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Introducing Gemma 3n
- wiradikusuma 1y agoI still don't understand the difference between Gemma and Gemini for on-device, since both don't need network access. From https://developer.android.com/ai/gemini-nano https://developer.android.com/ai/gemini-nano : "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.
- deleted 1y ago[deleted]
- readthenotes1 1y agoPerplexity.ai gave an easier to understand response than Gemini 2.5 afaict. 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
- tyushk 1y agoLicensing. You can't use Gemini Nano weights directly (at least commercial ly) and must interact with them through Android MLKit or similar Google approved runtimes. You can use Gemma commercially using whatever runtime or framework you can get to run it.
- littlestymaar 1y agoIt's not even clear you can license language model weight though. 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).
- skissane 1y agoIANAL either but the answer likely depends on the jurisdiction 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
- simonw 1y ago> 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. 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.
- jabroni_salad 1y agoGemma is open source and apache 2.0 licensed. If you want to include it with an app you have to package it yourself. gemini nano is an android api that you dont control at all.
- nicce 1y ago> Gemma is open source and apache 2.0 licensed Closed source but open weight. Let’s not ruin the definition of the term in advantage of big companies.
- zackangelo 1y agoYour reply adds more confusion, imo. 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 https://github.com/google-deepmind/gemma [1] https://github.com/vllm-project/vllm/pull/2964 https://github.com/vllm-project/vllm/pull/2964
- candiddevmike 1y agoIf for some reason you had the training data, is it even possible to create an exact (possibly same hash?) copy of the model? Seems like there are a lot of other pieces missing like the training harness, hardware it was trained on, etc?
- OneDeuxTriSeiGo 1y agoto be entirely fair that's quite a high bar even for most "traditional" open source. 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.
- impure 1y agoI suspect the difference is in the training data. Gemini is much more locked down and if it tries to repeat something from the draining data verbatim you will get a 'recitation error'.
- floridianfisher 1y agoAccording go the Gemma 3n preview blog, Gemma 3n shares the same architecture as the upcoming version of Gemini Nano. 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/ https://developers.googleblog.com/en/introducing-gemma-3n/ Video in the in the blog linked in this post
- danielhanchen 1y agoMade some GGUFs if anyone wants to run them! ./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-tune https://docs.unsloth.ai/basics/gemma-3n-how-to-run-and-fine-...
- upghost 1y agoLiterally was typing out "Unsloth, do your thing!!" but you are way ahead of me. You rock <3 <3 <3 Thank you!
- danielhanchen 1y ago:) Thanks!
- deleted 1y ago[deleted]
- knowaveragejoe 1y agoWhat is `jinja` in this context?
- bilsbie 1y agoThanks! What kind of rig do I need?
- turnsout 1y agoThis looks amazing given the parameter sizes and capabilities (audio, visual, text). I like the idea of keeping simple tasks local. I’ll be curious to see if this can be run on an M1 machine…
- arresin 1y ago[flagged]
- deleted 1y ago[deleted]
- minimaxir 1y agoLM Studio has MLX variants of the model out: http://huggingface.co/lmstudio-community/gemma-3n-E4B-it-MLX-bf16 http://huggingface.co/lmstudio-community/gemma-3n-E4B-it-MLX... However it's still 8B parameters and there are no quantized models just yet.
- Workaccount2 1y agoAnyone have any idea on the viability of running this on a Pi5 16GB? I have a few fun ideas if this can handle working with images (or even video?) well.
- gardnr 1y agoThe 4-bit quant weighs 4.25 GB and then you need space for the rest of the inference process. So, yeah you can definitely run the model on a Pi, you may have to wait some time for results. https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF https://huggingface.co/unsloth/gemma-3n-E4B-it-GGUF
- refulgentis 1y agoSee here, long story short, this is another in a series of blog posts that would lead you to believe this was viable, but it isn't :/ https://news.ycombinator.com/item?id=44389793 https://news.ycombinator.com/item?id=44389793
- jacknews 1y ago[flagged]
- svat 1y agoThis is completely offtopic, but in case your question is genuine: https://www.youtube.com/watch?v=F2X1pKEHIYw https://www.youtube.com/watch?v=F2X1pKEHIYw > Why Some People Say SHTRONG (the CHRUTH), by Dr Geoff Lindsey
- impure 1y agoI've been playing around with E4B in AI Studio and it has been giving me really great results, much better than what you'd expect from an 8B model. In fact I'm thinking of trying to install it on a VPS so I can have an alternative to pricy APIs.
- tgtweak 1y agoAny readily-available APKs for testing this on Android?
- refulgentis 1y agoAPK link here: https://github.com/google-ai-edge/gallery?tab=readme-ov-file#-get-started-in-minutes https://github.com/google-ai-edge/gallery?tab=readme-ov-file...
- tgtweak 1y agoAh, I already had edge installed and it had gemma 3n-e4b downloaded... is this the same model that was previously released?
- refulgentis 1y agoSomethings really screwy with on-device models from Google, I can't put my finger on what, and I think being ex-Google is screwing with my ability to evaluate. 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#-get-started-in-minutes 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?
- catchmrbharath 1y agoThe APK that you linked, runs the inference on CPU and does not run it on Google Tensor.
- refulgentis 1y agoThat sounds fair, but opens up another N questions: - 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)
- lucb1e 1y agoI read the general parts and skimmed the inner workings but I can't figure out what the high-level news is. What does this concretely do that Gemma didn't already do, or what benchmark/tasks did it improve upon? Until it goes into the inner details (MatFormer, per-layer embeddings, caching...), the only sentence I've found that concretely mentions a new thing is "the first model under 10 billion parameters to reach [an LMArena score over 1300]". So it's supposed to be better than other models until those that use 10GB+ RAM, if I understand that right?
- awestroke 1y ago> What does this concretely do that Gemma didn't already do Open weights
- deleted 1y ago[deleted]
- lucb1e 1y agoHuh? I'm pretty sure I ran Gemma on my phone last month. Or is there a difference between downloadable (you get the weights because it's necessary to run the thing) and "open" weights?
- throwaway2087 1y agoWasn't it a preview version?
- lucb1e 1y agoOh, that could be. So this is the first on-device model that Google releases, that's the news?
- ColonelPhantom 1y agoI think the other poster is confused. Both Gemma 3 and Gemma 3n are open-weight models. Google's proprietary model line is called Gemini. There is a variant that can be ran offline called Gemini Nano, but I don't think it can be freely distributed and is only allowed as part of Android. As for what's new, Gemma 3n seems to have some optimizations done to it that lead it to be better than the 'small' Gemma 3 models (such as 4B) at similar speed or footprint.
- conradev 1y agoKevin Kwok did a great job taking it apart: https://github.com/antimatter15/reverse-engineering-gemma-3n https://github.com/antimatter15/reverse-engineering-gemma-3n
- ghc 1y agoI just tried gemma3 out and it seems to be prone to getting stuck in loops where it outputs an infinite stream of the same word.
- sigmoid10 1y agoSounds a lot like an autoregressive sampling problem. Maybe try to set temperature and repeat penalty differently.
- ghc 1y agoYou're right, I should have checked the model settings. For some reason the default model profile in Ollama had temperature set to 0. Changing the temperature and repeat penalty worked much better than it did when I tried to correct similar behavior in the smallest phi4 reasoning model.
- jeffmcjunkin 1y agoThank you, this was affecting me too.
- actinium226 1y agoI'm not a fan of this anarchic naming convention that OpenAI has apparently made standard across the industry.
- unsupp0rted 1y agoWhat would you have called it?
- deleted 1y ago[deleted]
- Aeolun 1y agoGemma 4? I feel that one was incredibly obvious. Let us please just increase the version numbers. 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.
- ColonelPhantom 1y agoWhat do you mean by Anthropic shifting their ordering? It seems to still be consistently Opus > Sonnet > Haiku. They didn't release 4 Haiku, but they also didn't release 3.5 Opus, and pricing wise Sonnet 4 lines up with earlier Sonnets. 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.
- Aeolun 1y agoclaude-3-7-sonnet-latest -> claude-sonnet-4 Also, I think you just came up with a better name than Gemma 3n. Gemma 3 Lite is a lot easier to understand.
- actinium226 1y agoI wouldn't have added a random letter and I would have chosen a name that's less easy to conflate with Gemini.
- ericvolp12 1y agoThe Y-axis in that graph is fucking hilarious
- deleted 1y ago[deleted]
- lostmsu 1y agoI made a simple website[0] to check online model MMLU quickly (runs a subset), and Gemma 3n consistently loses to LLaMA 3.3 (~61% vs ~66%), and definitely loses to LLaMA 4 Scout (~86%). I suspect that means its rating on LMArena Leaderboard is just some form of gaming the metric. What's interesting, that it beats smarter models in my Turing Test Battle Royale[1]. I wonder if it means it is a better talker. 0. https://mmlu.borgcloud.ai/ https://mmlu.borgcloud.ai/ 1. https://trashtalk.borg.games/ https://trashtalk.borg.games/
- bravetraveler 1y agoUpdated Ollama to use this, now neither old or new work - much productivity
- rvnx 1y agoWell, see it the other way, there is something positive: commenters here on HN claim that AI is useless. You can now also join the bandwagon of people who have free time.
- lowbatt 1y agoIf I wanted to run this locally at somewhat decent speeds, is an RK3588S board (like OrangePi 5) the cheapest option?
- ac29 1y agoRK3588 uses a 7 year old CPU design and OrangePi 5 looks expensive (well over $100). A used sub-$100 x86 box is going to be much better
- lowbatt 1y agoyou're right. For my purposes, I was thinking of something I could use if I wanted to manufacture a new (smallish) product
- jm4 1y agoIt depends on your idea of decent speeds and what you would use it for. I just tried it on a laptop with an AMD HX 370 running on battery in power save mode and it's not especially impressive, although it runs much better in balanced or performance mode. I gave it the prompt "write a fizzbuzz program in rust" and it took almost a minute and a half. I expect it to be pretty terrible on an SBC. Your best bet is to try it out on the oldest hardware you have and figure out if you can tolerate worse performance.
- lowbatt 1y agogood idea, will test that out
- babl-yc 1y agoI'm going to attempt to get it running on the BeagleY-AI https://www.beagleboard.org/boards/beagley-ai https://www.beagleboard.org/boards/beagley-ai Similar form factor to raspberry pi but with 4 TOPS of performance and enough RAM.
- zipping1549 1y agoTried with S25+ (SD 8 elite). 0.82tok/s(4B L model). It's barely useful speed but it's pretty impressive either.
- nsingh2 1y agoWhats are some use cases for these local small models, for individuals? Seems like for programming related work, the proprietary models are significantly better and that's all I really use LLMs for personally. Though I can imagine a few commercial applications where something like this would be useful. Maybe in some sort of document processing pipeline.
- russdill 1y agoHoping to try it out with home assistant.
- toddmorey 1y agoI think speech to text is the highlight used case for local models because they are now really good at it and there’s no network latency.
- oezi 1y agoHow does it compare to whisper? Does it hallucinate less or is more capable?
- androng 1y agofiltering out spam SMS messages without sending all SMS to the cloud
- thimabi 1y agoI’m thinking about building a pipeline to mass generate descriptions for the images in my photo collection, to facilitate search. Object recognition in local models is already pretty good, and perhaps I can pair it with models to recognize specific people by name as well.
- jsphweid 1y agoFor me? Handling data like private voice memos, pictures, videos, calendar information, emails, some code etc. Stuff I wouldn't want to share on the internet / have a model potential slurp up and regurgitate as part of its memory when the data is invariably used in some future training process.
- thimabi 1y agoSuppose I'd like to use models like this one to perform web searches. Is there anything available in the open-source world that would let me do that without much tinkering needed? I think it’s something that even Google should consider: publishing open-source models with the possibility of grounding their replies in Google Search.
- vorticalbox 1y agoI have been using ollama + open web ui. open webUI already have a web search tool all you would need to do is click the toggle for it under the chat.
- zettabomb 1y agoUnfortunately the OWUI web search is really slow and just not great overall. I would suggest using an MCP integration instead.
- thimabi 1y agoCan you recommend a specific piece of software for using an MCP integration for web searches with local LLMs? That’s the first time I’ve heard of this.
- nickthegreek 1y agoi think the ones i’ve heard mentioned on youtube use an mcp that interfaces with searxng.
- joerick 1y agoGoogle do have an API for this. It has limits but perfectly good for personal use. https://developers.google.com/custom-search/v1/overview https://developers.google.com/custom-search/v1/overview
- thimabi 1y ago
- kccqzy 1y agoIt seems way worse than other small models, including responding with complete non sequiturs. I think my favorite small model is still DeepSeek distilled with Llama 8B.
- oezi 1y agoThe key here is multimodal.
- rvnx 1y agoIs there a chance that we see an uncensored version of this ?
- throwaway2087 1y agoCan you apply abiliteration? I'm not sure if their MatFormer architecture is compatible with current techniques
- pilooch 1y agoThis model is fully compatible with anything previously done with gemma3. Just passed it to one of my vlm fine-tuning scripts and it started without issues (hf transformer code). On a single GPU with Lora the E4B model takes 18Gb of VRAM in batch size 1 where gemma-4B was 21Gb. Nice one from deepmind, the gemma3 family tops the open weights VLLMs.
- pilooch 1y agoFix: it's the E2B
- refulgentis 1y agoMy post politely describing this blog post does not match Google's own app, running inference on Pixel, is downvoted to -1, below dead posts with one-off short jokes. I am posting again because I've been here 16 years now, it is very suspicious that happened, and given the replies to it, we now know this blog post is false. There is no open model that you can download today and run at even 1% of the claims in the blog post. You can read a reply from someone indicating they have inside knowledge on this, who notes this won't work as advertised unless you're Google (i.e. internally, they have it binding to a privileged system process that can access the Tensor core, and this isn't available to third parties. Anyone else is getting 1/100th of the speeds in the post) This post promises $150K in prizes for on-device multimodal apps and tells you it's running at up to 60 fps, they know it runs at 0.1 fps, Engineering says it is because they haven't prioritized 3rd parties yet, and somehow, Google is getting away with this.
- simonw 1y agoI tried my "Generate an SVG of a pelican riding a bicycle" prompt against Gemma 3n 7.5GB from Ollama and 15GB for mlx-vlm and got a pleasingly different result for the two quantization sizes: https://simonwillison.net/2025/Jun/26/gemma-3n/ https://simonwillison.net/2025/Jun/26/gemma-3n/
- JohnKemeny 1y agoIs that actually a useful benchmark, or is it just for the laughs? I've never really understood that.
- OtherShrezzing 1y agoFor me, it shows if LLM are generalising from their training data. LLM understand all of the words in the prompt. they understand the spec for svg better than any human. They know what a bird is. They know what a bike is. They know how to draw (and given access to computer-use could probably ace this test). They can plan and execute on those plans. 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.
- mvdtnz 1y ago[flagged]
- simonw 1y agoScored a whole two upvotes here, my scheme is clearly working great!
- DrammBA 1y agoLeave some upvotes for the rest of us
- Aeolun 1y agoKinda feel like the content is a much better reason to visit than the pelicans. Though I suppose the pelicans are part of the content. 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.
- zknowledge 1y agoanyone know how much it costs to use the deployed version of gemma 3n? The docs indicate you can use the gemini api for deployed gemma 3n but the pricing page just shows "unavailable"
- kgwxd 1y agoCan popular sci-fi go 30 seconds without some lame wad naming themselves or a product after it?
- jwr 1y agoI'd genuinely like to know how these small models are useful for anyone. I've done a lot of experimenting, and anything smaller than 27B is basically unusable, except as a toy. All I can say for smaller models is that they sometimes produce good answers, which is not enough for anything except monkeying around. 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.
- runeblaze 1y agoI am sure as ideation devices these can work fine. I treat this more like basic infra. I would absolutely love the future where most phones have some small LLM built in, kind of like a base layer of infra
- newswangerd 1y agoThe best use case I've found for tiny models (<5bn params) as a reference tool for when I don't have WiFi. I've been using qwen on my MacBook Air as a replacement for Google while I'm writing code on flights. They work great for asking basic questions about syntax and documentation.
- iamnotagenius 1y agoTiny, 4b or less models are designed for finetuning for some narrow tasks; this way can outperform large commercial models for a tiny fraction of price. Also great for code autocomplete. 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.
- concats 1y agoThere are use cases where even low accuracy could be useful. I can't predict future products, but here are two that are already in place today: - 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.
- jen729w 1y ago> for everything from safeguarding Maybe you could install it on YouTube, where my 78-year-old mother received a spammy advert this morning from a scam app pretending to be an iOS notification. Kinda sick of companies spending untold billions on this while their core product remains a pile of user-hostile shite. :-)
- lerp-io 1y agoimagine the entire internet is just an on the fly ui, would be pretty cool
- Brajeshwar 1y agoWe need tabular data somewhere on Google that lists the titles of the products and their descriptions or functions or what they do.
- lxgr 1y ago> It’s supported by your favorite tools including Hugging Face Transformers, llama.cpp, Google AI Edge, Ollama, MLX, and many others Does anybody know how to actually run these using MLX? mlx-lm does not currently seem to support them, so I wonder what Google means exactly by "MLX support".
- mh- 1y agoI had success using the models from lmstudio-community here. https://huggingface.co/lmstudio-community https://huggingface.co/lmstudio-community
- lxgr 1y agoThank you! Do you know if these actually preserve the structure of Gemma 3n that make these models more memory efficient on consumer devices? I feel like the modified inference architecture described in the article is what makes this possible, but it probably needs additional software support. But given that they were uploaded a day ago (together with the blog post), maybe these are actually the real deal? In that case, I wish Google could just link to these instead of to https://huggingface.co/mlx-community/gemma-3n-E4B-it-bf16 https://huggingface.co/mlx-community/gemma-3n-E4B-it-bf16. Edit: Ah, these are just non-MLX models. I might give them a try, but not what I was looking for. Still, thank you!
- mh- 1y agoThat's a great question that is beyond my technical competency in this area, unfortunately. I fired up LM Studio when I saw this HN post, and saw it updated its MLX runtime [0] for gemma3n support. Then went looking for an MLX version of the model and found that one. [0]: https://github.com/lmstudio-ai/mlx-engine https://github.com/lmstudio-ai/mlx-engine
- techlatest_net 1y ago[dead]
- eabeezxjc 1y agohttps://github.com/Mozilla-Ocho/llamafile https://github.com/Mozilla-Ocho/llamafile not working ;( 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
- eabeezxjc 1y agohow using ASR ? translate voice to text?
- g92sdi 1y agoAnyone run this on an RK3588 yet?