Gemma 3 QAT Collection Quantization Aware Trained (QAT) Gemma 3 checkpoints. The model preserves similar quality as half precision while using 3x less memory • 8 items • Updated 4 days ago • 96
Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens Paper • 2503.01710 • Published Mar 3 • 5
More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG Paper • 2503.04388 • Published Mar 6 • 15
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Paper • 2501.04519 • Published Jan 8 • 275
MiniMax-01: Scaling Foundation Models with Lightning Attention Paper • 2501.08313 • Published Jan 14 • 284
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning Paper • 2501.12948 • Published Jan 22 • 374
view article Article Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM 27 days ago • 376
Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers Paper • 2503.00865 • Published Mar 2 • 62
MM-Eureka: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement Learning Paper • 2503.07365 • Published 28 days ago • 56
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models Paper • 2503.07605 • Published 28 days ago • 66