Rethinking LLM Evaluation with 3C3H: AraGen Benchmark and Leaderboard

Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)

Multiple-Choice Benchmarks, Verifiers, Leaderboards, and LLM Judges with Code Examples

Judge Arena: Benchmarking LLMs as Evaluators

BenchMIRT: What are LLM benchmarks actually measuring?

Open LLM Leaderboard: DROP deep dive

Introducing the Open Ko-LLM Leaderboard: Leading the Korean LLM Evaluation Ecosystem

Bringing the Artificial Analysis LLM Performance Leaderboard to Hugging Face

Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL

Understanding LoRA Rank Trade-offs in Diffusion Model Fine-Tuning

The study investigates how the rank of Low‑Rank Adaptation (LoRA) affects diffusion model fine‑tuning on CIFAR‑10 using a DDPM U‑Net. Experiments with ranks 2, 4, 8, 16, and 32 show that moderate ranks—particularly rank 4—yield the best FID scores while keeping trainable parameters, runtime, and GPU memory low. Higher ranks offer only marginal improvements at a higher computational cost, suggesting that small‑to‑moderate ranks are efficient defaults for fixed training budgets.

📚 3LM: A Benchmark for Arabic LLMs in STEM and Code

What's going on with the Open LLM Leaderboard?

Llama 2 on Amazon SageMaker a Benchmark

LLMVisor: A Real-Time Latency Attribution Model for Multi-Tenant LLM Serving

arXiv:2608. 08382v1 Announce Type: new Abstract: As LLM inference shifts to multi-tenant GPU clusters, co-batching improves throughput but obscures per-tenant usage and limits control.