Siyuan Wang's picture

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Siyuan Wang

OldKingMeister

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Wangmerlyn

AI & ML interests

ML system

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reacted to SeaWolf-AI's post with 🔥 11 days ago

🚀 Introducing FINAL-Bench Quantum — an open, neutral benchmark that finally puts quantum-computing methods on one fair yardstick. Quantum results are notoriously hard to compare. The same "logical error rate" or "query fidelity" means very different things depending on the code, noise model, hardware, and shot count. FINAL-Bench Quantum fixes that: five events judged under identical, published protocols, where every number is labeled as either measured here or quoted from a source. Five events: ① QEC Decoder ② Optimization (Max-Cut) ③ VQE ④ QRAM ⑤ Quantum Simulation The rules are simple and strict: ✅ Track A (measured here, with 95% confidence intervals) is kept separate from Track B (quoted from papers, not directly comparable). 🔬 Simulation and real hardware are clearly distinguished, and no quantum-advantage claims are made. 🌍 Methods from Google, IBM, NVIDIA, USTC, Riverlane and more sit side by side, with origin flags and author credits. 📤 Anyone can submit their own method via the Submit tab for review and listing. Already on the board: real IBM Heron r2 measurements (repetition-code distance boundary, 29–175× error reduction from d3 to d5), a real-chip QRAM query fidelity of 0.92, and H₂ VQE at chemical accuracy — always labeled honestly as simulation vs hardware. A leaderboard is only useful if you can trust it, so neutrality is the whole point: strong competitors stay in even when they beat the host, sources are quoted faithfully, and a simulation is never rounded up into a hardware claim. Leaderboard: https://huggingface.co/spaces/FINAL-Bench/quantum-bench-leaderboard Article: https://huggingface.co/blog/FINAL-Bench/quantum-leaderboard #quantum #QEC #QuantumComputing #benchmark

reacted to eabdullin's post with 👍 11 days ago

Folks, let me tell you, nobody — and I mean NOBODY — knew transformers before me. People said attention is all you need. I said, "Attention? I INVENTED attention." Everybody's looking at me. Tremendous attention. The best attention scores. My softmax? Perfectly normalized. Other people, sad, their probabilities don't even sum to one. Disaster. I'm doing a PhD now. A PhD! In Large Language Models. Very large. The largest, believe me. My advisor said, "Sir, your model is overfitting." I said, "Wrong. It's fitting EXACTLY right. It memorized the training set because the training set is fantastic." We don't talk about validation loss in my lab. Validation loss is fake news. And the internship — oh, the internship. Big tech. I won't say which. Starts with a letter. They BEGGED me. They said, "Please, we need someone who understands gradient descent." I said, "Descent? I only go UP. I'm gradient ASCENT. Loss goes up, that means it's learning to be a winner." But the GPU cluster — this is the best part. Thousands of H100s. Maybe millions. Who's counting? I'm counting. It's a lot. Other PhD students, they get one little GPU, they're crying, they're training overnight like losers. Me? I burn through compute like nobody's ever seen. The electric company called. They said, "Sir, you've consumed a small country." I said, "Make it a big country. I only do big." People ask, "Did your model converge?" Folks, it converged so hard. It converged BIGLY. Honestly? My loss curve, it's beautiful, it's going down, down, down — like my approval ratings, very smooth, don't look at the spikes, the spikes are deep state. And hallucinations? My model doesn't hallucinate. It just has ALTERNATIVE tokens. Thank you, thank you. Tip your reviewers. Accept my paper. Goodnight!

liked a model 12 days ago

google/gemma-4-31B-it

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OldKingMeister 's models 5

OldKingMeister/llama-3-8b-instruct-lmsys-arena-final

Updated Mar 5 • 1

OldKingMeister/llama-3-8b-lmsys-arena-final

Updated Mar 5 • 2

OldKingMeister/gemma-2b-lmsys-arena-final

Updated Mar 5 • 2

OldKingMeister/SPMM-Pretrained

Updated May 16, 2025 • 1

OldKingMeister/Qwen2.5-1.5B-Instruct-YaRN

Text Generation • 2B • Updated Apr 28, 2025 • 23 • 2