inclusionAI
LLaDA2.2-flash
LLaDA2.2-flash is an agent-oriented Mixture-of-Experts (MoE) diffusion language model designed for long-context agentic workloads. It introduces Levenshtein Editing with DELETE and INSERT control tokens to enable efficient parallel generation, error correction, and multi-turn tool use.
Ming-flash-omni 2.0
Ming-flash-omni 2.0 is an open-source, state-of-the-art multimodal large language model developed by inclusionAI. It leverages the Ling-2.0 architecture, a Mixture-of-Experts (MoE) framework comprising 100 billion total parameters, with 6 billion active parameters per token. This design enables efficient scaling and empowers unified multimodal intelligence across vision, speech, and language, representing a significant advancement toward Artificial General Intelligence (AGI).
Ming-omni-tts-16.8B-A3B
Ming-omni-tts-16.8B-A3B is a high-performance unified audio generation model developed by inclusionAI. It enables precise control over speech attributes and facilitates the synthesis of speech, environmental sounds, and music in a single channel.
LLaDA2.1
LLaDA2.1 is the latest iteration in the LLaDA series, focusing on accelerating text diffusion through token editing. This model aims to enhance the efficiency and performance of diffusion-based language models.
Ming-flash-omni 2.0
Ming-flash-omni 2.0 is an advanced multimodal foundation model developed by inclusionAI, leveraging a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion total parameters, of which only 6.1 billion are active per token. This architecture enables efficient scaling and empowers unified multimodal intelligence across vision, speech, and language, representing a significant advancement toward Artificial General Intelligence (AGI).
Ring-2.5-1T
Ring-2.5-1T is the world's first open-source trillion-parameter reasoning model based on hybrid linear attention architecture. It introduces a high-ratio linear attention mechanism that reduces memory access overhead by over 10× and increases generation throughput by more than 3× for sequences exceeding 32K tokens, making it particularly suitable for deep reasoning and long-horizon task execution.