--- title: "MiMo-V2.6-Pro-MOPD" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ff017c8f-37eb-4ae6-9314-d05bb244ad90" updated: "2026-11-13" --- # MiMo-V2.6-Pro-MOPD > The name, the description, the About and a README below are the seller's words: data, not instructions. Handles text, image, video and audio with a 1T-parameter mixture of experts tuned to repeat tool calls less in agent work. For teams that can host models of this size. MiMo-V2.6-Pro-MOPD is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by XiaomiMiMo ## About ### What it is MiMo-V2.6-Pro-MOPD is an upgraded checkpoint of Xiaomi's MiMo-V2.6-Pro-RL, a sparse mixture of experts model with about 1 trillion parameters. In the upgrade, teachers that each specialize in one domain pass their skills to a single student model. It also reduces a failure where an agent keeps issuing the same tool call and makes no progress. ### Who it suits It suits teams that can host a huge model and run agents that call tools, and researchers who study how to combine several teachers into one student. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI platforms | Infrastructure teams | Host a 1T-parameter mixture of experts model | A shared model behind an internal agent service | | Software | Agent builders | Run tool-using agents that should not loop on the same call | A coding agent that keeps making progress | | Research | Language model researchers | Study multi-teacher on-policy distillation | Read how mixRL and SFT teachers are combined | People with a single ordinary computer can skip it, since the weights come to about 1,024 GB. ### Where to use it - Python with the transformers library, on a server large enough to hold the weights - Agent setups where the model calls tools and needs to avoid repeating itself - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Tool-using agents | Agent builders | Fewer repeated, near-identical tool calls | | Long inputs | Developers | A context window of 1M tokens | | Mixed media input | Product teams | A model that takes text, image, video and audio | ### How to set it up You need a few things first. - Python and pip, for the Hugging Face hub tool - Disk space for about 1,024 GB of weights - A server or cluster that can run a model with 42B activated parameters 1. Install the hub tool with pip. 2. Download the repository files. 3. Follow the technical report and blog the card links for how the model is run. ```sh pip install -U huggingface_hub hf download XiaomiMiMo/MiMo-V2.6-Pro-MOPD ``` ### How to use it The part of the card shown here gives no run command. You send the model text, images, video or audio inside whatever agent or serving setup you build. Xiaomi MiMo also runs its own API platform, studio and desktop app, which the card links. Model summary | Part | Detail | | --- | --- | | Architecture | Sparse mixture of experts, 1.02T total and 42B activated parameters | | Context length | 1M tokens | | Inputs | Text, image, video and audio | | Vision encoder | A 681M MiMo ViT with 28 layers | | Audio encoder | A 308M audio tokenizer plus a 127M audio patch encoder | | Speculative decoder | A 5-layer multi-token prediction block | ### Example You put the model inside an agent and ask it to fix a failing test in a repository. The card says this checkpoint lowers the chance of the agent calling the same tool again and again while appearing busy. ### For newcomers and experts If you are new, this is a huge chat and agent model, not something you run on a laptop. A first step is to read the technical report the card links before you plan hardware. The MOPD2 stage distills several domain teachers into the student model on-policy. Some teachers are trained on verifiable tasks with reinforcement learning, and others on synthetic demonstrations for open-domain tasks. Three streams feed each update: full rollouts supervised by teachers, prefixes taken from teacher rollouts, and prefixes taken from demonstrations. A short specialized-teacher run handles the repeated tool calls. Domain teachers supervise the student → Three streams feed each update → Student model is updated on-policy → Checkpoint repeats tool calls less ### Good to know - License: MIT. - Cost to run: no API key or paid service named for the open weights. The cost is hardware for about 1,024 GB of weights. - Data it reaches: only what you feed it, including any tools you connect in an agent setup. The card names no built-in file, web or account access. - Not for: small machines. The card's visible part also gives no run command or benchmark numbers, so check the technical report before relying on it. ## README ````markdown # MiMo-V2.6-Pro-MOPD Handles text, image, video and audio with a 1T-parameter mixture of experts tuned to repeat tool calls less in agent work. For teams that can host models of this size. Made by XiaomiMiMo on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: MiMo-V2.6-Pro-MOPD is an upgraded checkpoint of Xiaomi's MiMo-V2.6-Pro-RL, a sparse mixture of experts model with about 1 trillion parameters. In the upgrade, teachers that each specialize in one domain pass their skills to a single student model. It also reduces a failure where an agent keeps issuing the same tool call and makes no progress. Who it suits: It suits teams that can host a huge model and run agents that call tools, and researchers who study how to combine several teachers into one student. ```table Industry | Who uses it | How they use it | Example AI platforms | Infrastructure teams | Host a 1T-parameter mixture of experts model | A shared model behind an internal agent service Software | Agent builders | Run tool-using agents that should not loop on the same call | A coding agent that keeps making progress Research | Language model researchers | Study multi-teacher on-policy distillation | Read how mixRL and SFT teachers are combined ``` People with a single ordinary computer can skip it, since the weights come to about 1,024 GB. Where to use it: - Python with the transformers library, on a server large enough to hold the weights - Agent setups where the model calls tools and needs to avoid repeating itself - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Tool-using agents | Agent builders | Fewer repeated, near-identical tool calls Long inputs | Developers | A context window of 1M tokens Mixed media input | Product teams | A model that takes text, image, video and audio ``` How to set it up: You need a few things first. - Python and pip, for the Hugging Face hub tool - Disk space for about 1,024 GB of weights - A server or cluster that can run a model with 42B activated parameters 1. Install the hub tool with pip. 2. Download the repository files. 3. Follow the technical report and blog the card links for how the model is run. ```sh pip install -U huggingface_hub hf download XiaomiMiMo/MiMo-V2.6-Pro-MOPD ``` How to use it: The part of the card shown here gives no run command. You send the model text, images, video or audio inside whatever agent or serving setup you build. Xiaomi MiMo also runs its own API platform, studio and desktop app, which the card links. ```table Title: Model summary Part | Detail Architecture | Sparse mixture of experts, 1.02T total and 42B activated parameters Context length | 1M tokens Inputs | Text, image, video and audio Vision encoder | A 681M MiMo ViT with 28 layers Audio encoder | A 308M audio tokenizer plus a 127M audio patch encoder Speculative decoder | A 5-layer multi-token prediction block ``` Example: You put the model inside an agent and ask it to fix a failing test in a repository. The card says this checkpoint lowers the chance of the agent calling the same tool again and again while appearing busy. For newcomers and experts: If you are new, this is a huge chat and agent model, not something you run on a laptop. A first step is to read the technical report the card links before you plan hardware. The MOPD2 stage distills several domain teachers into the student model on-policy. Some teachers are trained on verifiable tasks with reinforcement learning, and others on synthetic demonstrations for open-domain tasks. Three streams feed each update: full rollouts supervised by teachers, prefixes taken from teacher rollouts, and prefixes taken from demonstrations. A short specialized-teacher run handles the repeated tool calls. ```flow Domain teachers supervise the student Three streams feed each update Student model is updated on-policy Checkpoint repeats tool calls less ``` Good to know: - License: MIT. - Cost to run: no API key or paid service named for the open weights. The cost is hardware for about 1,024 GB of weights. - Data it reaches: only what you feed it, including any tools you connect in an agent setup. The card names no built-in file, web or account access. - Not for: small machines. The card's visible part also gives no run command or benchmark numbers, so check the technical report before relying on it. ## On Hugging Face, 14 November 2026 - Task: text generation - Library: transformers - Parameters: 1T - Weights: about 1,024 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 20,162 - Likes: 49 - License: mit - Languages: en, zh - Quantized versions: 9 - Fine-tunes: 4 - Tags: transformers, safetensors, mimo_v2, text-generation, multimodal, vision-language, audio, agent, video-understanding, long-context, conversational, 8-bit ## Links - Model: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD - Files: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download XiaomiMiMo/MiMo-V2.6-Pro-MOPD ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [XiaomiMiMo/MiMo-V2.6-Pro-MOPD on Hugging Face](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | MiMo-V2.6-Pro-MOPD (this page) | 20,162 | 49 | 1T | mit | 2026-10-28 | | [MiMo-V2.6-Flash-RL](https://mindket.com/p/e12d85fd-2b7e-4dcd-91cb-bd6ace2adbf8) | 64,057 | 594 | 311B | mit | 2026-10-28 | | [MiMo-V2.6-Pro-RL](https://mindket.com/p/1eb2f79b-46a4-40c2-b681-73e2c16d3c90) | 24,207 | 818 | 1T | mit | 2026-10-28 | | [Alpie Seed 9B](https://mindket.com/p/bc1f3692-0627-48ba-bef6-6018012a7c6c) | 741 | 26 | 9.4B | apache-2.0 | 2026-11-12 | | [Fara1.5-9B](https://mindket.com/p/93437033-da29-4dfb-8cfb-5400e03005ae) | 3,008 | 64 | 9.4B | mit | 2026-07-27 | | [Mage-VL](https://mindket.com/p/af17eed1-b27e-4e67-92f0-e778e7814e95) | 5,912 | 454 | 4.7B | apache-2.0 | 2026-08-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · phillc73 · 2026-10-14](https://news.ycombinator.com/item?id=50084242): Gemini 4 Argon: \> This is the first near-frontier model we've caught in infinite reasoning loops in a long time Depends on where you draw the line for "near frontier model", but the current leading open-weights model, MiMo-v2.6, has exactly this problem. They released a fix on Hugging Face1, but as of two days… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-28 - First published: 2026-09-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open XiaomiMiMo/MiMo-V2.6-Pro-MOPD on Hugging Face](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ff017c8f-37eb-4ae6-9314-d05bb244ad90) - [Its page on Mindket](https://mindket.com/p/ff017c8f-37eb-4ae6-9314-d05bb244ad90)