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Shrijanagain 
posted an update 4 days ago
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Excited to launch SKT-ST-X-0-3B by SKT AI Labs! 🚀🇮🇳

​A powerful 3B Parameter Mixture of Experts (MoE) model optimized for high-performance reasoning with a small footprint.


​--> Quick Specs:
> Total Params: ~3B | Active Params: ~1.1B (2 experts/token)
> Pre-trained on 40B tokens (SKT-OMNI-CORPUS-2T)

1.Context: 8K tokens
2.Bilingual: English & Hindi 🇬🇧🇮🇳
3. Base: Built on ST-X-0 with Mixtral stability


​Get 3B intelligence at 1B inference speeds. Fully open-source under Apache-2.0! 👇

​🔗 Try it on Hugging Face: sKT-Ai-Labs/SKT-ST-X-0-3B

​#AI #OpenSource #LLM #MixtureOfExperts #SKTAILabs #MachineLearning
codelion 
posted an update 12 days ago
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Inspired by the Nemotron Diffusion recipe, check out dhara-250m: a 250M experimental language model that supports three decoding modes from one set of weights: autoregressive, block-diffusion, and self-speculation.

It is small, easy to try, and meant for exploring diffusion-style decoding and latency tradeoffs in compact LMs.

Model: codelion/dhara-250m

Try the chat demo here: codelion/dhara-chat
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PhysiQuanty 
posted an update 13 days ago
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🌐 We crawled the entirety of Hugging Face to help the community! Huge thanks to the Hugging Face API 🌐
🤖 2.91M model repos (file names included), 📚 1.02M dataset repos, 🚀 1.31M Space repos
🤗 617,501 committers (datasets and models), we’ll share Hugging Face statistics with you in the coming days..

We also identified 61,398 users with “AI/ML Interests”, and NOW we can find each other through our “AI/ML Interests”🤗
HF-Collab-Center/Searching-For-HuggingFace-Users
HF-Collab-Center/All-Model-Repos
HF-Collab-Center/All-Dataset-Repos
HF-Collab-Center/All-Space-Repos

HF-Collab-Center/HF-Users
HF-Collab-Center/HF-Users-with-last-seen
HF-Collab-Center/HF-Users-With-AI-ML-Interests-Only

Made By @QuantaSparkLabs and @PhysiQuanty
C'est français, bon.. en anglais.. mais c'est français ;)
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PhysiQuanty 
posted an update 18 days ago
Shrijanagain 
posted an update 18 days ago
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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
PhysiQuanty 
posted an update 25 days ago
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❗ Dating apps do not allow us to control the profiles suggested to us based on our mutual search criteria ❗
🧬 If you want to see if your soulmate has already existed, I have published a dataset of 59k anonymized public profiles

SpiceeChat/OkCupid-59k-Anonymized-Profiles

Are you looking for a female ML engineer who is looking for a male ML engineer and you can't find it on the apps ?
You need to look for her, but more importantly, she needs to look for you.
Personally, I'm looking for a physicist I'm encountering the same problem. I can't find it
My answer : Paradox of choice of dating apps solved by patent ⚡ WO2026082672 ⚡
https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026082672

J'ai du breveté pour te trouver et on se trouvera bientôt !
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Tonic 
posted an update 25 days ago
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🙋🏻‍♂️ 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
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Tonic 
posted an update about 1 month ago
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🙋🏻‍♂️ 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 ! 🚀
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Tonic 
posted an update about 2 months ago
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🙋🏻‍♂️ 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 🚀
Shrijanagain 
posted an update 2 months ago
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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
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PhysiQuanty 
posted an update 2 months ago
Shrijanagain 
posted an update 2 months ago
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​🚀 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
PhysiQuanty 
posted an update 2 months ago
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🧬 Can an LLM speak in binary ?
✅ YES ... RADIX 2 / VOCAB 4
PhysiQuanty/Binary-LLM-POC

🤖 >_ Can an LLM execute logic gates and boolean arithmetic ?

We need to create datasets :
- Neural Arithmetic and Logic Unit (NALU) 32 bits
- Neural Application Binary Interface (NABI) 32 bits

🎯 Optimal Instruction Set = RV32IMAF

This opens the way for code writing and execution by the LLMs themselves without an external CLI.

The more of us who want it, the more possible it will become ...

PhysiQuanty/Binary-Addition-LLM-POC
(10-bits binary addition : binary carry propagation, sampling no longer has any effect on the logits due to the fact that it is deterministic next token.)

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Shrijanagain 
posted an update 2 months ago
Shrijanagain 
posted an update 3 months ago
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​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
ZennyKenny 
posted an update 3 months ago
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🤔 So we're supposed to post our repo storage graphs now right?