4 papers
AACR-Bench: Evaluating Automatic Code Review with Holistic Repository-Level Context
Lei Zhang, Yongda Yu, Minghui Yu +11
High-quality evaluation benchmarks are pivotal for deploying Large Language Models (LLMs) in Automated Code Review (ACR). However, existing benchmarks suffer from two critical limi…
SQ-format: A Unified Sparse-Quantized Hardware-friendly Data Format for LLMs
Ruixuan Huang, Hao Zeng, Hantao Huang +4
Post-training quantization (PTQ) plays a crucial role in the democratization of large language models (LLMs). However, existing low-bit quantization and sparsification techniques a…
SALS: Sparse Attention in Latent Space for KV cache Compression
Junlin Mu, Hantao Huang, Jihang Zhang +3
Large Language Models capable of handling extended contexts are in high demand, yet their inference remains challenging due to substantial Key-Value cache size and high memory band…
AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training
Huawei Bai, Yifan Huang, Wenqi Shi +4
The training efficiency and scalability of language models on massive clusters currently remain a critical bottleneck. Mainstream approaches like ND parallelism are often cumbersom…