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Xiaomi MiMo Reasoning Model
- keepamovin 1y ago[flagged]
- cruzcampo 1y agoDoes Xiaomi literally mean Little Rice? That's what my very limited mandarin would suggest
- rs186 1y agohttps://en.wikipedia.org/wiki/Foxtail_millet https://en.wikipedia.org/wiki/Foxtail_millet
- iszomer 1y agoYes.
- keepamovin 1y agoThat is what my literally also rather limited Chinese would suggest. haha But with many single characters in Chinese, a Chinese person will tell you, if you ask for what a single character means, something like, "Well it's not so easy to pin down the meaning of that one. Sometimes we use it like this, and sometimes like that." Sure, some characters have an easy meaning (for me, I think the rice in Mi is one of them!) but there's plenty where you cannot get a Chinese person to easily tell you what a single character means. I guess it's a little like, but not the same as, asking an English person to tell you, what any given "morpheme" (word part, like fac-) means. Hahaha. Not a perfect analogy tho! :) Here's this list of morphemes I found just now thinking about this: https://www.fldoe.org/core/fileparse.php/16294/urlt/morphemeML.pdf https://www.fldoe.org/core/fileparse.php/16294/urlt/morpheme... Seems incomplete list when you consider etymology of English words are often composed of parts from ages past! :)
- deleted 1y ago[deleted]
- os2warpman 1y ago小米 little rice Yes. But it's more complicated than that.
- kzz102 1y agoXiaomi can also mean millet. I think it's a reference to this Mao quote: https://en.wikipedia.org/wiki/Millet_plus_rifles?wprov=sfla1 https://en.wikipedia.org/wiki/Millet_plus_rifles?wprov=sfla1
- keepamovin 1y agoWow, that's interesting. I guess that's like a US company being called "MRE". We would view that like a veteran's owned and operated company. Interesting. And all the products would be "MRE-Phone", "MRE-Pod", hehehe :)
- est 1y agoThat's just a fun coicidence but in reality LeiJun and 12 others from Kingsoft Corp founded Xiaomi after they had a bowl of millet gruel. https://www.scmp.com/abacus/tech/article/3028654/documentary-provides-rare-glimpse-xiaomis-early-days https://www.scmp.com/abacus/tech/article/3028654/documentary...
- kzz102 1y agoThis is one of the things that everyone gets the reference, but it won't be good to admit it publicly. This quote is known to almost everyone born in that area, and it's the first thing that come to mind when you hear the name.
- gandalfgreybeer 1y agoEtymology of the brand name here: https://en.wikipedia.org/wiki/Xiaomi#Name_etymology https://en.wikipedia.org/wiki/Xiaomi#Name_etymology
- fenprace 1y agoMandarin speaker here. Literally, Yes. Xiaomi means 'little rice'. But in reality when people say xiaomi, they always refer to another kind of crop, foxtail millet (https://en.wikipedia.org/wiki/Foxtail_millet https://en.wikipedia.org/wiki/Foxtail_millet). It is a traditional food and still very common in China and other place in Asia.
- cynicalsecurity 1y agoMi has probably a bunch of meanings depending on tone?
- fredwu 1y agoNot sure why it would be "funny" as this is literally why they named the company Xiaomi. Source (Chinese): https://finance.sina.cn/tech/2020-11-26/detail-iiznctke3397952.d.html https://finance.sina.cn/tech/2020-11-26/detail-iiznctke33979...
- keepamovin 1y agoFor me it's funny that all the products are called "Rice-something" that's funny, hahaha! :)
- bojan 1y agoNot that different from "apple" something.
- ReptileMan 1y agoNot quite. Rice-something has been used for goods coming from East Asia - depending on the quality of the goods in both derogatory and non derogatory manner. Like rice rockets - the japanese ultra high performance sport bikes for example
- thijson 1y agoI was reading the Steve Jobs biography and thought it was interesting that the choice in the name "apple" came from him wanting something that came before Atari in the yellow pages, and also that he had spent time at a Hippie apple orchard in Oregon. I was reading a Jack Tramiel biography recently, and read that early on the two Steve's sought to sell Apple to Commodore for under a million dollars.
- est 1y agoNah, Xiaomi literally means Millet, which also prefixes Mi.
- amazingamazing 1y agojust as funny as an Apple, for sure.
- keepamovin 1y agoThat's a good point hahahaha! :)
- w4yai 1y agoAnyone tried it ?
- Alifatisk 1y agoNo, where can I try it? I saw a huggingface link but I wonder if they host it themselves somewhere to like how Alibaba does with Qwen chat.
- yorwba 1y agoThere is a HuggingFace space (probably not official) at: https://huggingface.co/spaces/orangewong/xiaomi-mimo-7b-rl https://huggingface.co/spaces/orangewong/xiaomi-mimo-7b-rl You might have to wait a minute to get a response. Also, the space doesn't seem to have turn-taking implemented, so after giving the Assistant's response, it kept on generating the Human's next message and so on and so forth.
- benterix 1y agoYes, not great, not terrible. I gave it my personal test (a coding task), it produced semi-decent quality code that produced a minor error, after pasting the error it failed to solve it during multiple rounds. I believe another 2-3 years and we'll have quite usable small models.
- ramesh31 1y agoThese benchmark numbers cannot be real for a 7b model
- andrepd 1y agoEvery LLM is basically being trained on benchmarks so "benchmark" as applied to LLMs is a pretty meaningless term.
- GaggiX 1y agohttps://qwenlm.github.io/blog/qwen3/ https://qwenlm.github.io/blog/qwen3/ Go look at the benchmark numbers of qwen3-4B if you think these are unrealistic.
- energy123 1y agoAlso not "real" in the sense that the model developers most likely put the benchmarks into the training data.
- strangescript 1y agoThe smaller models have been creeping upward. They don't make headlines because they aren't leapfrogging the mainline models from the big companies, but they are all very capable. I loaded up a random 12B model on ollama the other day and couldn't believe how good it competent it seemed and how fast it was given the machine I was on. A year or so ago, that would have not been the case.
- apples_oranges 1y agoexactly, it seems to validate my assumption from some time ago, that we will mostly use local models for everyday tasks.
- pzo 1y agoyeah especially that this simplifies e.g. doing mobile app for 3rd party developers - not extra cost, no need to setup proxy server, monitoring usage to detect abuse, don't need to make complicated subscription plan per usage. We just need Google or Apple to provide their own equivalent of both: Ollama and OpenRouter so user either use inference for free with local models or BringYourOwnKey and pay themself for tokens/electricity bill. We then just charge smaller fee for renting or buying our cars.
- mobilio 1y agoWaiting for GGUF or MLX models. Probably within few hours will be released.
- CodeCompost 1y agoOpen Source or Open Weights?
- NitpickLawyer 1y agoMIT - so open source
- Davidzheng 1y agoWeights
- deleted 1y ago[deleted]
- ilrwbwrkhv 1y agoAnd this point everybody will open source their models or weights. The only one which will not is open AI.
- rvz 1y ago> The only one which will not is open AI. I think you meant Anthropic. OpenAI is "planning" to release an open weight model this year likely competing against the Llama models. [0] I have not seen an open weight AI model ever being released by Anthropic at all. [0] https://openai.com/open-model-feedback/ https://openai.com/open-model-feedback/
- userbinator 1y ago...and searching for things related to multiple antennae just got harder. They could've called it Xiaomimo.
- arghwhat 1y agomultiple-input, multiple-output was horribly generic to begin with. Terms like multipath propagation and spatial multiplexing will do just fine.
- Jotalea 1y agoI wonder if they will use this model for their AI assistant on their Xiaomi 15 series phones. They most likely will. I'm not really sure what to expect from it.
- shihabkhanbd 1y ago[flagged]
- levzzz 1y ago[dead]
- jedisct1 1y agoGGUF version (for LM Studio, Ollama, etc): https://huggingface.co/jedisct1/MiMo-7B-RL-GGUF https://huggingface.co/jedisct1/MiMo-7B-RL-GGUF
- m4r1k 1y agoMy Chinese friend told me MiMo doesn’t have a meaning in Chinese (of course Mi 米 = rice). Anybody have a clue for what it stands for?
- column 1y ago(Xiao)mi mo(del) ?
- johanyc 1y agoYeah i think so 小(xiao)_米(mi)模(mo)_型(xing)
- echelon_musk 1y agoRice Model?
- gandalfgreybeer 1y agoA lot of Xiaomi products have the prefix Mi. My initial guess is Mo is for model. Also related reference https://en.wikipedia.org/wiki/Xiaomi#Name_etymology https://en.wikipedia.org/wiki/Xiaomi#Name_etymology
- nicman23 1y agoprobably μίμος (mime)
- SchemaLoad 1y agoIsn't that basically standard for names? What does Google mean? Even when the words in a name do have some meaning, they often don't apply. The meaning of the word brother doesn't really mean anything in the company Brother.
- xmorse 1y agoXiaomi is an amazing company
- lvl155 1y agoWhy are there so many English-first AI models from China? Are they not interested in serving their own population? Or is it that if they publish Chinese-first models it won't get publicity in the West?
- whynotmaybe 1y agoHaven't we reached a situation where English is the de facto language of scientific research, especially AI benchmarks ? It's clearly impossible for me to try anything in Chinese, I'd need a translation.
- xmichael909 1y agoCorrect. Lingua franca for at least the last 75 years, if not longer.
- numpad0 1y agoFor publishing results, yes, but not necessarily for the generation part of it.
- unsupp0rted 1y agoLess and less, it feels like, every year. I wonder if anybody has hard numbers on that.
- enlyth 1y agoI assume a large portion of high quality training material is in English
- sigmoid10 1y agoYou'd be correct. The largest portion of all languages in Common Crawl (aka the "whole open internet" training corpus) is English with 43%. No other language even reaches double digit percentages. The next biggest one is Russian at 6%, followed by German at 5%.
- sida 1y agoXiaomi in Chinese translates to "Little Rice" Here is the meaning of the name Described here: https://finance.sina.cn/tech/2020-11-26/detail-iiznctke3397952.d.html https://finance.sina.cn/tech/2020-11-26/detail-iiznctke33979... 在后来的讨论中，我突然想到了我最喜欢的一句话——“佛观一粒米，大如须弥山”。 Translated into English, it means: “In the later discussions, I suddenly thought of one of my favorite sayings — ‘A Buddha sees a single grain of rice as vast as Mount Sumeru.’” This expression emphasizes the idea that even something seemingly small (like a grain of rice) can hold immense significance or value when viewed from a different perspective. Thanks to chatgpt for translating this
- onefeduk21 1y ago[dead]
- rahimnathwani 1y agoWhen you guys use gguf files in ollama, do you normally create a modelfile to go with it, or just hope that whatever default ollama has work with the new model? https://github.com/ollama/ollama/blob/main/docs%2Fmodelfile.md https://github.com/ollama/ollama/blob/main/docs%2Fmodelfile....
- monkmartinez 1y agoIf you ollama pull <model> the modelfile will be downloaded along with the blob. To modify the model permanently, you can copypasta the modelfile into a text editor and then create a new model from the old modelfile with the changes you require/made. Here is my workflow when using Open WebUI: 1. ollama show qwen3:30b-a3b-q8_0 --modelfile 2. Paste the contents of the modelfile into -> admin -> models -> OpenwebUI and rename qwen3:30b-a3b-q8_0-monkversion-1 3. Change parameters like num_gpu 90 to change layers... etc. 4. Keep | Delete old file Pay attention to the modelfile, it will show you something like this: # To build a new Modelfile based on this, replace FROM with: # FROM qwen3:30b-a3b-q8_0 and you need to make sure the paths are correct. I store my models on a large nvme drive that isn't default ollama as an example of why that matters. EDIT TO ADD: The 'modelfile' workflow is a pain in the booty. It's a dogwater pattern and I hate it. Some of these models are 30 to 60GB and copying the entire thing to change one parameter is just dumb. However, ollama does a lot of things right and it makes it easy to get up and running. VLLM, SGLang, Mistral.rs and even llama.cpp require a lot more work to setup.
- rahimnathwani 1y agoSorry, I should have been clearer. I meant when you download a gguf file from huggingface, instead of using a model from ollama's library.
- monkmartinez 1y agoollama pull hf.co/unsloth/Qwen3-30B-A3B-GGUF:Q4_K_M and the modelfile comes with it. It may have errors in the template or parameters this way. It has to be converted to GGUF/GGML prior to using it this way. You can, of course, convert and create the specific ollama model from bf16 safetensors as well.
- gizmodo59 1y agoIts funny to see benchmarks where they omit the top performing models like O3 (Which is the best model in many benchmarks currently) and Gemini Pro/Claude 3.7.
- daveguy 1y agoThose are much much larger models, and they are proprietary. Those model providers just don't have the distilled versions identified and available. Notice most of the models they are comparing with are 7B models. The exception is also an open weights model (Qwen-2.5-32B-RL-Zero). Even with 32B parameters the MiMo-7B outperforms it.
- erikig 1y agoI believe the goal here is to compare them against similar models that are being optimized to run offline or on mobile hardware.
- vessenes 1y agoUmm wow. Great benchmarks. I’m looking forward to chatting with this one. A couple things stand out to me — first is that the 7B model is trained on 25T tokens(!). This is Meta-scale training; Llama 4 Maverick was trained on 22T or so. (Scout, the smaller model: 40T). Second, this is an interesting path to take - not a distilled model or an RL layer to get reasoning out of another model, but a from-scratch RL model with reasoning baked in; the claims seem to indicate you get a lot of extra efficiency per-parameter doing this. I don’t have experience with Xiaomi models, so I’m cautious about this one until I play with it, but it looks like a super viable local reasoning model from the stats.
- siliconc0w 1y agoThis is incredibly strong coding performance for a 7b. I use Gemini Pro 2.5 which got 67.8 and this got 57.8, very close to Gemini 2.5 Flash which got 60.6. I've become pretty skeptical about eval results given what we've heard about llama4 so we'll see where this lands on the closed evals but very impressive to see.
- Arcuru 1y agoFrom the paper, I was intrigued by how they handled their RL step for Code Data. They trained against hard but solvable code generation tasks by running unit testing. Is that training step done by the other models? > Code Data For coding problems, we curate a high-quality training set comprising open-source datasets and our newly collected problem set. We remove problems without test cases. For problems with golden solutions, we exclude those where the golden solution failed to pass all test cases. For problems without golden solution, we discard problems where no test case can be solved in 16 rollouts of advanced reasoning models. Similar to math data, we utilize an SFT version of MiMo-7B to filter out easy problems that are perfectly solved in all 16 rollouts. This rigorous cleaning process yields 30K code problems. > During each RL iteration, we evaluate thousands of problems to compute the rewards, with each problem potentially containing hundreds of test cases. To improve reward computing efficiency and eliminate GPU idle time, we developed an online judge environment that enables parallel execution of extremely high-volume unit tests.
- loufe 1y agoIs any RL done without unit testing? I would be surprised to hear that that wasn't the case, as it would imply a disregard for accuracy for other model makers, which would be surprising. Perhaps you can do this for small modular problems but not for a problem with a 200k token input?
- Havoc 1y agoBeen testing it a bit and overall pretty solid. The lengthy think times means one waits quite a while though. Longer than much larger models like say the recent qwen moe That moe strikes me as the better overall tradeoff
- xpe 1y agoThe README says "RL" without specifying what kind of RL is used. Researchers: I know you are busy, and I know good writing takes time, but please don't skip this kind of detail.
- paulluuk 1y agoI assume they mean "Reinforcement Learning", and it's been a decade since I studied AI in university, but isn't it perfectly valid to just say "RL"? What kind of specificity are you looking for, whether they used Q-Learning or some other algorithm?
- xpe 1y agoI wouldn’t phrase it as a matter of “validity”. I would phrase it as a question of transparency. Putting a model out in public without clearly explaining how it works doesn’t meet my bar for a proper scientific exchange of knowledge. Perhaps they are being intentionally vague for competitive reasons. RL is a generic term that can be mixed and matched with various other methods. In the context of LLMs, often some variation of RLHF is used. But the authors don’t even say “RLHF”, much less explain their methodology. Understanding this isn’t just a matter of academic interest; it has implications for understanding and using this work. I’m often concerned by the writing quality of ML/AI papers but this strikes me as particularly disappointing. It is increasingly important to have confidence that the creators of AI systems are thoughtful and thorough. I want to see their reasoning. I want to understand the trade-offs they make and why.
- paulluuk 1y agoIf you put it like that, I absolutely agree with you, except that I suppose I don't really consider this an exchange of knowledge but more like the release of an open-source project: the only thing they need to publish are instructions on how to use it. I don't think they’re really interested in anyone improving their model by themselves or reproducing the work. It would be amazing if they did, though!
- ainch 1y ago
- badmonster 1y agoMiMo-7B claims to outperform larger models like Qwen-32B and match OpenAI o1-mini on math/code benchmarks — all with a 7B model trained from scratch. Is this a sign that pretraining + RLHF optimization is finally outpacing scale? Or are we just getting better at benchmarking narrow capabilities?
- loufe 1y agoQwen 3 or 2.5?
- 8ibzjj 1y ago[flagged]
- good-luck86523 1y ago[flagged]