Llama-2

LLaMA-2 is a dataset/benchmark used to evaluate the performance of Large Language Models (LLMs) in the context of model pruning and compression techniques.

Papers using Llama-2 (12)

Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels2025IntraSlice: Towards High-Performance Structural Pruning with Block-Intra PCA for LLMs2026Compressing LLMs with MoP: Mixture of Pruners2026Leveraging KV Similarity for Online Structured Pruning in LLMs2025TRIM: Achieving Extreme Sparsity with Targeted Row-wise Iterative Metric-driven Pruning2025Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based Language Models2025Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs2025Beyond One-Size-Fits-All Pruning via Evolutionary Metric Search for Large Language Models2025Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild2024AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation2024DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs2024LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models2024