Oct 9, 2024 · 1) The larger the models, the better they can preserve performance with an increased quantization ratio, as measured by perplexity in pre- ...
Oct 14, 2024 · 1) The larger the models, the better they can preserve performance with an increased quantization ratio, as measured by perplexity in pre-training tasks or ...
Oct 9, 2024 · We discuss the potential benefits and implications of the proposed scaling laws for future. AI inference systems and hardware designs, arguing ...
View recent discussion. Abstract: Post-training quantization of Large Language Models (LLMs) has proven effective in reducing the computational requirements ...
Oct 9, 2024 · The larger the models, the better they can preserve performance with an increased quantization ratio, as measured by perplexity in ...
Nov 28, 2024 · Key Takeaways 1 Larger LLMs can maintain performance with significantly fewer high-precision components, scaling exponentially as model size ...
Oct 12, 2024 · 1) The larger the models, the better they can preserve performance with an increased quantization ratio, as measured by perplexity in pre- ...
Yiren (Aaron) Zhao on LinkedIn: Scaling Laws for Mixed ...
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Oct 10, 2024 · We show that model size (Law 1) exhibit exponential scaling relative to the “ease of quantization”. While quantization granularity (Law 2) exhibit power ...
Oct 10, 2024 · We show that model size (Law 1) exhibit exponential scaling relative to the “ease of quantization”.
We use a Vector Quantized Variational autoencoders. (VQGAN (Esser et al., 2020)) model to tokenize image data into discrete tokens. The VQGAN model compresses.