This 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.
lvl155 · 4h ago
Why 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?
throwup238 · 57m ago
CommonCrawl [1] is the biggest and most easily accessible legally acquired crawling dataset around, collecting data since 2008. Pretty much everyone uses this as their base dataset for training foundation LLMs and since it's mostly English, all models perform well in English.
Haven'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.
enlyth · 4h ago
I assume a large portion of high quality training material is in English
sigmoid10 · 4h ago
You'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%.
Only place where russian is in top 5 is in Wikipedia views. Russian part of internet steadily goes down, as russian imperialism crumbles.
yyhhsj0521 · 2h ago
Chinese internet mostly consists of a few closed gardens tightly controlled by big corps. Crawlers simply don't work when each company employs an army of engineers to guard their data. Many of the most popular websites are also app only. It's impossible to get the corpus necessary to train a good LLM.
bredren · 1h ago
Do we have estimates on the corpus that is available? This model's repo describes "multiple strategies to generate massive diverse synthetic reasoning data." FWIW, AI 2027 forecasts heavy emphasis on synthetic data creation.
Is the lack of existing corpus just an extra hurdle for Hanzi-first models that are also leading the pack in benchmarks?
chvid · 4h ago
All LLMs are trained on the same basic blob of data - mostly in English, mostly pirated books and stuff.
Leary · 1h ago
They are not "English-first". Deepseek-R1, for example, reasons in Chinese when you ask it a question in Chinese.
lwansbrough · 31m ago
I’m going to go with: to ensure it is not disadvantaged in benchmarks
choutianxius · 3h ago
One reason is that there is no "good" search engine in China. The most popular one, Baidu, is like garbage compared to Google search. The most useful training data in Chinese would likely be from the social media and video sharing platforms, which I guess is much more difficult to crawl and clean up.
thoroughburro · 1h ago
A few thousand years of literature ain’t nothing…
kccqzy · 47m ago
Peanuts compared to the discourse available on the internet.
The literature that survived thousands of years are cream of the crop; you won't find lots of random unimportant dialog between people thousands of years ago, but you find that on Reddit.
fwipsy · 48m ago
Given premodern population sizes and literacy rates, historical texts probably don't exist in anything like the quantity that internet posts do. Even if they did, the information may not be relevant to the modern world.
Havoc · 1h ago
I was under the impression that we just see the English stuff given that we're using English news channels.
overfeed · 1h ago
Why are so many American models multi-lingual, supporting hundreds of languages not commonly spoken in the United States?
Could it be that being multilingual results in a larger pool of human knowledge on the technical side compared to training on just a single language or 2. And on the business side, supporting more languages results in a larger TAM (total addressable market). Using english-language dataset for training LLMs is the default, not the other way like you insinuate.
achierius · 1h ago
That's clearly a different question. It'd be possible for these models to be Mandarin-first while still supporting other languages, like American models are English-first while doing the same, but that's not what's happening.
overfeed · 1h ago
> That's clearly a different question. It'd be possible for these models to be Mandarin-first while still supporting other languages
What would a hypothetical "Mandarin-first" model look like to you?
I challenge the notion that the current models are "English-first" - that is an unsubstantiated opinion not supported by fact. I bet, dollars to donuts, these models are SoTA in Mandarin as well. When framed that way, asking "Why are they marketed as English-speaking models outside of China" or "Why are they really good at English" are simply not interesting questions - they have obvious answers.
paulsutter · 2h ago
I don’t see any indication that it’s English-first?
spacebanana7 · 2h ago
I wonder whether English text having fewer characters provides an advantage somehow.
jmole · 2h ago
not really, since tokenization combines multiple characters
bilbo0s · 4h ago
The mandarin language models obviously exist, but what would you do with them if they provided access to them? And what knowledge would be in them? What is the body of knowledge encoded in Mandarin? What does that look like?
Sad reality is that not many outside of China have the facility with Mandarin to use those models. Even non-native Mandarin speakers who claim to be "fluent", are often messing up intended meaning in text. Or making literal translations that wind up making no sense.
Inside of China, llm use will be Mandarin based. Outside, it seems to me English is the natural choice.
Irony of Irony, probably the best way for a non Mandarin speaking layman to test a Mandarin based model would be to use another LLM to translate prompts to Mandarin.
It's a sad future we're looking at.
Or a brilliant one.
Time will tell.
johnla · 4h ago
For it to be brilliant, AI needs to be a benevolent tool all the time. It would take just a few malignant actors to turn our world upside. I suspect it'll follow the same Internet and social media path. Great at first, grow markets, bring us together and then take a turn.
horacemorace · 2h ago
You’re right of course. That’s why these open source / weight releases are so critically important.
mensetmanusman · 4h ago
English won. The Chinese youth struggle to write their own calligraphy characters they can read now. Typing favors English.
rahimnathwani · 3h ago
It's easy and fast to type Chinese sentences using a keyboard.
-__---____-ZXyw · 49m ago
Source?
This smacks of "I saw a headline once"-itis. Especially the fact that you refer to the Chinese characters as "calligraphy characters", as if that were the general term or something.
throwaway519 · 3h ago
The pendulum already turned back. The current generation under 20 grew up with touchscreens. That obseletes input with pinyin; many don't care if the device has no keyboard.
thenthenthen · 2h ago
Input is so interesting in China, basically a sorta t9 but just single letters and picking the right characters, with common/frequently used characters first, using pinyin. For example to say “ How are you?” You just type “nhm” (Ni Hao Ma) and 你好吗 shows up as suggestion/autofill. You can make surprisingly long sentences using this method.
olalonde · 2h ago
> That obseletes input with pinyin
Uh? Pinyin input is by far the most popular input technique in China. I rarely see anyone using handwriting input.
That being said, it has nothing to do with English winning. It's just a Chinese input technique that uses the latin alphabet. English fluency in China is not very common, especially spoken English.
pertymcpert · 29m ago
What? Only people I've seen use the writing input mode was old people.
34679 · 4h ago
Nearly everyone in the urban areas of China spoke some English when I visited way back in 1995. It's a bilingual society.
crazygringo · 3h ago
This is not true. I was in Beijing around then and never met a single person who spoke English if they hadn't learned it for professional reasons (they worked in tourism, international business, etc.).
It could not have been further from a bilingual society.
rahimnathwani · 3h ago
I lived in Beijing and Shanghai for 9 years (2010-2019) and this is NOT my impression at all.
gcy · 1h ago
I suppose you probably were visiting some university districts/CBDs where people likely to have received higher education. Elsewhere, aside from basic "hello"/"how are you", locals in general are not able to communicate in English.
gizmodo59 · 3h ago
Its 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 · 3h ago
Those 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.
rahimnathwani · 3h ago
When 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?
One of the core design goals Georgi Gerganov had with GGUF was to not need other files. It's literally bullet point #1 in the specs
>Single-file deployment
>Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
If you only ever have one set of configuration parameters per model (same temp, top_p, system prompt...), then I guess you can put them in a gguf file (as the format is extensible).
But what if you want two different sets? You still need to keep them somewhere. That could be a shell script for llama.cpp, or a ModelFile for ollama.
(Assuming you don't want to create a new (massive) gguf file for each permutation of parameters.)
monkmartinez · 3h ago
If 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.
o11c · 2h ago
Pretty sure the whole reason Ollama uses raw hashes everywhere is to avoid copying the whole NN gigabytes every time.
monkmartinez · 2h ago
Maybe I am doing something wrong! When I change parameters on the modelfile, the whole thing is copied. You can't just edit the file as far as I know, you have to create another 38GB monster to change num_ctx to a reasonable number.
o11c · 2h ago
The parameters (prompt, etc.) should be set only in the new modelfile (passed to `ollama create`), using a FROM referencing the previous ollama model. Parameters in a Modelfile override the hard-coded parameters from the GGUF itself (which are sometimes buggy); in fact from elsewhere in the thread it sounds like Mimo is missing proper stop tokens, or maybe templates in general; I'm not an expert).
This will show a separate entry in `ollama list` but only copy the Modelfile not the GGUF.
Alternatively, if you use the API, you can override parameters "temporarily". Some UIs let you do this easily, at least for common parameters.
rahimnathwani · 2h ago
Sorry, 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 · 2h ago
ollama 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.
rahimnathwani · 53m ago
Yeah when I do this, the modelfile has only FROM and TEMPLATE. No PARAMETERs:
ollama pull hf.co/jedisct1/MiMo-7B-RL-GGUF:Q4_K_M
ollama show --modelfile hf.co/jedisct1/MiMo-7B-RL-GGUF:Q4_K_M
memhole · 3h ago
I’ll typically use the defaults initially and then use a Modelfile if it’s something I plan on using. I think you can dump the modelfile ollama uses to have a template to work with.
vessenes · 2h ago
Umm 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.
I 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.
m4r1k · 5h ago
My 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?
gandalfgreybeer · 5h ago
A lot of Xiaomi products have the prefix Mi. My initial guess is Mo is for model.
These benchmark numbers cannot be real for a 7b model
strangescript · 6h ago
The 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.
djmips · 3m ago
Which model?
apples_oranges · 6h ago
exactly, it seems to validate my assumption from some time ago, that we will mostly use local models for everyday tasks.
pzo · 6h ago
yeah 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.
mring33621 · 3h ago
strong agree
my employer talks about spending 10s of millions on AI
but, even at this early stage, my experiments indicate that the smaller, locally-run models are just fine for a lot of tech and business tasks
this approach has definite privacy advantages and likely has cost advantages, vs pay-per-use LLM over API.
jillesvangurp · 6h ago
Including figuring out which more expensive models to use when needed instead of doing that by default. Early LLMs were not great at reasoning and not great at using tools. And also not great at reproducing knowledge. Small models are too small to reliably reproduce knowledge but when trained properly they are decent enough for simple reasoning tasks. Like deciding whether to use a smarter/slower/more expensive model.
wg0 · 6h ago
But who will keep them updated and what incentive they would have? That's I can't imagine. Bit vague.
ebiester · 4h ago
Eventually? Microsoft and Copilot, and Apple and Siri - even if they have to outsource their model making. It will be a challenge to desktop Linux.
WorldPeas · 2h ago
I figure this will take the same shape as package distribution. If you have ever used a linux distribution you’ll always see a couple .edu domains serving you packages. Big tech might be able to have specialized models, but following the linux paradigm, it will likely have more cutting edge but temperamental models from university research
cruzcampo · 6h ago
Who keeps open source projects maintained and what incentive do they have?
jsheard · 6h ago
Most open source projects don't need the kinds of resources that ML development does. Access to huge GPU clusters is the obvious one, but it's easy to forget that the big players are also using huge amounts of soulcrushing human labor for data acquisition, cleaning, labeling and fine tuning, and begrudgingly paying for data they can't scrape. People coding in their free time won't get very far without that supporting infrastructure.
I think ML is more akin to open source hardware, in the sense that even when there are people with the relevent skills willing to donate their time for free, the cost of actually realizing their ideas is still so high that it's rarely feasible to keep up with commercial projects.
cruzcampo · 6h ago
That's a fair point. I think GPU clusters are the big one, the rest sounds like a good fit for volunteer work.
wg0 · 2h ago
Or sharing GPU compute. Crowd sourcing.
cruzcampo · 1h ago
Ooooh I can see a Seti@Home setup working
jsheard · 1h ago
Easier said than done, training is usually done on "big iron" GPUs which are a cut above any hardware that consumers have lying around, and the clusters run on multi-hundred-gigabit networks. Even if you scaled it down to run on gaming cards, and gathered enough volunteers, the low bandwidth and high latency of the internet would still be a problem.
simiones · 5h ago
For the bigger open source projects, companies who use that code for making money. Such as Microsoft and Google and IBM (and many others) supporting Linux because they use it extensively. The same answer may end up applying to these models though - if they really become something that gets integrated into products and internal workflows, there will be a market for companies to collaborate on maintaining a good implementation rather than competing needlessly.
nickip · 6h ago
What model? I have been using api's mostly since ollama was too slow for me.
patates · 5h ago
I really like Gemma 3. Some quantized version of the 27B will be good enough for a lot of things. You can also take some abliterated version[0] with zero (like zero zero) guardrails and make it write you a very interesting crime story without having to deal with the infamous "sorry but I'm a friendly and safe model and cannot do that and also think about the children" response.
Qwen3 and some of the smaller gemma's are pretty good and fast. I have a gist with my benchmark #'s here on my m4 pro max (with a whole ton of ram, but most small models will fit on a well spec'ed dev mac.)
Last time I did that I was also impressed, for a start.
Problem was that of a top ten book recommendations only the first 3 existed and the rest was a casually blended hallucination delivered in perfect English without skipping a beat.
"You like magic? Try reading the Harlew Porthouse series by JRR Marrow, following the orphan magicians adventures in Hogwesteros"
And the further towards the context limit it goes the deeper this descent into creative derivative madness it goes.
It's entertaining but limited in usefulness.
omnimus · 6h ago
LLMs are not search engines…
achierius · 1h ago
Many tasks that one might want to give a model end up implicitly including search as a subtask. For example, "plan me a trip to Santiago" obviously requires the model to understand details about the real city of Santiago. Less obviously, "write me a Python script to do ..." requires they understand APIs, libraries, etc., the same things you might ask a search engine to pull up. The tasks which do not require a coherent + mostly-correct exterior-world-model are relatively few -- text processing (e.g. "proofread this") is a big one; calculation tasks fit, but LLMs are also bad at those.
Philpax · 5h ago
An interesting development to look forward to will be hooking them up to search engines. The proprietary models already do this, and the open equivalents are not far behind; the recent Qwen models are not as great at knowledge, but are some of the best at agentic functionality. Exciting times ahead!
hedgehog · 2h ago
If you use something like Open Web UI today the search integration works reasonably well.
mirekrusin · 5h ago
Exactly, I think all those base models should be weeded out from this nonsense, kardashian-like labyrinths of knowledge complexities that just makes them dumber by taking space and compute time. If you can google out some nonsense news, it should stay there in search engines for retrieval. Models should be good at using search tools, not at trying to replicate their results. They should start from logic, math, programming, physics and so on, similar to how education system is suppose to equip you with. IMHO small models can give this speed advantage (faster to experiment ie. with parallel diverging results, ability to munch through more data etc). Stripped to this bare minimum they can likely be much smaller with impressive results, tunable, allow for huge context etc.
justlikereddit · 5h ago
They are generalists, being search engines is a subset of that.
bearjaws · 5h ago
My guess is that it is over fitted to the tests.
revel · 4h ago
They used RFT and there's only so many benchmarks out there, so I would be very surprised if they didn't train on the tests.
Yes, 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.
Alifatisk · 7h ago
No, 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 · 7h ago
There is a HuggingFace space (probably not official) at: 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.
CodeCompost · 6h ago
Open Source or Open Weights?
NitpickLawyer · 5h ago
MIT - so open source
Davidzheng · 5h ago
Weights
ilrwbwrkhv · 5h ago
And this point everybody will open source their models or weights. The only one which will not is open AI.
rvz · 4h 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.
“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.
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.
[1] https://commoncrawl.org/
It's clearly impossible for me to try anything in Chinese, I'd need a translation.
Only place where russian is in top 5 is in Wikipedia views. Russian part of internet steadily goes down, as russian imperialism crumbles.
Is the lack of existing corpus just an extra hurdle for Hanzi-first models that are also leading the pack in benchmarks?
The literature that survived thousands of years are cream of the crop; you won't find lots of random unimportant dialog between people thousands of years ago, but you find that on Reddit.
Could it be that being multilingual results in a larger pool of human knowledge on the technical side compared to training on just a single language or 2. And on the business side, supporting more languages results in a larger TAM (total addressable market). Using english-language dataset for training LLMs is the default, not the other way like you insinuate.
What would a hypothetical "Mandarin-first" model look like to you?
I challenge the notion that the current models are "English-first" - that is an unsubstantiated opinion not supported by fact. I bet, dollars to donuts, these models are SoTA in Mandarin as well. When framed that way, asking "Why are they marketed as English-speaking models outside of China" or "Why are they really good at English" are simply not interesting questions - they have obvious answers.
Sad reality is that not many outside of China have the facility with Mandarin to use those models. Even non-native Mandarin speakers who claim to be "fluent", are often messing up intended meaning in text. Or making literal translations that wind up making no sense.
Inside of China, llm use will be Mandarin based. Outside, it seems to me English is the natural choice.
Irony of Irony, probably the best way for a non Mandarin speaking layman to test a Mandarin based model would be to use another LLM to translate prompts to Mandarin.
It's a sad future we're looking at.
Or a brilliant one.
Time will tell.
This smacks of "I saw a headline once"-itis. Especially the fact that you refer to the Chinese characters as "calligraphy characters", as if that were the general term or something.
Uh? Pinyin input is by far the most popular input technique in China. I rarely see anyone using handwriting input.
That being said, it has nothing to do with English winning. It's just a Chinese input technique that uses the latin alphabet. English fluency in China is not very common, especially spoken English.
It could not have been further from a bilingual society.
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.
https://github.com/ollama/ollama/blob/main/docs%2Fmodelfile....
>Single-file deployment
>Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
https://github.com/ggml-org/ggml/blob/master/docs/gguf.md
We literally just got rid of that multi file chaos only for ollama to add it back :/
https://github.com/ggml-org/llama.cpp/blob/master/examples/m...
If you only ever have one set of configuration parameters per model (same temp, top_p, system prompt...), then I guess you can put them in a gguf file (as the format is extensible).
But what if you want two different sets? You still need to keep them somewhere. That could be a shell script for llama.cpp, or a ModelFile for ollama.
(Assuming you don't want to create a new (massive) gguf file for each permutation of parameters.)
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.
This will show a separate entry in `ollama list` but only copy the Modelfile not the GGUF.
Alternatively, if you use the API, you can override parameters "temporarily". Some UIs let you do this easily, at least for common parameters.
I meant when you download a gguf file from huggingface, instead of using a model from ollama's library.
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.
Also related reference https://en.wikipedia.org/wiki/Xiaomi#Name_etymology
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.
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.
my employer talks about spending 10s of millions on AI
but, even at this early stage, my experiments indicate that the smaller, locally-run models are just fine for a lot of tech and business tasks
this approach has definite privacy advantages and likely has cost advantages, vs pay-per-use LLM over API.
I think ML is more akin to open source hardware, in the sense that even when there are people with the relevent skills willing to donate their time for free, the cost of actually realizing their ideas is still so high that it's rarely feasible to keep up with commercial projects.
[0]: https://huggingface.co/mlabonne/gemma-3-12b-it-abliterated
https://gist.github.com/estsauver/a70c929398479f3166f3d69bce...
Problem was that of a top ten book recommendations only the first 3 existed and the rest was a casually blended hallucination delivered in perfect English without skipping a beat.
"You like magic? Try reading the Harlew Porthouse series by JRR Marrow, following the orphan magicians adventures in Hogwesteros"
And the further towards the context limit it goes the deeper this descent into creative derivative madness it goes.
It's entertaining but limited in usefulness.
Go look at the benchmark numbers of qwen3-4B if you think these are unrealistic.
They could've called it Xiaomimo.
Probably within few hours will be released.
But yeah waiting is the easier option
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/
Here is the meaning of the name
Described here: 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