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Awesome GitHub RepositoriesPadding-Free Sequence Processing
Optimization techniques that remove non-informative padding tokens to reduce memory consumption during training and inference.
Distinct from Training Memory Optimizers: Distinct from Training Memory Optimizers: focuses specifically on padding removal rather than general memory optimization strategies like gradient checkpointing.
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- ibm-granite/granite-code-models
ibm-granite/granite-code-models
1,250在 GitHub 上查看Granite Code Models is a family of transformer-based foundational models designed for software engineering and logical reasoning tasks. These models are trained on high-quality programming datasets to interpret natural language prompts and generate functional source code, explain complex logic, repair code defects, and produce technical documentation. The project distinguishes itself through specialized training methodologies that align model behavior with complex programming instructions and mathematical problem-solving. By utilizing chain-of-thought reasoning and instruction-tuned parameter
Reduces activation memory consumption by removing non-informative padding tokens during training and inference.
