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Found 2 papers, 1 papers with code
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning
While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training.
An Efficient 2D Method for Training Super-Large Deep Learning Models
However, due to memory constraints, model parallelism must be utilized to host large models that would otherwise not fit into the memory of a single device.
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