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MiMo-V2.5
MiMo-V2.5 is a Xiaomi MiMo model family with downloadable main, Base, Pro and Pro-Base entries. The main model understands text, images, video and audio; the Pro model is a language model.
The model cards link hosted access and serving guides. Choosing a variant, updating its configuration and connecting its outputs are separate parts of using the family. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
Related models, different inputs
The main model card describes media understanding; the Pro card describes a larger language model.
Why it stands out
Model limits and serving settings
The main card lists up to one million tokens of context while its example launch configures a smaller window.
Availability
Weights and serving references
The official collection links four model entries. Their authored cards provide download and deployment information.
Why it matters
What makes it useful
For a clip or recording, the SGLang cookbook shows video_url or audio_url alongside a text question. Its examples request a video summary or audio transcription and summary. These are media-understanding requests, distinct from generating a new video or voice recording.
What to know
Where it fits
Choose the main entry for the media-input workflow in the cookbook. Its variant table labels Pro as text-only, with multimodal support planned. The Pro name alone does not establish that it accepts the main model’s media inputs. When connecting responses to an application, separate the answer from reasoning. The cookbook reads message.reasoning_content and message.content separately; its tool example checks tool_calls as another field. Choose which result your interface displays or handles rather than treating every field as the final answer. Xiaomi announced the newer MiMo-V2.6 family on September 22, 2026. The input and setup distinctions here describe V2.5, rather than transferring the newer family's capabilities to these checkpoints.
Notable points
What stands out
A model’s stated context limit and a running server’s setting can differ. The main card lists up to one million tokens, but its SGLang launch example sets context-length to 262144. Check the launch configuration when planning a long request; the advertised maximum is not the example’s enabled window.
Before using
What to review
The main card warns that downloads preceding commit 4da2748 may behave worse with outdated configuration. Its stated response is to refresh config.json and tokenizer_config.json. It supplies a download command for those two files rather than the entire checkpoint. Check that notice when returning to an earlier local copy. Choose what media and private context may enter the selected local or API path. Set which tools, files, credentials and external actions the surrounding application may reach, and where a person reviews consequential actions.
Reader fit
Who may find it relevant
It fits technical builders comparing media-understanding or text-focused variants for their own applications, with a serving stack capable of loading the selected checkpoint. For serving the main checkpoint, the cookbook documents attention weights interleaved for tensor parallelism of four. It says a bare TP=8 configuration fails and supplies attention DP size 2 alongside TP=8. It also requires LM-head and multimodal-encoder sharding flags when attention DP exceeds one. Use the matching combination when adapting its multi-GPU example.
Editorial note
Why LifeHubber lists it
We list MiMo-V2.5 for builders comparing how a long-context model accesses nearby and earlier tokens. Sliding-window layers attend within a local token window, while global layers can attend across the preceding context. Mixing them provides local processing alongside wider context access, so the 128-token local window is not the model's full context limit. The main card specifies a sliding-window-to-global attention ratio of 5:1, while Pro specifies 6:1. These describe different mixtures of local and wider attention to compare alongside the stated context capacity. The ratios do not establish memory use, serving speed or task accuracy on the reader's setup.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
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