Cheng Luo
@ChengLuo_lc
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🌟 Announcing the 1st Workshop on Efficient Reasoning (ER) at @NeurIPSConf 2025 — Dec 6 or 7, San Diego !. 📣 We welcome submissions! Submit your work here: .🗓️ Deadline: September 1, 2025 (AoE) .🔗 Website: 💬 Topics
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RT @InfiniAILab: 🔥 We introduce Multiverse, a new generative modeling framework for adaptive and lossless parallel generation. 🚀 Multivers….
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RT @InfiniAILab: 🥳 Happy to share our new work – Kinetics: Rethinking Test-Time Scaling Laws. 🤔How to effectively build a powerful reasoni….
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RT @AnimaAnandkumar: HeadInfer: Unlocking Long-Context LLM Inference on Consumer GPUs (Million-level Tokens).*long-context inputs require l….
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Dec. 10-Dec. 15 in NeurIPS’24. Our poster will be presented Friday 11 am.See u in Vancouver!
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RT @ChengLuo_lc: 🤩🤩 we introduce MST, a memory-efficient transformer, reducing intermediate memory usage and enabling longer sequence train….
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🤩🤩 we introduce MST, a memory-efficient transformer, reducing intermediate memory usage and enabling longer sequence training without compromising performance. 🚀🚀 How does it work? MST partitions sequence into mini-sequences and apply activation recomputation for optimal
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RT @rohanpaul_ai: Really 👀 new Paper, MINI-SEQUENCE TRANSFORMER claims to extend the maximum context length of Qwen, Mistral, and Gemma-2 b….
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RT @papers_anon: Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer. Saw a 16x increase in sequence le….
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Curious about boosting context length in Llama 3.1 by 16x? 🦙 Our Mini-sequence Transformer (MST) offers insights! 🚀 MST extends context length with no performance drop. 📈 Our paper: and GitHub: #Llama #NLP #AI #llama31.
github.com
Contribute to wdlctc/mini-s development by creating an account on GitHub.
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RT @AnimaAnandkumar: Introducing long-context transformer using mini sequences. It is a simple and effective method for highly efficient an….
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