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Awesome GitHub RepositoriesModel Layer Patching
Runtime replacement of specific neural network layers with optimized implementations without source code changes.
Distinct from Runtime Method Patching: Distinct from Runtime Method Patching: targets ML model layers specifically, not general application methods.
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Awesome Model Layer Patching GitHub Repositories
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- linkedin/liger-kernel
linkedin/Liger-Kernel
6,148在 GitHub 上查看Liger-Kernel is a collection of pre-built fused Triton kernels and patching utilities designed to accelerate large language model training. It provides drop-in kernel replacements for common LLM operations such as RMSNorm, cross-entropy loss, and attention, enabling increased throughput and reduced memory usage while preserving bitwise-exact gradients. The project serves as a toolkit for composing custom model architectures from individual optimized kernels and for patching pre-existing models with minimal code changes. The project distinguishes itself through its ability to perform runtime m
Enables runtime monkey-patching of Hugging Face and Megatron-LM model layers with optimized Triton kernels.
Pythonfinetuninggemma2hacktoberfest
