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🤗Intern-S2 Model Collections • 🤗Intern-S1 Model Collections • ModelScope • 📜Technical Report(S1) • 📜Technical Report(S1-Pro) • 💬Online Chat
The main features of internlm/intern-s1 are: Frontier Reasoning Models.
Projects with overlapping indexed features include: deepseek-ai/deepseek-r1 — DeepSeek-R1 is an open-weights large language model focused on advanced reasoning. It uses chain-of-thought processing… minimax-ai/minimax-m1 — MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. moonshotai/kimi-k2 — Kimi-K2 is a conversational AI engine and reasoning framework designed for text generation, advanced problem solving,… nvidia/megatron-lm — Megatron-LM is a distributed transformer training library and large language model training framework designed to… open-reasoner-zero/open-reasoner-zero — An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model. bytedance-seed/seed-oss — 👋 Hi, everyone! We are ByteDance Seed Team.
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
MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.
Kimi-K2 is a conversational AI engine and reasoning framework designed for text generation, advanced problem solving, and coding tasks. It functions as a tool-augmented language model capable of producing human-like chat responses through a compatible model interface. The system utilizes a reasoning-optimized architecture that separates standard conversational flow from deep logical processing. This allows the model to execute autonomous tasks by invoking external functions and calling APIs to retrieve real-time data. The project supports structured JSON output parsing for function-call inte
