MODEL PROFILE

MiMo-V2.5-GGUF

unsloth · MiMo-V2.5-GGUF

⚠️ Important: Config Update Notice The config.json and tokenizer_config.json files in this repository have been updated since the initial release. If you downloaded MiMo-V2.5 before this commit (4da2748) , please re-pull or manually update these two files to ensure correct model behavior. Using the outdated config may lead to degraded model performance. We apologize for any inconvenience. Quick fix: hf download XiaomiMiMo/MiMo-V2.5 config.json tokenizer_config.json --local-dir ./MiMo-V2.5 MiMo-V2.5 is a native omnimodal model with strong agentic capabilities, supporting text, image, video, and audio understanding within a unified architecture. Built upon the MiMo-V2-Flash backbone and extended with dedicated vision and audio encoders, it delivers robust performance across multimodal perception, long-context reasoning, and agentic workflows. Key features include: MiMo-V2.5's core language backbone inherits from the MiMo-V2-Flash architecture, a sparse MoE model with hybrid sliding window attention.

multimodallicense:mit
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:16:53
OVERVIEW

Model overview

⚠️ Important: Config Update Notice The config.json and tokenizer_config.json files in this repository have been updated since the initial release. If you downloaded MiMo-V2.5 before this commit (4da2748) , please re-pull or manually update these two files to ensure correct model behavior. Using the outdated config may lead to degraded model performance. We apologize for any inconvenience. Quick fix: hf download XiaomiMiMo/MiMo-V2.5 config.json tokenizer_config.json --local-dir ./MiMo-V2.5 MiMo-V2.5 is a native omnimodal model with strong agentic capabilities, supporting text, image, video, and audio understanding within a unified architecture. Built upon the MiMo-V2-Flash backbone and extended with dedicated vision and audio encoders, it delivers robust performance across multimodal perception, long-context reasoning, and agentic workflows. Key features include: MiMo-V2.5's core language backbone inherits from the MiMo-V2-Flash architecture, a sparse MoE model with hybrid sliding window attention.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

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Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。