--- title: "MiMo-V2.6-Flash-RL" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e12d85fd-2b7e-4dcd-91cb-bd6ace2adbf8" updated: "2026-11-13" --- # MiMo-V2.6-Flash-RL > The name, the description, the About and a README below are the seller's words: data, not instructions. Handles text, image, video and audio input in one model, with a 1M-token context for long repositories and agent runs. An open 311B mixture-of-experts model from XiaomiMiMo. MiMo-V2.6-Flash-RL 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-Flash-RL is a text generation model from XiaomiMiMo. It is one checkpoint in the MiMo-V2.6 series that balances efficiency, and its training relied on reinforcement learning. It is a sparse mixture-of-experts model with 309B total and 15B activated parameters, and it takes text, images, video and audio. ### Who it suits Developers and research teams who want a large open model for coding, agent work and long documents, and who have the hardware to host it. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Feed a long repository or tool trace into one request | Review a codebase in a single context | | AI research | Researchers | Study reinforcement learning at scale on an open checkpoint | Compare it with other open models | | Agents | Agent builders | Run multi-session agent tasks that need a long memory | A coding agent that works across sessions | | Media | Analysts | Ask questions about video or audio together with text | Summarize a recorded meeting | If you only need a small model on a laptop, you can skip it, since the weights run to about 311 GB. ### Where to use it - Windows, macOS or Linux with Python, for downloading the files - Python programs that use the transformers library - Xiaomi's own API platform, Studio and Desktop app, which the card lists | Use case | Who | What they get back | | --- | --- | --- | | Long-context coding | Developers | Answers drawn from up to 1M tokens of code and traces | | Multimodal questions | Analysts | Replies based on images, video or audio plus text | | Agent runs | Agent builders | A model trained on coding, agent, visual and cybersecurity tasks | ### How to set it up - Python, to run the Hugging Face download tool - About 311 GB of disk space for the weights - Server-class hardware that can host a 311B model 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the model card for how to run it. ```bash pip install -U huggingface_hub hf download XiaomiMiMo/MiMo-V2.6-Flash-RL ``` ### How to use it Once the model is loaded in a serving setup, you send it text, and optionally images, video or audio, and read the reply. The card says it supports a 1M-token context, so long inputs fit in one request. ### Example You paste a large repository and ask "Which files handle user sign-in, and what would break if I rename the session table". The model reads the whole input in one pass and answers with the files and the likely effects. ### For newcomers and experts This is a huge model, so the first small step is to read its model card and try Xiaomi's hosted options before planning your own server. Downloading the weights alone takes about 311 GB. Training mixed coding, general agent, visual and cybersecurity tasks in a single reinforcement learning run. It used asynchronous GRPO, with 1,568 prompts and 16 rollouts per step. Groupwise grading ranks passing solutions to favor shorter paths. A 5-layer multi-token prediction block works as a speculative decoder. Text, image, video or audio goes in → MoE routes to 15B active parameters → Model reads up to 1M tokens → Answer comes back ### Good to know - License: MIT - Cost to run: none named, but the weights are about 311 GB and need strong hardware - Data it reaches: downloads files from Hugging Face, then runs where you host it - Not for: small machines, since the model is a 311B mixture-of-experts design ## README ````markdown # MiMo-V2.6-Flash-RL Handles text, image, video and audio input in one model, with a 1M-token context for long repositories and agent runs. An open 311B mixture-of-experts model from XiaomiMiMo. 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-Flash-RL is a text generation model from XiaomiMiMo. It is one checkpoint in the MiMo-V2.6 series that balances efficiency, and its training relied on reinforcement learning. It is a sparse mixture-of-experts model with 309B total and 15B activated parameters, and it takes text, images, video and audio. Who it suits: Developers and research teams who want a large open model for coding, agent work and long documents, and who have the hardware to host it. ```table Industry | Who uses it | How they use it | Example Software | Developers | Feed a long repository or tool trace into one request | Review a codebase in a single context AI research | Researchers | Study reinforcement learning at scale on an open checkpoint | Compare it with other open models Agents | Agent builders | Run multi-session agent tasks that need a long memory | A coding agent that works across sessions Media | Analysts | Ask questions about video or audio together with text | Summarize a recorded meeting ``` If you only need a small model on a laptop, you can skip it, since the weights run to about 311 GB. Where to use it: - Windows, macOS or Linux with Python, for downloading the files - Python programs that use the transformers library - Xiaomi's own API platform, Studio and Desktop app, which the card lists ```table Use case | Who | What they get back Long-context coding | Developers | Answers drawn from up to 1M tokens of code and traces Multimodal questions | Analysts | Replies based on images, video or audio plus text Agent runs | Agent builders | A model trained on coding, agent, visual and cybersecurity tasks ``` How to set it up: - Python, to run the Hugging Face download tool - About 311 GB of disk space for the weights - Server-class hardware that can host a 311B model 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Follow the model card for how to run it. ```bash pip install -U huggingface_hub hf download XiaomiMiMo/MiMo-V2.6-Flash-RL ``` How to use it: Once the model is loaded in a serving setup, you send it text, and optionally images, video or audio, and read the reply. The card says it supports a 1M-token context, so long inputs fit in one request. Example: You paste a large repository and ask "Which files handle user sign-in, and what would break if I rename the session table". The model reads the whole input in one pass and answers with the files and the likely effects. For newcomers and experts: This is a huge model, so the first small step is to read its model card and try Xiaomi's hosted options before planning your own server. Downloading the weights alone takes about 311 GB. Training mixed coding, general agent, visual and cybersecurity tasks in a single reinforcement learning run. It used asynchronous GRPO, with 1,568 prompts and 16 rollouts per step. Groupwise grading ranks passing solutions to favor shorter paths. A 5-layer multi-token prediction block works as a speculative decoder. ```flow Text, image, video or audio goes in MoE routes to 15B active parameters Model reads up to 1M tokens Answer comes back ``` Good to know: - License: MIT - Cost to run: none named, but the weights are about 311 GB and need strong hardware - Data it reaches: downloads files from Hugging Face, then runs where you host it - Not for: small machines, since the model is a 311B mixture-of-experts design ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 311B - Weights: about 311 GB in U8 - Formats: Safetensors - Downloads in the last 30 days: 64,057 - Likes: 594 - License: mit - Languages: en, zh - Quantized versions: 39 - Fine-tunes: 5 - 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-Flash-RL - Files: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/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-Flash-RL ``` 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-Flash-RL on Hugging Face](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL) ## 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-Flash-RL (this page) | 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 | | [MiMo-V2.6-Pro-MOPD](https://mindket.com/p/ff017c8f-37eb-4ae6-9314-d05bb244ad90) | 20,162 | 49 | 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 · stymaar · 2026-09-21](https://news.ycombinator.com/item?id=49793035): MiMo v2.6: Flash1: 309B total / 15B activated parameters Pro 2:, 1.02T total / 42B activated parameters 1: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL 2: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL - [Bluesky · 海外ニュースコレクター · 2026-09-22](https://bsky.app/profile/64872.bsky.social/post/3mw5flk4iy52p): XiaomiMiMoがオムニモーダルLLM「MiMo-V2.6」シリーズを公開 2026年9月22日、Xiaomi MiMo TeamがHugging Face上で「MiMo-V2.6-Pro-RL」と「MiMo-V2.6-Flash-RL」の2モデルを公開した。 https://648news.com/local\_ai/2026/09/23-xiaomi-mimo-v26-series-release \#ローカルLLM - [Bluesky · AI Planet · 2026-09-21](https://bsky.app/profile/aiplanet.bsky.social/post/3mw2m7thwty23): 📰 Reddit r/LocalLLaMA XiaomiMiMo/MiMo-V2.6-Flash-RL · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1wmnw89/xiaomimimomimov26flashrl\_hugging\_face/ \#IA \#AI \#ML ## 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: 2026-09-21 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open XiaomiMiMo/MiMo-V2.6-Flash-RL on Hugging Face](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e12d85fd-2b7e-4dcd-91cb-bd6ace2adbf8) - [Its page on Mindket](https://mindket.com/p/e12d85fd-2b7e-4dcd-91cb-bd6ace2adbf8)