🔄 In a Training Loop

Dean Byrne PRO

Quazim0t0

16 3 56

dean-byrne-02a28b191

AI & ML interests

DaisyChainAI🌼 / SmallLM's / San Francisco / Open Source

Recent Activity

reacted to Shrijanagain's post with 👀 about 18 hours ago

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 : https://huggingface.co/spaces/SKT-NRS/RD EXPERIMENT : https://huggingface.co/sKT-Ai-Labs/SKT-SURYA-H DIRECT TO MAIN DISCUSSION : https://huggingface.co/spaces/SKT-NRS/RD/discussions/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! 💡

reacted to SeaWolf-AI's post with 🔥 about 18 hours ago

🔓 We ran genuine quantum key-recovery on 'real IBM quantum hardware' — and pushed the frontier well past the largest hardware demos we're aware of (which sat at N=4). Using Simon's algorithm on `ibm_kingston`, we recovered the secret key of two symmetric-cipher structures: • Even–Mansour — N=5 → N=10 • 3-round Feistel (DES-family) — block 6 → 8 Each verified against an 'independent control key', using error mitigation only (no QEC). 🧭 Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review. 📄 Write-up: https://huggingface.co/blog/FINAL-Bench/quantum 🕹️ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto 🏆 Leaderboard: https://huggingface.co/spaces/FINAL-Bench/quantum-bench-leaderboard #quantum #cryptography #quantumcomputing

reacted to RDTvlokip's post with 👍 about 18 hours ago

I finally changed the architecture of my 15M French LLM. It worked. Then I almost fooled myself about how much and catching that was the real win. After proving last time that architecture is a threshold, not a lever, I got stubborn: could I change how the model learns? Four honest attempts, Lion, a sharper AdamW β2, multi-token prediction, LayerScale. Four failures. The bottleneck wasn't the learning rule either. So I changed the shape of the computation instead: loop the same transformer blocks 4×, deeper reasoning, zero added parameters. It beat the baseline on perplexity, the first thing in the whole project to move that number. Then I added my own twist: let each token decide how deep to think, halting on its own entropy. My first evaluation was spectacular. Coherence up 65%. Hallucinated names down 62%. It was noise. Eight prompts, one seed. I re-ran on 50 prompts × 200 tokens and watched the gains shrink to "modest" and on out-of-domain prompts, recurrence actually made things worse. No universal winner. And none of it is new: it's Adaptive Computation Time (2016), the Universal Transformer (2018), and LoopViT (2026), recombined and measured honestly. The real lesson: A number from 8 prompts is a rumor. The eval harness that kills your own best result is worth more than the result it kills. Cite your lineage. Stay preliminary until multiple seeds say otherwise. The three models are live. The write-up is honest about every caveat 👇 🔗 https://huggingface.co/blog/RDTvlokip/teaching-a-15m-french-llm-to-think-deeper

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published an article 1 day ago

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Some notes on swapping out the "important" parts of neural nets