Wayfair improved model accuracy by 20+ points – want to learn how? Join to see how Wayfair and Snorkel used programmatic data development and foundation models to deliver a production model which helps customers search 40 million products. https://
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MACHINE LEARNING
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Wayfair Improves Model Accuracy Using Programmatic Data Development
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Custom GPTs Updated with Dependency Grammar Framework
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I just updated my Custom GPTs with Dependency Grammar Framework! You can test them out, but I already see content output as 100% human written: – Sales Email Generator GPT: https://
godofprompt.ai/gpts/sales-ema
il-generator
… – SEO Article Generator GPT: https://
godofprompt.ai/gpts/seo-artic
le-writer
… – Article Rewriter GPT: -
Phi Training Approach Criticism: Quality Data vs Quantity Debate
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in my opinion, the Phi approach to training language models is just wrong • i'm not convinced that training on less (albeit "higher-quality") data is better than training on as much data as possible
• i'm not convinced that training on synthetic data ever works better than -
Axelera AI Showcases In-Memory-Compute Computer Vision Solutions
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Join us at the @EmbVisionSummit in Santa Clara, USA, from May 21 – 23! Witness our cutting-edge AI solutions firsthand. Bram Verhoef, our head of Machine Learning, will present on May 22 about "Axelera AI: Digital In-Memory-Compute for Fast, Energy-Efficient Computer Vision"
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Google’s Machine Learning Energy Costs: Fact or Fiction?
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> Google alone spends 75% of its electrical cost only on machine learning today i've read this somewhere, but I'm wondering if there's a reference source for this claim?
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NVIDIA Hopper leads generative AI performance in MLPerf benchmarks
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NVIDIA Hopper takes lead in Generative AI on MLPerf! See https://
nvda.ws/3J1knIW In the latest (4th) round of #MLPerf performance benchmarking – the 'gold standard' for #AI workload #testing – the formidable Llama 2 70B and Stable Diffusion XL are center stage -
Training costs for 8B parameter models relatively affordable
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Couple hundred. Pretty cheap b/c only 16K on 8B
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Fine-tuning vs RAG: Best Practices for Model Knowledge
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Not that I've seen yet, but you probably don't want to do that – as a general rule fine-tuning on a small number of documents isn't an effective way to "add knowledge" to a model – you should look at implementing RAG instead Some notes on that here:
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Implementation Discovery of Speed Enhancement Through EvoSDXL-JP Model Merging
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Wow, this is amazing! I tried to download a model from HuggingFace and run it, but CUDA ran out of memory. That's odd, so I looked at the contents of the .py file and found that it appears to be implemented as "Download 4 ingredient models and merge synthesize EvoSDXL-JP on your end"…! So you can actually achieve speed enhancement just by merging…! x.com/iwiwi/status/1…
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LLMs as Arrays: Meditation on Entropy and Truth
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As I emerged from meditation it dawned on me that LLMs are just one array of floats and a while loop over some super simple arithmetic on its elements. It is entropy that is the root of suffering. It's by deleting the superfluous that we uncover truth. And thus I was enlightened.