least that's the concept !

check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth

- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval

3 replies

Tonic

posted an update 6 months ago

Post

4,521

🙋🏻‍♂️ Hey there folks,

since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !

Check this one out :
NuTonic/sat-bbox-metadata-sft-v1

the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .

hope you like it ! 🚀

2 replies

Tonic

posted an update 6 months ago

Post

3,765

🙋🏻‍♂️ Hey there folks ,

I'm sharing huggingface's largest dataset of annotated statelite images today.

check it out here : NuTonic/sat-image-boundingbox-sft-full

I hope you like it , the idea is to be able to use this with small vision models 🚀

suayptalha

authored a paper 6 months ago

Selectivity and Shape in the Design of Forward-Forward Goodness Functions

Paper • 2604.13081 • Published Apr 16

Shrijanagain

posted an update 7 months ago

Post

4,350

sKT-Ai-Labs

Join fast we will soon published tokens and all join and get started because we will soon off join request button if you want you can join fast guys

1 reply

Shrijanagain

posted an update 7 months ago

Post

2,705

🚀 Bharat AI Revolution ka Hissa Banein! 🇮🇳

Kya aap Bharat ko AI ki duniya mein ek nayi pehchan dilana chahte hain ?

SKT AI Labs sirf ek naam nahi, ek mission hai—desh ko digital shakti dene ka aur "Viksit Bharat" ke sapne ko sach karne ka.

Humse Kyun Judein?

1. Desh ka Apna AI: Hum aise models bana rahe hain jo khas taur par Bharat ki zarooraton aur bhashaon ke liye hain.

2. Open Collaboration: Hamare Hugging Face repository par hamare kaam ko dekhein, test karein aur apna yogdan dein.

3. Technological Growth: Agar aap student hain, developer hain ya tech enthusiast hain, toh hamare saath naya seekhne aur grow karne ka yeh behtareen mauka hai.

Join here

sKT-Ai-Labs
🔗

sKT-Ai-Labs

Aaiye, saath milkar Bharat AI Revolution ko aage badhate hain! 💻🔥

#SKTAILabs #DigitalIndia #AIRevolution #ViksitBharat #TechInnovation #JoinTheMission

Shrijanagain

posted an update 7 months ago

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6,938

SOME NEW HINDI + ENGLISH DATASETS

🔗
- sKT-Ai-Labs/HIN
- sKT-Ai-Labs/SKT-MIX
- sKT-Ai-Labs/ST-H

Download and Use And Train Models

You Can Alsoo Use ST-x-LIGHTING Module For Faster Training

pip install ST-x-LIGHT-V11

2 replies

suayptalha

authored a paper 7 months ago

Diffutron: A Masked Diffusion Language Model for Turkish Language

Paper • 2603.20466 • Published Mar 20 • 5

Shrijanagain

posted an update 7 months ago

Post

5,676

We are thrilled to announce the launch of SKT-OMNI-CORPUS-2T, a massive-scale, high-quality dataset designed to power the next generation of Foundation Models (LLMs) from scratch.
Developed at SKT AI LABS, this corpus is not just a collection of data; it’s a mission to decentralize high-grade AI training for regional languages and global knowledge.

💎 Key Highlights:

•• Massive Scale: Targeting a multi-terabyte architecture for 2T-level tokenization.

•• Pure Quality: Curated from 500+ Elite Sources

•• Structured for MoE: Perfectly sharded into 3.5GB standardized units (SKT-𝕻 series) for seamless distributed training.

🤝 Open for Collaboration!

We are looking for AI researchers, CUDA engineers, and data scientists to join us in this journey of building Project Surya and the ST-X Series models. Whether it's optimization, custom tokenization, or architecture design—let’s build the future together.

Explore the Dataset on Hugging Face:

🔗 https://huggingface.co/datasets/Shrijanagain/SKT-OMNI-CORPUS-146T-V1

DSR -- 🔗 https://huggingface.co/datasets/Shrijanagain/SKT-DSRx10000

#AI #MachineLearning #OpenSource #IndicAI #SKTAILABS #LLM #BigData #HuggingFace #InnovationIndia

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