4 papers
A Case Study of Selected PTQ Baselines for Reasoning LLMs on Ascend NPU
Yuchen Luo, Fangyue Zhu, Ruining Zhou +4
Post-Training Quantization (PTQ) is crucial for efficient model deployment, yet its effectiveness on Ascend NPU remains under-explored compared to GPU architectures. This paper pre…
vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models
Peiqi Yin, Jiangyun Zhu, Han Gao +13
Any-to-any multimodal models that jointly handle text, images, video, and audio represent a significant advance in multimodal AI. However, their complex architectures (typically co…
ACPO: Adaptive Curriculum Policy Optimization for Aligning Vision-Language Models in Complex Reasoning
Yunhao Wang, Ziting Li, Shuai Chen +6
Aligning large-scale vision-language models (VLMs) for complex reasoning via reinforcement learning is often hampered by the limitations of existing policy optimization algorithms,…
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
Bo Peng, Daniel Goldstein, Quentin Anthony +27
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-value…