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Found 6 papers, 0 papers with code
Understanding the Difficulty of Low-Precision Post-Training Quantization for LLMs
Large language models of high parameter counts are computationally expensive, yet can be made much more efficient by compressing their weights to very low numerical precision.
Scaling Laws for Post Training Quantized Large Language Models
Generalization abilities of well-trained large language models (LLMs) are known to scale predictably as a function of model size.
Post Training Quantization of Large Language Models with Microscaling Formats
Large Language Models (LLMs) have distinguished themselves with outstanding performance in complex language modeling tasks, yet they come with significant computational and storage challenges.
Self-Selected Attention Span for Accelerating Large Language Model Inference
We demonstrate that using this custom CUDA kernel improves the throughput of LLM inference by 28%.
Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes
The interpretability of the EBM models reveals surprising insights into the features contributing to risk (e. g. maternal height is the second most important feature for shoulder dystocia) and may have potential for clinical application in the prediction and prevention of serious complications in pregnancy.
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies.
