🤖 >_ 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.)
We are thrilled to announce the launch of SKT-OMNI-CORPUS-146T-V1, 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 146T-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.