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Found 2 papers, 1 papers with code
GrACE: Generation using Associated Code Edits
In our experiments with two datasets, the knowledge of prior edits boosts the performance of the LLMs significantly and enables them to generate 29% and 54% more correctly edited code in top-1 suggestions relative to the current state-of-the-art symbolic and neural approaches, respectively.
KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization
In recent years, the growing size of neural networks has led to a vast amount of research concerning compression techniques to mitigate the drawbacks of such large sizes.
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