COALA applies convex optimization reformulations of neural networks to direct preference optimization, claiming single-GPU training with ~18% of DPO's TFLOPs and competitive performance on multiple datasets and models up to 8B parameters.
arXiv preprint arXiv:2409.09086 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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A roofline-based model is used to assess bandwidth and latency needs for High Bandwidth Storage in 13B-parameter models with long contexts and the utility of bonded memory chiplets for 1B-parameter models to ease capacity and bandwidth constraints in on-device gen-AI inference.
citing papers explorer
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Convex Optimization for Alignment and Preference Learning on a Single GPU
COALA applies convex optimization reformulations of neural networks to direct preference optimization, claiming single-GPU training with ~18% of DPO's TFLOPs and competitive performance on multiple datasets and models up to 8B parameters.
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Technology solutions targeting the performance of gen-AI inference in resource constrained platforms
A roofline-based model is used to assess bandwidth and latency needs for High Bandwidth Storage in 13B-parameter models with long contexts and the utility of bonded memory chiplets for 1B-parameter models to ease capacity and bandwidth constraints in on-device gen-AI inference.