🤖 >_ 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.
We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.
Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
if you like it give the demo a little star and send a shoutout to : @MaxLSB@jddqd and @GAD-cell for absolutely obliterating the pareto frontier of the french language understanding .
The moment we've been waiting for — ACE-Step dropped their new model: Ace-Step 1.5 🎉 🔗 ACE-Step/Ace-Step1.5 And the best part? It's released under the MIT license. We've already started integrating it into our project. Let's go 🚀