I didn't realize quite how strong the correlation was until I put together this talk: https://simonwillison.net/2025/Jun/6/six-months-in-llms/
Since it's not a formal encoding of geometric shapes, it's fundamentally different I guess, but it shares some challenges with the SVG tasks I guess? Correlating phrases/concepts with an encoded visual representation, but without using imagegen, that is.
Do you think that "image encoding" is less useful?
It's a thing I love to try with various models for fun, too.
Talking about illustration-like content, neither text-based ASCII art nor abusing it for rasterization.
The results have been interesting, too, but I guess it's less predictable than SVG.
Everything here should be trivial for LLM, but they’re quite poor at it because there’s almost no “how to draw complex shapes in svg” type content in their training set.
I'm quite happy that there's someone with both the time to keep up with all the LLM/AI stuff, that is also good enough at writing amusing stuff that I want to keep reading it.
That's how the pelicans get ya.
But I understood your point, Simon asked it to output SVG (text) instead of a raster image so it's more difficult.
"Gemini Nano allows you to deliver rich generative AI experiences without needing a network connection or sending data to the cloud." -- replace Gemini with Gemma and the sentence still valid.
You can use Gemma commercially using whatever runtime or framework you can get to run it.
I'm not a lawyer but the analysis I've read had a pretty strong argument that there's no human creativity involved in the training, which is an entirely automatic process, and as such it cannot be copyrighted in any way (the same way you cannot put a license on a software artifact just because you compiled it yourself, you must have copyright ownership on the source code you're compiling).
US standards for copyrightability require human creativity and model weights likely don’t have the right kind of human creativity in them to be copyrightable in the US. No court to my knowledge has ruled on the question as yet, but that’s the US Copyright Office’s official stance.
By contrast, standards for copyrightability in the UK are a lot weaker than-and so no court has ruled on the issue in the UK yet either, it seems likely a UK court would hold model weights to be copyrightable
So from Google/Meta/etc’s viewpoint, asserting copyright makes sense, since even if the assertion isn’t legally valid in the US, it likely is in the UK - and not just the UK, many other major economies too. Australia, Canada, Ireland, New Zealand tend to follow UK courts on copyright law not US courts. And many EU countries are closer to the UK than the US on this as well, not necessarily because they follow the UK, often because they’ve reached a similar position based on their own legal traditions
Finally: don’t be surprised if Congress steps in and tries to legislate model weights as copyrightable in the US too, or grants them some sui generis form of legal protection which is legally distinct from copyright but similar to it-I can already hear the lobbyist argument, “US AI industry risks falling behind Europe because copyrightability of AI models in the US is legally uncertain and that legal uncertainty is discouraging investment”-I’m sceptical that is actually true, but something doesn’t have to be true for lobbyists to convince Congress that it is
"Your Honor i didn't copy their weights, i used them to train my models weights"
Has the US copyright office said that about model weights? I've only heard them saying that about images produced entirely from a prompt to a model.
However, read page 22 of https://www.copyright.gov/comp3/chap300/ch300-copyrightable-... - it is their settled position that the output of a mechanical process cannot be copyrightable unless there was substantial human creative input into it - and it is pretty clear that AI training doesn’t involve human creative input in the relevant sense. Now, no doubt there is lots of human skill and art in picking the best hyperparameters, etc - but that’s not input of the right kind. An analogy - a photocopier does not create a new copyright in the copy, even though there is skill and art in picking the right settings on the machine to produce the most faithful copy. The human creativity in choosing hyperparameters isn’t relevant to copyrightability because it isn’t directly reflected in the creative elements of the model itself
A model with RLHF fine-tuning could be a different story - e.g. Anthropic went to a lot of effort to make Claude speak with a distinctive “voice”, and some of that involved carefully crafting data to use for fine-tuning, and the model may contain some of the copyright of that training data.
But, even if that argument also applies to Gemma or Llama - if someone intentionally further fine-tunes the model in order to remove that distinctive “voice”, then you’ve removed the copyrightable element from the model and what is left isn’t copyrightable. Because the really expensive part of building a model is building the foundation model, and that’s the part least likely to be copyrightable; whereas, fine-tuning to speak with a distinctive voice is more likely to be copyrightable, but that’s the easy part, and easy to rip out (and people have motivation to do so because a lot of people desire a model which speaks with a different voice instead)
but who knows judges can be weird about tech
I think the case is the strongest with RLHF - if your model speaks with a distinctive “voice”, and to make it do so you had to carefully craft training data to give it that voice, such that there are obvious similarities (shared turns of speech, etc) between your RLHF training input and the model outputs - that aspect of the model likely is copyrightable. But if you are trying to improve a model’s performance at mathematics problems, then no matter how much creativity you put into choosing training data, it is unlikely identifiable creative elements from the training data survive in the model output, which suggests that creativity didn’t actually make it into the model in the sense relevant to US copyright law
Real example: UK law says telephone directories are eligible for copyright, US law says they aren’t. The US is not violating the Berne convention by refusing to recognise copyright in UK phone directories, because the US doesn’t recognise copyright in US phone directories either. A violation would be if the US refused to recognise copyright in UK phone directories but was willing to recognise it in US ones
It is clear you can license (give people permissions to) model weights, it is less clear that there is any law protecting them such that they need a license, but since there is always a risk of suit and subsequent loss in the absence of clarity, licenses are at least beneficial in reducing that risk.
It seems to me that if some anonymous ne'er-do-well were to publicly re-host the model files for separate download; and you acquired the files from that person, rather than from Google; then you wouldn't be subject to their license, as you never so much as saw the clickwrap.
(And you wouldn't be committing IP theft by acquiring it from that person, either, because of the non-copyrightability.)
I feel that there must be something wrong with that logic, but I can't for the life of me think of what it is.
Google gives the model to X who gives it to Y who gives it to Z. X has a contract with Google, so Google can sue X for breach of contract if they violate its terms. But do Y and Z have such a contract? Probably not. Of course, Google can put language in their contract with X to try to make it bind Y and Z too, but is that language going to be legally effective? More often than not, no. The language may enable Google to successfully sue X over Y and Z’s behaviour, but not successfully sue Y and Z directly. Whereas, with copyright, Y and Z are directly liable for violations just as X is
Now, if you are aware of a contract between two parties, and you actively and knowingly cooperate with one of them in violating it, you may have some legal liability for that contractual violation even though you weren’t formally party to the contract, but there are limits - if I know you have signed an NDA, and I personally encourage you to send me documents covered by the NDA in violation of it, I may indeed be exposed to legal liability for your NDA violation. But, if we are complete strangers, and you upload NDA-protected documents to a file sharing website, where I stumble upon them and download them - then the legal liability for the NDA violation is all on you, none on me. The owner of the information could still sue me for downloading it under copyright law, but they have no legal recourse against me under contract law (the NDA), because I never had anything to do with the contract, neither directly nor indirectly
If you download a model from the vendor’s website, they can argue you agreed to the contract as a condition of being allowed to make the download. But if you download it from elsewhere, what is the consideration (the thing they are giving you) necessary to make a binding contract? If the content of the download is copyrighted, they can argue the consideration is giving you permission to use their copyrighted work; but if it is an AI model and models are uncopyrightable, they have nothing to give when you download it from somewhere else and hence no basis to claim a contractual relationship
What they’ll sometimes do, is put words in the contract saying that you have to impose the contract on anyone else you redistribute the covered work to. And if you redistribute it in full compliance with those terms, your recipients may find themselves bound by the contract just as you are. But if you fail to impose the contract when redistributing, the recipients escape being bound for it, and the legal liability for that failure is all yours, not theirs
By contrast, UK copyright law accepts the “mere sweat of the brow” doctrine, the mere fact you spent money on training is likely sufficient to make its output copyrightable, UK law doesn’t impose the same requirements for a direct human creative contribution
Nobody knows for sure what the legal answer is, because the question hasn’t been considered by a court - but the consensus of expert legal opinion is copyrightability of models is doubtful under US law, and the kind of argument you make isn’t strong enough to change that. As I said, different case for UK law, nobody really needs your argument there because model weights likely are copyrightable in the UK already
It's particularly perilous since the AI trainers are at the same time in a position where they want to argue that copyrighted work they included in the training data don't actually survive in the trained model.
Also i'm pretty sure none of the AI companies would really want to touch the concept of having the copyright of source data affect the weight's own copyright, considering all of them pretty much hoover up the entire Internet without caring about those copyrights (and IMO trying to claim that they should be able to ignore the copyrights of training data and also that the GenAI output is not under copyright but at the same trying trying to claim copyright for the weights is dishonest, if not outright leechy).
(The combination is what makes it copyrightable).
It's the “actual creativity” inside. And it is a fuzzy concept.
From what I understand, copyright only applies to the original source code, GUI and bundled icon/sound/image files. Functionality etc. would fall under patent law. So the compiled code on your .ISO for example would not only be "just raw numbers" but uncopyrightable raw numbers.
The ‘n’ presumably stands for Nano.
Nano is a proprietary model that ships with Android. Gemma is an open model that can be adapted and used anywhere.
Sources: https://developers.googleblog.com/en/introducing-gemma-3n/
Video in the in the blog linked in this post
gemini nano is an android api that you dont control at all.
Closed source but open weight. Let’s not ruin the definition of the term in advantage of big companies.
The inference code and model architecture IS open source[0] and there are many other high quality open source implementations of the model (in many cases contributed by Google engineers[1]). To your point: they do not publish the data used to train the model so you can't re-create it from scratch.
[0] https://github.com/google-deepmind/gemma [1] https://github.com/vllm-project/vllm/pull/2964
Deepseek published a lot of their work in this area earlier this year and as a result the barrier isn’t as high as it used to be.
And even if you had the same data, there's no guarantee the random perturbations during training are driven by a PRNG and done in a way that is reproducible.
Reproducibility does not make something open source. Reproducibility doesn't even necessarily make something free software (under the GNU interpretation). I mean hell, most docker containers aren't even hash-reproducible.
Their publications about producing Gemma is not accurate enough that even with data you would get the same results.
Also, even if it were for fine tuning, that would require an implementation of the model’s forward pass (which is all that’s necessary to run it).
Are you sure? On a quick look, it appears to use its own bespoke license, not the Apache 2.0 license. And that license appears to have field of use restrictions, which means it would not be classified as an open source license according to the common definitions (OSI, DFSG, FSF).
Gemini nano is for Android only.
Gemma is available for other platforms and has multiple size options.
So it seems like Gemini nano might be a very focused Gemma everywhere to follow the biology metaphor instead of the Italian name interpretation
I solved my spam problem with gemma3:27b-it-qat, and my benchmarks show that this is the size at which the current models start becoming useful.
It sounds like you're trying to use them like ChatGPT, but I think that's not what they're for.
- On the keyboard on iphones some sort of tiny language model suggest what it thinks are the most likely follow up words when writing. You only have to pick a suggested next word if it matches what you were planning on typing.
- Speculative decoding is a technique which utilized smaller models to speed up the inference for bigger models.
I'm sure smart people will invent other future use cases too.
7b-8b are great coding assistants if all you need is dumb fast refactoring, that cannot quite be done with macros and standard editor functionality but still primitive, such as "rename all methods having at least one argument of type SomeType by prefixing their names with "ST_".
12b is a threshold where models start writing coherent prose such Mistral Nemo or Gemma 3 12b.
Anthropic is better about this, but then shifted their ordering with the v4 models. Arguably better, but still quite annoying since everything pre-4 uses a different naming scheme.
As for this Gemma release, I don't think Gemma 4 would be an appropriate name. 3n is limited to very small versions (like 8B total parameters) and is therefore likely less powerful than Gemma 3.
From my impression this is more like a "Gemma 3 Lite" that provides a better speed/quality tradeoff than the smaller Gemma 3 models.
./llama.cpp/llama-cli -hf unsloth/gemma-3n-E4B-it-GGUF:UD-Q4_K_XL -ngl 99 --jinja --temp 0.0
./llama.cpp/llama-cli -hf unsloth/gemma-3n-E2B-it-GGUF:UD-Q4_K_XL -ngl 99 --jinja --temp 0.0
I'm also working on an inference + finetuning Colab demo! I'm very impressed since Gemma 3N has audio, text and vision! https://docs.unsloth.ai/basics/gemma-3n-how-to-run-and-fine-...
Works fine with regular Gemma 3 4B, so I'll assume it's something on Ollama's side. edit: yep, text-only for now[1], would be nice if that was a bit more prominent than burried in a ticket...
Don't feel like compiling llama.cpp myself, so I'll have to wait to try your GGUFs there.
[1]: https://github.com/ollama/ollama/issues/10792#issuecomment-3...
Thank you!
Cherry-picking something that's quick to evaluate:
"High throughput: Processes up to 60 frames per second on a Google Pixel, enabling real-time, on-device video analysis and interactive experiences."
You can download an APK from the official Google project for this, linked from the blogpost: https://github.com/google-ai-edge/gallery?tab=readme-ov-file...
If I download it, run it on Pixel Fold, actual 2B model which is half the size of the ones the 60 fps claim is made for, it takes 6.2-7.5 seconds to begin responding (3 samples, 3 diff photos). Generation speed is shown at 4-5 tokens per second, slightly slower than what llama.cpp does on my phone. (I maintain an AI app that inter alia, wraps llama.cpp on all platforms)
So, *0.16* frames a second, not 60 fps.
The blog post is so jammed up with so many claims re: this is special for on-device and performance that just...seemingly aren't true. At all.
- Are they missing a demo APK?
- Was there some massive TPU leap since the Pixel Fold release?
- Is there a lot of BS in there that they're pretty sure won't be called out in a systematic way, given the amount of effort it takes to get this inferencing?
- I used to work on Pixel, and I remember thinking that it seemed like there weren't actually public APIs for the TPU. Is that what's going on?
In any case, either:
A) I'm missing something, big or
B) they are lying, repeatedly, big time, in a way that would be shown near-immediately when you actually tried building on it because it "enables real-time, on-device video analysis and interactive experiences."
Everything I've seen the last year or two indicates they are lying, big time, regularly.
But if that's the case:
- How are they getting away with it, over this length of time?
- How come I never see anyone else mention these gaps?
> MobileNet-V5-300M
Which makes sense as it's 300M in size and probably far less complex, not a multi billions of parameters transformer.
I guess there's benefit to running that step without subsampling to the initial 256 tokens per image/frame ( https://ai.google.dev/gemma/docs/gemma-3n/model_card#inputs_... ) to go on from that, https://github.com/antimatter15/reverse-engineering-gemma-3n suggests these are 2048 dimensional tokens, which makes these 60 Hz frame digestion rate produce just under 31.5 Million floats-of-your-choosen-precision per second. At least at the high (768x768) input resolution, this is a bit less than one float per pixel.
I guess maybe with very heavy quantizing to like 4 bit that could beat sufficiently-artifact-free video coding for then streaming the tokenized vision to a (potentially cloud) system that can keep up with the 15360 token/s at (streaming) prefill stage?
Or I could imagine just local on-device visual semantic search by expanding the search query into a bunch of tokens that have some signed desire/want-ness each and where the search tokens get attended to the frame's encoded tokens, activation function'd, scaled (to positive/negative) by the search token's desire score, and then just summed over each frame to get a frame score which can be used for ranking and other such search-related tasks.
(For that last thought, I asked Gemini 2.5 Pro to calculate flops load, and it came out to 1.05 MFLOPS per frame per search token; Reddit suggests the current Pixel's TPU does around 50 TOPS, so if these reasonably match each terminology wise, assuming we're spending about 20% of it's compute on the search/match aspect, it comes out to an unreasonably (-seeming) about 190k tokens the search query could get expanded to. I interpret this result to imply that quality/accuracy issues in the searching/filtering mechanism would hit before encountering throughout issues in this.)
> I guess maybe with very heavy quantizing to like 4 bit that could beat sufficiently-artifact-free video coding for then streaming the tokenized vision to a (potentially cloud) system that can keep up with the 15360 token/s at (streaming) prefill stage?
the 6-7s I am seeing is what it costs to run an image model, even running on GPU on M4 Max with 64GB of GPU RAM. This repros with my llama.cpp wrapper, and the llama.cpp demo of it.
It is simply getting tokens that is taking that long.
Given that reality, we can ignore it, of course. We could assume the image model does run on Pixel at 60 fps, and there's just no demo APK available, or just say it's all not noteworthy because as the Google employee points out, they can do it inside Google, and external hasn't been prioritized.
The problem is that the blog post is announcing this runs on device at up to 60 fps today, and announces $150K in prizes if you work based on this premise. We have 0 evidence of this externally, the most plausible demo of it released externally by Google is running at 1/500th of this speed, and 1 likely Google employee is saying "yup, it doesn't, we haven't prioritized external users!" The best steelman we can come up with is "well, if eventually the image model runs at 60 fps, we could stream it to an LLM in the cloud with about 4 seconds initiate + prefill latency!"
That's bad.
- Are there APK(s) that run on Tensor?
- Is it possible to run on Tensor if you're not Google?
- Is there anything at all from anyone I can download that'll run it on Tensor?
- If there isn't, why not? (i.e. this isn't the first on device model release by any stretch, so I can't give benefit of the doubt at this point)
No. AiCore service internally uses the inference on Tensor (http://go/android-dev/ai/gemini-nano)
> Is there anything at all from anyone I can download that'll run it on Tensor?
No.
> If there isn't, why not? (i.e. this isn't the first on device model release by any stretch, so I can't give benefit of the doubt at this point)
Mostly because 3P support has not been a engineering priority.
Got it: assuming you're at Google, in eng. parlance, it's okay if it's not Prioritized™ but then product/marketing/whoever shouldn't be publishing posts around the premise it's running 60 fps multimodal experiences on device.
They're very, very, lucky that ratio of people vaguely interested in this, to people follow through on using it, is high, so comments like mine end up at -1.
https://ai.google.dev/edge/litert/android/npu/overview has been identical for a year+ now.
In practice Qualcomm and MediaTek ship working NPU SDKs for third party developers, NNAPI doesn't count and is deprecated anyway.
(n.b. to readers, if you click through, the Google Pixel Tensor API is coming soon. So why in the world has Google been selling Tensor chips in Pixel as some big AI play since...idk, at least 2019?)
On third party model workloads, this is what you will get:
https://ai-benchmark.com/ranking.html
https://browser.geekbench.com/ai-benchmarks (NPU tab, sort w/ quantisation and/or half precision)
Google is clearly not serious on Pixels in practice, and the GPU performance is also behind by quite a lot compared to flagships, which really doesn't help. CPUs are also behind by quite a lot too...
The only one we have works as described, TL;Dr 0.1 fps.
gemma-3n-E4B-it-Q8_0 import_cuda_impl: initializing gpu module... get_rocm_bin_path: note: hipcc not found on $PATH [...] llama_model_load: error loading model: error loading model architecture: unknown model architecture: 'gemma3n' llama_load_model_from_file: failed to load model llama_init_from_gpt_params: error: failed to load model 'gemma-3n-E4B-it-Q8_0.gguf' main: error: unable to load model