Qwen

The 'Qwen' dataset/benchmark contains a set of checkpoints used to evaluate the performance of weight-space model merging techniques, specifically focusing on expert access efficiency and I/O budget management.

Papers using Qwen (13)

EdgeRazor: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation2026TransMLA: Multi-head Latent Attention Is All You Need2025 · 6 citesAsk the Right Comparison:Bias-Aware Bayesian Active Top-$k$ Ranking with LLM Judges2026RAS: Measuring LLM Safety Through Refusal Alignment2026ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference2026Reasoning-preserved Efficient Distillation of Large Language Models via Activation-aware Initialization2026GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets2026Access Sets Matter: Budgeting Expert Reads for Scalable Weight-Space Model Merging2026NIRVANA: Structured pruning reimagined for large language models compression2025Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization2025ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization2025Accelerating Large Language Model Reasoning via Speculative Search2025LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades2025