MiMo-V2.5 Model Documentation and Integration Guide
Blog post from Deepinfra
XiaomiMiMo's MiMo-V2.5 is an advanced omnimodal AI model designed to process and understand diverse data types such as text, image, video, and audio through a unified architecture leveraging a 310-billion-parameter Sparse Mixture of Experts framework, activating only 15 billion parameters during inference. This model offers a substantial context window of 1 million tokens, supporting complex multimodal perception and autonomous workflows, while integrating native encoders for various data types to enhance cohesion. MiMo-V2.5 showcases significant improvements over its predecessor, particularly in reasoning and computational efficiency, by employing a hybrid attention architecture and multi-token prediction modules that enhance inference speed and reinforcement learning efficacy. Hosted on DeepInfra, it provides high-performance, low-latency inference via an API compatible with OpenAI, making it a versatile choice for developers aiming to implement agentic workflows and process extensive document sets. Pricing is usage-based, with options for standard and priority tiers to optimize cost and processing speed, making the model accessible for professional-grade deployments.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 3 | 804 | 153 | 68 | -87% |
| Reinforcement learning | 2 | 5 | 2 | 2 | -93% |
| Vector Search | 2 | 260 | 55 | 31 | -89% |
| AI Agents | 1 | 744 | 142 | 68 | -87% |
| AI Model Fine-tuning | 1 | 61 | 20 | 16 | -92% |
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