Putri is a structured pruning technique for LLMs that compensates for pruning errors via weight updates and sequential processing while pruning at the attention-head level to reach state-of-the-art results at extreme sparsity.
A framework for few-shot language model evaluation, 07 2024
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MixLLM uses global output-feature importance to set mixed bit-widths for LLM quantization and adds two-step dequantization plus software pipelining for system efficiency.
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Prune, Update and Trim: Robust Structured Pruning for Large Language Models
Putri is a structured pruning technique for LLMs that compensates for pruning errors via weight updates and sequential processing while pruning at the attention-head level to reach state-of-the-art results at extreme sparsity.
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MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design
MixLLM uses global output-feature importance to set mixed bit-widths for LLM quantization and adds two-step dequantization plus software pipelining for system efficiency.