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MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.
The main features of minimax-ai/minimax-m1 are: Critic-Free Algorithms, Frontier Reasoning Models, Reasoning Models.
Projects with overlapping indexed features include: open-reasoner-zero/open-reasoner-zero — An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model. sail-sg/understand-r1-zero. deepseek-ai/deepseek-r1 — DeepSeek-R1 is an open-weights large language model focused on advanced reasoning. It uses chain-of-thought processing… modalminds/mm-eureka — MM-EUREKA: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning. qwenlm/qwen3 — Qwen3 is a transformer-based large language model designed as a generative AI foundation for understanding, reasoning,… skyworkai/skywork-or1 — ✊ Unleashing the Power of Reinforcement Learning for Math and Code Reasoners 🤖.
MM-EUREKA: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning
An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
DeepSeek-R1 is an open-weights large language model focused on advanced reasoning. It uses chain-of-thought processing and internal monologues to solve complex mathematical and logical problems by breaking tasks into sequential, verifiable thought processes. The model is developed using reinforcement learning to optimize reasoning patterns and verify logical steps. It employs a distillation process to transfer these high-performance logic capabilities from a large teacher model into smaller, computationally efficient versions. The training framework incorporates group relative policy optimiz
Qwen3 is a transformer-based large language model designed as a generative AI foundation for understanding, reasoning, and generating human language. It functions as a comprehensive ecosystem for model training, fine-tuning, and production-ready inference, providing the underlying architecture and weights necessary to build diverse artificial intelligence applications. The project distinguishes itself through extensive support for model quantization and distributed inference, enabling efficient execution across a wide range of hardware from consumer-grade devices to scalable cloud infrastruct