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Found 5 papers, 3 papers with code
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Based on W2*A8 quantization configuration on LLaMA-7B model, it achieved a WikiText2 perplexity of 7. 59 (2. 17$\downarrow $ vs 9. 76 in AffineQuant).
FoldGPT: Simple and Effective Large Language Model Compression Scheme
Based on this observation, we propose an efficient model volume compression strategy, termed FoldGPT, which combines block removal and block parameter sharing. This strategy consists of three parts: (1) Based on the learnable gating parameters, we determine the block importance ranking while modeling the coupling effect between blocks.
SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity
Our framework consists of two parts: (a) A fine-grained kernel sparsity schema with a sparsity granularity between structured pruning and unstructured pruning.
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution
Since the high resolution of intermediate features in SISR models increases memory and computational requirements, efficient SISR transformers are more favored.
Residual Local Feature Network for Efficient Super-Resolution
Moreover, we revisit the popular contrastive loss and observe that the selection of intermediate features of its feature extractor has great influence on the performance.
