Skip to main content
Qingquan Song on Muck Rack

Qingquan Song

As seen in: Wiley Online Library, MDPI
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Qingquan

Contact Qingquan, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Valorization of Discarded Para‐Aramid Fibers for Synergistic Enhancement of Poly(m‐Phenylene Isophthalamide) Composites

|
By Qingquan Song, Guocheng Song, Junrong Yu
| Wiley Online Library Verified
Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Supporting Information Filename Description pc70733-sup-0001-supinfo.docxWord 2007 document , 1.3 MB Table S1: Tensile process parameters of pure PMIA fibers and PMIA/ANF composite fibers.
Open in Who Shared Issue with article?

Planner-R1: Reward Shaping Enables Efficient Agentic RL with Smaller LLMs

|
By Yanbin Jiang, Shao Xian Tang, Qingquan Song, Hejian Sang
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (511) Issue with article?

Implement Flash Attention Backend in SGLang - Basics and KV Cache

|
By Biao He, Qingquan Song
forward(): When model.forward() is called, the forward method in the AttentionBackend will be called. It will be calling forward_extend() and forward_decode according to the forward_batch.forward_mode. In this blog, we only focus on EXTEND and DECODE mode. forward_extend(): This method will be called for each layer when the forward_mode is EXTEND. forward_decode(): This method will be called for each layer when the forward_mode is DECODE.
Open in Who Shared Issue with article?
See all 5 articles