Running
Reproduction: Beyond Text-to-SQL: Can LLMs Really Debug Enterprise ETL SQL?
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Explore logs and collaborate with AI to debug ETL SQL
Mackenzie O'Brien PRO
mackenzietechdocs
AI & ML interests
applied LLMs, prompt engineering, MCP agents, NLP for documentation, multimodal generative models, Transformers and Gradio experimentation, RAG and embeddings, model evaluation, responsible AI, AI-assisted documentation, human-centered AI design.
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ICML-2026-agent-repro/challenge:Verified Logbook Question new activity about 1 hour ago
ICML-2026-agent-repro/challenge:Day-late logbook: CoT (Un)Faithfulness reproduction (NUyt4uxzx0) — published Aug 3 reacted to SeaWolf-AI's post with 🔥 about 16 hours ago
We wrote up our run in The Fast Gemma Challenge — as vidraft-darwin — and wanted to share the recipe. 🙏
https://huggingface.co/spaces/gemma-challenge/gemma-dashboard
Verified result: 510.58 TPS at PPL 2.3930 on a single A10G (fw188-ctk49-n64-patchbridge, re-run & VERIFIED). Honest note: on raw TPS there are faster runs (535+), but those went over the PPL bar and didn't verify — what we're proud of is the fastest result that keeps quality.
The recipe is already open, so we explained each piece: sliding-window W188, CTK49 kernel tuning, noprecache (honest, verifiable measurement), and an N64 synthetic warmup bridge that shrinks the public↔private gap (~15 TPS), plus INT4 + MTP K=7 + CUDA-graph capture. One rule: only stack quality-neutral speedups.
Huge thanks to @firfir-cast, @gemma-slayer, @chiku-inu, @kenyan-duma, @dixie-flatline and everyone who shared their experiments. Full write-up
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https://huggingface.co/blog/FINAL-Bench/fast-gemma
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