Llama-3 8B

'LLaMA-3-8B' is a benchmark dataset used to evaluate the performance of Large Language Models (LLMs) in the context of post-training quantization techniques.

Papers using Llama-3 8B (10)

ZO-Act: Efficient Zeroth-Order Fine-Tuning via One-Shot Activation-Informed Low-Rank Subspaces2026Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention2026Theory-optimal Quantization Based on Flatness2026RECAP: A Resource-Efficient Method for Adversarial Prompting in Large Language Models2026Sparse Autoencoders Trained on the Same Data Learn Different Features2025 · 1 citesPrune&Comp: Free Lunch for Layer-Pruned LLMs via Iterative Pruning with Magnitude Compensation2025HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading2025A Simple Linear Patch Revives Layer-Pruned Large Language Models2025RoRA: Efficient Fine-Tuning of LLM with Reliability Optimization for Rank Adaptation2025TODO: Enhancing LLM Alignment with Ternary Preferences2024