EMNLP AfterDark: Happy Hour with Abaka AI x SGLang
🌌 EMNLP AfterDark: Happy Hour with Abaka AI x SGLang
💕 This EMNLP party, hosted by Abaka AI and SGLang, is made for MLEs, tech leads, and NLP enthusiasts from leading companies.
No academic posturing.
No KPI stress.
No more spending two months chasing “practical implementation ideas.”
🤝🏻 Take a break from your packed EMNLP schedule and dive into open, relaxed conversations on the hottest topics in NLP — LLM Agents, multimodality, interpretability, and the long road toward AGI.
🙋Who’s Invited:
MLEs who can debug model overfitting before you finish describing it
“Oh, that bug? Yeah, I hit it last week.”
Tech Leads turning LLMs and RAG systems into real-world products.
No slide decks — just honest talk about surviving production.
NLP veterans, whose trend forecasts are sharper than your hyperparameter tuning — and can save you six months of trial and error.
👏🏻 Hidden Gem: We're thrilled to announce the attendance of a leading NLP Professor (Academia) and the Head of MiniMax-M2 (Industry).
📅 Date: November 5th, 2025 | 6:00 PM – 11:00 PM
📍 Location: Just a 2-minute drive from the EMNLP venue — with a breathtaking night view of Jinji Lake.
😋 Perks: Authentic German cuisine and top-tier craft beer — on the house. Come hungry, leave inspired.
🍺Don’t tune your tokenizer alone — come join us.
One conversation with the right person could unlock your next breakthrough — because when great minds align, opportunities never run out.
🌟About us
Abaka AI: We are a leading AI partner focusing on high-quality dataset solutions, data annotations, and other data-related services including model evaluation.
💼Follow us on LinkedIn (We're also hiring!)
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💻Visit our website abaka.ai
SGlang: Originating from RadixAttention, SGLang is an open-source, high-performance inference engine for large language models (LLMs) and vision language models (VLMs), incubated by the non-profit organization LMSYS. It delivers low-latency and high-throughput inference in various environments, from a single GPU to large distributed clusters.