1. Basic information
The Xiaomi MiMo model is a general intelligent base-related model and service system launched by the Xiaomi team, built around language model capabilities, and provides usable forms for web page interaction and developer access. The Xiaomi MiMo model covers model versions of different scales and different training stages, including both dense model sequences targeting inference tasks and hybrid expert model routes for efficient inference and agent workflows, with an overall emphasis on inference, code and complex task execution.
2. Product Overview
The positioning of the Xiaomi MiMo model is part of the general intelligent base capability, with the goal of supporting stronger reasoning and task completion capabilities on the basis of language understanding and generation, and providing deployable model weights and inference implementations for practical applications. Focusing on this positioning, the MiMo system covers two stages of pre-training and post-training in the training process, and emphasizes key indicators such as inference throughput, context length and cost efficiency in engineering implementation to adapt to different needs from research evaluation to product integration.
3. Model family and representative version
1. MiMo-7B inference model sequence
The MiMo-7B series is a sequence of reasoning-oriented language models trained from scratch, providing a base model, a supervised fine-tuning model, and a model morphology aligned by reinforcement learning. This route emphasizes the improvement of the reasoning potential of the foundation model through pre-trained data processing and data mixing strategies, and introduces verifiable mathematical and programming problems for reinforcement learning in the post-training stage, so that the model can achieve more stable improvement in mathematical reasoning and code reasoning tasks.
2. MiMo-V2-Flash efficient inference and agent model
MiMo-V2-Flash belongs to the hybrid expert architecture model route, which adopts the design of separating the total parameter scale and the activation parameter scale, and is oriented to high-speed inference and agent workflow. This version is engineered to balance inference efficiency with long-context capability, and provide resources such as weights and inference code for real-world deployments.
4. Core functions and capability boundaries
1. Reasoning and problem solving
The Xiaomi MiMo model emphasizes the performance of verifiable reasoning tasks and is suitable for scenarios such as mathematical derivation, logic problems, step-by-step analysis and multi-constraint reasoning. For tasks that require decomposition of problems, step-by-step solving, and outputting structured conclusions, MiMo systems usually rely on reinforcement learning and verifiable data construction as key supports.
2. Code understanding and generation
The MiMo system takes programming-related capabilities as an important direction, which can be used for tasks such as code completion, function implementation, unit test assistance, error positioning and repair suggestions, and can also be used as code reasoning components in automated workflows. The focus of different versions on code tasks may be different, and the corresponding model version description and evaluation results shall prevail.
3. Agent and tool call tasks
In agent workflow scenarios, MiMo-related models can be used for task planning, step-by-step execution, and converting natural language instructions into executable operation sequences. These capabilities often rely on stronger long-context processing, stable instruction adherence, and the ability to maintain multiple rounds of state, making them suitable as foundational components for complex task execution and process automation.
5. Key technical features
1. Pre-training and data strategy
The MiMo-7B route emphasizes improving the density of inference modes through data preprocessing enhancement and multi-stage data mixing in the pre-training stage, and introducing mechanisms such as multi-token prediction into the training target to take into account the ability and inference efficiency.
2. Post-training and reinforcement learning alignment
The MiMo-7B route introduces a regularly verifiable dataset for reinforcement learning in the post-training stage, focusing on reducing the dependence on subjective rewards with verifiable signals, thereby improving training stability and reproducibility, and supporting consistency improvements in math and code tasks.
3. Efficiency optimization and long context capability
The MiMo-V2-Flash route introduces a hybrid expert architecture to reduce the effective computation amount during inference, and improves throughput and reduces cache pressure through designs such as hybrid attention and multi-token prediction, while supporting longer context windows to adapt to the needs of long documents, codebases, and multi-round task execution.
6. Acquisition method and deployment form The
Xiaomi MiMo model is usually provided in two ways: one is research resources such as model weights and technical reports, which are convenient for research and self-deployment evaluation; The second is the user-oriented web page interaction and developer API access form, which is used to integrate model capabilities into applications and services. The deployment requirements and the scope of support for different versions of the inference framework may be different, and the actual release repository and technical description shall prevail.
7. Pricing and Version
:MiMo-related services may have both self-deployment and online API call methods with open source weights. Billing rules, free credits, and regional availability for online APIs are generally subject to change over time and may vary by region; If it needs to be used for production environment cost accounting, the real-time display on the official open platform page shall prevail.
8. Applicable scenarios and groups
The Xiaomi MiMo model is suitable for R&D and product teams that require reasoning and code capabilities, including but not limited to agent application development, code generation and repair assistance, mathematical and logical reasoning evaluation, long documents and knowledge base Q&A, multi-round task planning and execution, etc. For teams that need self-deployed and controllable inference links, open-source weights and inference implementations can also be used to build on-premises or privatized inference services.
9. Frequently Asked Questions
1. What is the difference between MiMo-7B and MiMo-V2-Flash in positioning
Q: What is the difference between MiMo-7B and MiMo-V2-Flash in the Xiaomi MiMo model?
A: MiMo-7B is more inclined to dense small and medium-sized inference model sequences, emphasizing the shaping of reasoning ability from pre-training to post-training and verifiable reinforcement learning. MiMo-V2-Flash is more inclined to the hybrid expert route, emphasizing efficiency optimization for inference throughput, long context, and agent workflow scenarios.
2. Does the Xiaomi MiMo model support local deployment
Q: Can the Xiaomi MiMo model be deployed offline or private?
A: Some MiMo models provide open source weights and inference-related resources, which can be used for self-deployment and privatization inference service construction; The specific available weights, inference codes, and license scope are subject to the release notes of the corresponding version.
3. What tasks is the MiMo model suitable for priority
Q: Is the Xiaomi MiMo model more suitable for reasoning or chat conversations?
A: The MiMo system as a whole emphasizes reasoning, code and complex task execution, and also supports general dialogue interaction. If the tasks are mainly mathematical derivation, code reasoning, and agent processes, they can usually give full play to the advantages of their training routes.
4. How to confirm the context length and input limit of the MiMo model
Q: What is the context length of the Xiaomi MiMo model?
A: The context capabilities of different versions are different, and the technical description of the specific model version shall prevail; During engineering integration, it is also necessary to confirm the inference framework, hardware resources and service-side limitations.
