including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer contexts.
@jiqizhixin
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MiniMax-01: Scaling Foundation Models with Lightning Attention
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introducing MiniMax-01: Scaling Foundation Models with Lightning Attention https://
github.com/MiniMax-AI Hugging Face：
https://
huggingface.co/MiniMaxAI https://
filecdn.minimax.chat/_Arxiv_MiniMax
_01_Report.pdf
… https://
hailuo.ai API：
https://
minimaxi.com/platform -
Transformer²: Self-Adaptive LLMs for Real-Time Task Adaptation
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introducing Transformer²: SELF-ADAPTIVE LLMS https://
arxiv.org/pdf/2501.06252 a novel self-adaptation framework that adapts LLMs for unseen tasks in real-time by selectively adjusting only the singular components of their weight matrices. -
Modern GAN Baseline: The GAN is Dead, Long Live the GAN
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The GAN is dead; long live the GAN! A Modern GAN Baseline https://
arxiv.org/abs/2501.05441 https://
github.com/brownvc/R3GAN HuggingFace： -
Train Your Own O1 Preview Model for Under $450
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Sky-T1: Train your own O1 preview model within $450 https://
novasky-ai.github.io/posts/sky-t1/ -
FedCFA: Counterfactual Federated Learning Resolves Simpson’s Paradox
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FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning https://
arxiv.org/abs/2412.18904 -
Do Not Overthink: o1-Like LLMs Reasoning Optimization
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introducing Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs https://
arxiv.org/pdf/2412.21187 -
Metadata Conditioning Accelerates Language Model Pre-training
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Metadata Conditioning Accelerates Language Model Pre-training Tianyu Gao Alexander Wettig Luxi He Yihe Dong Sadhika Malladi Danqi Chen Princeton Language and Intelligence, Princeton University https://
arxiv.org/pdf/2501.01956
v1
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SMAC-Hard: Mixed Opponent Strategy and Self-play Research
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SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC https://
arxiv.org/abs/2412.17707 -
ILLUME: Enhancing LLMs with Vision and Self-Improvement
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ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance https://
arxiv.org/pdf/2412.06673