Impressive model, for sure. I've been running it on my Mac, now I get to have it locally in my iPhone? I need to test this. Wait, it does agent skills and mobile actions, all local to the phone? Whaaaat? (Have to check out later! Anyone have any tips yet?)
I don't normally do the whole "abliterated" thing (dealignment) but after discovering https://github.com/p-e-w/heretic , I was too tempted to try it with this model a couple days ago (made a repo to make it easier, actually) https://github.com/pmarreck/gemma4-heretical and... Wow. It worked. And... Not having a built-in nanny is fun!
It's also possible to make an MLX version of it, which runs a little faster on Macs, but won't work through Ollama unfortunately. (LM Studio maybe.)
Runs great on my M4 Macbook Pro w/128GB and likely also runs fine under 64GB... smaller memories might require lower quantizations.
I specifically like dealigned local models because if I have to get my thoughts policed when playing in someone else's playground, like hell am I going to be judged while messing around in my own local open-source one too. And there's a whole set of ethically-justifiable but rule-flagging conversations (loosely categorizable as things like "sensitive", "ethically-borderline-but-productive" or "violating sacred cows") that are now possible with this, and at a level never before possible until now.
Note: I tried to hook this one up to OpenClaw and ran into issues
To answer the obvious question- Yes, this sort of thing enables bad actors more (as do many other tools). Fortunately, there are far more good actors out there, and bad actors don't listen to rules that good actors subject themselves to, anyway.
c2k 9 hours ago [-]
I run mlx models with omlx[1] on my mac and it works really well.
Holy hell, how new is this? I've never heard of it, looks great!
nothinkjustai 3 hours ago [-]
It’s completely vibe coded, doesn’t even run on my Mac lol
barbazoo 8 hours ago [-]
> And there's a whole set of ethically-justifiable but rule-flagging conversations (loosely categorizable as things like "sensitive", "ethically-borderline-but-productive" or "violating sacred cows") that are now possible with this, and at a level never before possible until now.
I checked the abliterate script and I don't yet understand what it does or what the result is. What are the conversations this enables?
SL61 6 hours ago [-]
LLMs are very helpful for transcribing handwritten historical documents, but sometimes those documents contain language/ideas that a perfectly aligned LLM will refuse to output. Sometimes as a hard refusal, sometimes (even worse) by subtly cleaning up the language.
In my experience the latest batch of models are a lot better at transcribing the text verbatim without moralizing about it (i.e. at "understanding" that they're fulfilling a neutral role as a transcriber), but it was a really big issue in the GPT-3/4 era.
dolebirchwood 6 hours ago [-]
I have a project where I'm using LLMs to parse data from PDFs with a very complicated tabular layout. I've been using the latest Gemini models (flash and pro) for their strong visual reasoning, and they've generally been doing a really good job at it.
My prompt states that their job is to extract the text exactly as it appears in the PDF. One data point to be extracted is the race of each person listed. In one case, someone's race was "Indian". Gemini decided to extract it as "Native American". So ridiculous.
janalsncm 5 hours ago [-]
According to Gemini, Native America is the most populous country.
devmor 5 hours ago [-]
I was attempting to help someone who runs a small shop selling restored clothing set up a gemini pipeline that would restage images she took of clothing items with bad lighting, backgrounds, etc.
Basically anything that showed any “skin” on a mannequin it would refuse to interact with. Even just a top, unless she put pants on the mannequin.
It was infuriating.
spijdar 8 hours ago [-]
Realistically, a lot of people do this for porn.
In my experience, though, it's necessary to do anything security related. Interestingly, the big models have fewer refusals for me when I ask e.g. "in <X> situation, how do you exploit <Y>?", but local models will frequently flat out refuse, unless the model has been abliterated.
tredre3 6 hours ago [-]
From what I've seen gemma 4 doesn't refuse a lot regarding sex, it only needs little nudging in the right direction sometimes.
But it does refuse being critical of the usual topics: israel, islam, trans, or race.
So wanting to discuss one of those is the real reason people would use an uncensored model.
throwuxiytayq 8 hours ago [-]
The in-ter-net is for porn
rav3ndust 7 hours ago [-]
that song is going to be stuck in my head all day now. lol
golem14 3 hours ago [-]
That whole musical is just fantastic!
pmarreck 7 hours ago [-]
1) Coming up with any valid criticism of Islam at all (for some reason, criticisms of Christianity or Judaism are perfectly allowed even with public models!).
2) Asking questions about sketchy things. Simply asking should not be censored.
3) I don't use it for this, but porn or foul language.
4) Imitating or representing a public figure is often blocked.
5) Asking security-related questions when you are trying to do security.
6) For those who have had it, people who are trying to use AI to deal with traumatic experiences that are illegal to even describe.
Many other instances.
tshaddox 4 hours ago [-]
> Coming up with any valid criticism of Islam at all (for some reason, criticisms of Christianity or Judaism are perfectly allowed even with public models!).
When’s the last time you tried this? ChatGPT and Gemini have no trouble responding with all the common criticisms of Islam.
peyton 6 hours ago [-]
The manufacturing of biologics can be heavily censored to an absurd degree. I don’t know about Gemma 4 in particular.
pmarreck 5 hours ago [-]
Really? That's fascinating. Why is that?
eloisant 7 hours ago [-]
I tried it on my mac, for coding, and I wasn't really impressed compared to Qwen.
I guess there are things it's better at?
nkohari 7 hours ago [-]
You're comparing apples to oranges there. Qwen 3.5 is a much larger model at 397B parameters vs. Gemma's 31B. Gemma will be better at answering simple questions and doing basic automation, and codegen won't be it's strong suit.
kgeist 7 hours ago [-]
Qwen3.5 comes in various sizes (including 27B), and judging by the posts on HN, /LocalLlama etc., it seems to be better at logic/reasoning/coding/tool calling compared to Gemma 4, while Gemma 4 is better at creative writing and world knowledge (basically nothing changed from the Qwen3 vs. Gemma3 era)
Mil0dV 6 hours ago [-]
Does this also apply to gemma's 26B-A4B vs say Qwens 35B-A3B?
I'm not sure if I can make the 35B-A3B work with my 32GB machine
tredre3 7 hours ago [-]
Gemma 4 31B is still not impressive at coding compare to even Qwen 3.5 27B. It's just not its strong suit.
So far gemma 4 seems excellent at role playing, document analysis, and decent at making agentic decisions.
gigatexal 6 hours ago [-]
This has been my experience as well, Qwen via Ollama locally has been very very impressive.
magospietato 8 hours ago [-]
Haven't built anything on the agent skills platform yet, but it's pretty cool imo.
On Android the sandbox loads an index.html into a WebView, with standardized string I/O to the harness via some window properties. You can even return a rendered HTML page.
Definitely hacked together, but feels like an indication of what an edge compute agentic sandbox might look like in future.
bossyTeacher 7 hours ago [-]
>there's a whole set of ethically-justifiable but rule-flagging conversations (loosely categorizable as things like "sensitive", "ethically-borderline-but-productive" or "violating sacred cows") that are now possible with this, and at a level never before possible until now.
Mind giving us a few of the examples that you plan to run in your local LLM? I am curious.
pmarreck 4 hours ago [-]
I'm not sure what you're angling at but I already gave a set of questions that are ethically legitimate yet routinely censored by the public models:
Not to mention that doing what the big model makers do literally dumbs the model down.
They should at least allow something like letting you prove your age and identity to give you access to better/unaligned models, maybe even requiring a license of some sort. Because you know what? SOMEONE in there absolutely has access to the completely uncensored versions of the latest models.
satvikpendem 3 hours ago [-]
I tried 1 and a few others with hypothetical situations, public models answer perfectly fine it looks like.
3yr-i-frew-up 3 hours ago [-]
[dead]
jackp96 9 hours ago [-]
[flagged]
potsandpans 8 hours ago [-]
I'm tired of this concern trolling.
8 hours ago [-]
7 hours ago [-]
jackp96 8 hours ago [-]
[flagged]
karimf 8 hours ago [-]
This app is cool and it showcases some use cases, but it still undersells what the E2B model can do.
I just made a real-time AI (audio/video in, voice out) on an M3 Pro with Gemma E2B. I posted it on /r/LocalLLaMA a few hours ago and it's gaining some traction [0]. Here's the repo [1]
I'm running it on a Macbook instead of an iPhone, but based on the benchmark here [2], you should be able to run the same thing on an iPhone 17 Pro.
That's cool! You can add SoulX-FlashHead for real-time AI head animation as well if you want to simulate a teacher.
nothinkjustai 7 hours ago [-]
Parlor is so cool, especially since you’re offering it for free. And a great use case for local LLMs.
karimf 7 hours ago [-]
Thanks! Although, I can't claim any credit for it. I just spent a day gluing what other people have built. Huge props to the Gemma team for building an amazing model and also an inference engine that's focused for edge devices [0]
1) I am able to run the model on my iPhone and get good results. Not as good as Gemini in the cloud, but good.
2) I love the “mobile actions” tool calls that allow the LLM to turn on the flashlight, open maps, etc. It would be fun if they added Siri Shortcuts support. I want the personal automation that Apple promised but never delivered.
3) I am so excited for local models to be normalized. I build little apps for teachers and there are stringent privacy laws involved that mean I strongly prefer writing code that runs fully client-side when possible. When I develop apps and websites, I want easy API access to on-device models for free. I know it sort of exists on iOS and Chrome right now, but as far as I’m aware it’s not particularly good yet.
buzzerbetrayed 7 hours ago [-]
For me the hallucination and gaslighting is like taking a step back in time a couple of years. It even fails the “r’s in strawberry” question. How nostalgic.
It’s very impressive that this can run locally. And I hope we will continue to be able to run couple-year-old-equivalent models locally going forward.
dimmke 5 hours ago [-]
I haven't seen anybody else post it in this thread, but this is running on 8GB of RAM. It's not the full Gemma 4 32B model. It's a completely different thing from the full Gemma 4 experience if you were running the flagship model, almost to the point of being misleading.
It's their E2B and E4B variants (so 2B and 4B but also quantized)
The relevant constraint when running on a phone is power, not really RAM footprint. Running the tiny E2B/E4B models makes sense, this is essentially what they're designed for.
trvz 47 minutes ago [-]
It absolutely is RAM…
So much so that this was what made Apple increase their base sizes.
shtack 2 hours ago [-]
With reasoning on I found E4B to be solid, but E2B was completely unusable across several tests.
1f60c 5 hours ago [-]
Strangely, reasoning is not on by default. If you enable it, it answers as you'd expect.
janandonly 8 hours ago [-]
OP Here. It is my firm belief that the only realistic use of AI in the future is either locally on-device for almost free, or in the cloud but way more expensive then it is today.
The latter option will only bemusedly for tasks that humans are more expensive or much slower in.
This Gemma 4 model gives me hope for a future Siri or other with iPhone and macOS integration, “Her” (as in the movie) style.
crazygringo 7 hours ago [-]
> or in the cloud but way more expensive then it is today.
Why? It's widely understood that the big players are making profit on inference. The only reason they still have losses is because training is so expensive, but you need to do that no matter whether the models are running in the cloud or on your device.
If you think about it, it's always going to be cheaper and more energy-efficient to have dedicated cloud hardware to run models. Running them on your phone, even if possible, is just going to suck up your battery life.
mbesto 7 hours ago [-]
> It's widely understood that the big players are making profit on inference.
This is most definitely not widely understood. We still don't know yet. There's tons of discussions about people disagreeing on whether it really is profitable. Unless you have proof, don't say "this is widely understood".
igtt 3 hours ago [-]
The reality is we can’t trust accounting earnings anyway.
We need to see the cash flows.
zozbot234 7 hours ago [-]
The big players are plausibly making profits on raw API calls, not subscriptions. These are quite costly compared to third-party inference from open models, but even setting that up is a hassle and you as a end user aren't getting any subsidy. Running inference locally will make a lot of sense for most light and casual users once the subsidies for subscription access cease.
Also while datacenter-based scaleout of a model over multiple GPUs running large batches is more energy efficient, it ultimately creates a single point of failure you may wish to avoid.
janalsncm 5 hours ago [-]
> It's widely understood that the big players are making profit on inference.
If you add in the cost of training, it’s not profitable.
Not including the cost of training is a bit like saying the only cost of a cup of coffee is the paper cup it’s in. The only way OpenAI gets to charge for inference is by selling a product people can’t get elsewhere for much cheaper, which means billions in R&D costs. But because of competition, each model effectively has a “shelf life”.
tybit 2 hours ago [-]
At least Anthropic claims that they are profitable on a per model basis. But since both revenue and training costs are growing exponentially, and they need to pay for model N training today, and only get revenue for model N-1 today, the offset makes it look worse than it is.
Obviously that doesn’t help them turn a profit, until they can stop growing training costs exponentially.
So it’s really a race to see whether growth in revenue or training costs decelerates first.
6 hours ago [-]
huijzer 7 hours ago [-]
Laptop/desktop could work. Most systems are on charger most of time anyway
nothinkjustai 7 hours ago [-]
> It's widely understood that the big players are making profit on inference.
Are they? Or are they just saying that to make their offerings more attractive to investors?
Plus I think most people using agents for coding are using subscriptions which they are definitely not profitable in.
Locally running models that are snappy and mostly as capable as current sota models would be a dream. No internet connection required, no payment plans or relying on a third party provider to do your job. No privacy concerns. Etc etc.
nl 5 hours ago [-]
> Plus I think most people using agents for coding are using subscriptions which they are definitely not profitable in.
Where on earth do people get this idea? Subscriptions that are based around obscure, vendor defined "credits" are the perfect business model for vendors. They can change the amount you can use whenever they want.
It's likely they occasionally make a loss on some users but in general they are highly profitable for AI companies:
> Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025
and
> OpenAI projected a gross margin of around 46% in 2025, including inference costs of both paying and nonpaying ChatGPT users.
Both of those companies are losing hella money, dude just cuz they say they “expect” to be profitable doesn’t mean they are.
zozbot234 7 hours ago [-]
You can pick models that are snappy, or models that are as capable as SOTA. You don't really get both unless you spend extremely unreasonable amounts of money on what is essentially a datacenter-scale inference platform of your own, meant to service hundreds of users at once. (I don't care how many agent harnesses you spin up at once, you aren't going to get the same utilization as hundreds of concurrent users.)
This assessment might change if local AI frameworks start working seriously on support for tensor-parallel distributed inference, then you might get away with cheaper homelab-class hardware and only mildly unreasonable amounts of money.
jrflowers 7 hours ago [-]
> It's widely understood that the big players are making profit on inference.
I love the whole “they are making money if you ignore training costs” bit. It is always great to see somebody say something like “if you look at the amount of money that they’re spending it looks bad, but if you look away it looks pretty good” like it’s the money version of a solar eclipse
skybrian 5 hours ago [-]
The reason it matters is that if they are making a profit on inference, then when people use their services more, it cuts their losses. They might even break even eventually and start making a profit without raising the price.
But if they're losing money on inference, they will lose more money when people use their services more. There's no way to turn that around at that price.
_pdp_ 6 hours ago [-]
If you can run free models on consumer devices why do you think cloud providers cannot do the same except better and bundled with a tone of value worth paying?
amelius 7 hours ago [-]
A local model running on a phone owned and controlled by the vendor is still not really exciting, imho.
It may be physically "local" but not in spirit.
0dayman 8 hours ago [-]
this is not that first step towards your dream
kennywinker 8 hours ago [-]
Did you really watch “Her” and think this is a future that should happen??
Seriously????
jfreds 7 hours ago [-]
I don’t think OP’s point has anything to do with AI companions.
The big benefit of moving compute to edge devices is to distribute the inference load on the grid. Powering and cooling phones is a lot easier than powering and cooling a datacenter
kennywinker 3 hours ago [-]
Local ai is probably a good direction, i agree. But there was a part of their point that had to do with ai companions: the bit where they say we are closer to “her”-like ai companions. That was the bit i was responding to.
satvikpendem 4 hours ago [-]
What does what they said have anything to do with Her? Local LLMs are better than big corporations owning your data and offering LLMs for a huge cost.
kennywinker 3 hours ago [-]
I get the local ai thing. I agree it’s probably a good direction. The bit that has to do with the movie “her” is the bit at the end where they are excited about “her”-like companions on our phones.
sambapa 7 hours ago [-]
Torment Nexus sounds fun
kennywinker 3 hours ago [-]
Watch out! We got an info hazard here! Danger danger
aninteger 7 hours ago [-]
Having Scarlett Johansson's voice might not be so bad or even something less robotic.
Do you genuinely think a “frequency” is what makes a human voice recognizable?
That’s like using someone’s face in an app and then saying “how can you steal pixels?”
nothinkjustai 2 hours ago [-]
How can you steal pixels?
Or rather, what does “ownership” mean? What does it mean to own light waves? What does it mean to own sound waves? Etc
kennywinker 2 hours ago [-]
You can’t steal pixels or frequencies. But you can use someone’s image or their voice to sell your product without their permission.
You can get all existential about it if you want - I just know that if someone used my face or my voice to shill for a product without my permission i’d be pissed. I’m pretty sure you would be too.
nothinkjustai 59 minutes ago [-]
I’d be pissed if my code was used for training an AI too but that seems legal thus far…
8 hours ago [-]
esafak 6 hours ago [-]
Unfortunately, one man's dystopia is another's utopia.
I find it odd they are using the term “edge” to brand this, if it’s target is the general public.
I’ve been to a few tech conferences and saw the term used there for the first time. It took me a little bit to see the pattern and understand what it meant. I have never heard the term used outside of those circles. It seems like “local” would be the term average users would be familiar with. Normal people don’t call their stuff “edge devices”.
My son just started using 2B on his Android. I mentioned that it was an impressively compact model and next thing I knew he had figured out how to use it on his inexpensive 2024 Motorolla and was using it to practice reading and writing in foreign languages.
rudedogg 2 hours ago [-]
This is fun, FYI you don’t have to sign in/up with a Google account. I hesitated downloading it for that reason.
allpratik 7 hours ago [-]
Nice! Tried on iPhone 16 pro with 30 TPS from Gemma-4-E2B-it model.
Although the phone got considerably hot while inferencing. It’s quite an impressive performance and cannot wait to try it myself in one of my personal apps.
golem14 2 hours ago [-]
It's at least somewhat limited in non-English content. It knows how to make lentil soup, so I was happy that I never need to look up recipe sites with awful UX and ads, but then it couldn't find a recipe for "Kalter Hund"/"Kalte Schnauze". So sad ;)
Still, absolutely fabulous. What a time to be alive!
TGower 8 hours ago [-]
These new models are very impressive. There should be a massive speedup coming as well, AI Edge Gallery is running on GPU, but NPUs in recent high end processors should be much faster. A16 chip for example (Macbook Neo and iphone 16 series) has 35 TOPS of Neural Engine vs 7 TFLOPS gpu. Similar story for Qualcomm.
api 8 hours ago [-]
That’s nuts actually for such a low power chip. Can’t wait to see the M series version of that.
I’m sure very fast TPUs in desktops and phones are coming.
zozbot234 8 hours ago [-]
The Apple Silicon in the MacBook Neo is effectively a slimmed down version of M4, which is already out and has a very similar NPU (similar TFLOPS rating). It's worth noting however that the TFLOPS rating for Apple Neural Engine is somewhat artificial, since e.g. the "38 TFLOPS" in the M4 ANE are really 19 TFLOPS for FP16-only operation.
hadrien01 9 hours ago [-]
Is it me or does the App Store website look... fake? The text in the header ("Productiviteit", "Alleen voor iPhone") looks pixelated, like it was edited on Paint, the header background is flickering, the app icon and screenshots are very low quality, the title of the website is incomplete ("App Store voor iPho...")
It looks like there is some sort of glow effect on the text that isn't rendering right on your browser? It arguably doesn't have the best contrast, but seems to be as intended in Safari 26.3. Looks similar on Chrome macOS too: https://imgur.com/yq5PrKm.
t-sauer 8 hours ago [-]
Renders equally weird for me on Firefox on Windows 11. Firefox on MacOS looks good though.
This is also on Android and has an option to use AICore with the NPU which can run much faster than even the GPU models.
nout 3 hours ago [-]
How do you get it running on Android?
satvikpendem 3 hours ago [-]
It's the same app, Google AI edge gallery.
carbocation 8 hours ago [-]
It would be very helpful if the chat logs could (optionally) be retained.
nickvec 2 hours ago [-]
Extremely impressed by how fast responses are on iPhone 17 Pro Max. Can’t wait for this to be used for Siri’s brain one of these days (hopefully!)
burnto 8 hours ago [-]
My iPhone 13 can’t run most of these models. A decent local LLM is one of the few reasons I can imagine actually upgrading earlier than typically necessary.
Gigachad 56 minutes ago [-]
I’ve got a 17 pro and tbh I haven’t found any use for local models yet. They are a neat curiosity but the online ones are absolutely massively far ahead. Considering they are being given away for free currently, it’s hard to justify not making use of them over dumber local models.
deckar01 8 hours ago [-]
It doesn’t render Markdown or LaTeX. The scrolling is unusable during generation. E4B failed to correctly account for convection and conduction when reasoning about the effects of thermal radiation (31b was very good). After 3 questions in a session (with thinking) E4B went off the rails and started emitting nonsense fragment before the stated token limit was hit (unless it isn’t actually checking).
Sharmaji000 2 hours ago [-]
Still didnt release training recipe, data, methodology etc unlike deepseek. Mostly released to get developer ecosystem across their android built in ai. Still good and interesting, but not exactly philanthropic to the open source progress.
thot_experiment 6 hours ago [-]
Gemma 4 E4B is an incredible model for doing all the home assistant stuff I normally just used Qwen3.5 35BA4B + Whisper while leaving me with wayy more empty vram for other bullshit. It works as a drop in replacement for all of my "turn the lights off" or "when's the next train" type queries and does a good job of tool use. This is the really the first time vramlets get a model that's reliably day to day useful locally.
I'm curious/worried about the audio capability, I'm still using Whisper as the audio support hasn't landed in llama.cpp, and I'm not excited enough to temporarily rewire my stuff to use vLLM or whatever their reference impl is. The vision capabilities of Gemma are notably (thus far, could be impl specific issues?) much much worse than Qwen (even the big moe and dense gemma are much worse), hopefully the audio is at least on par with medium whisper.
neurostimulant 5 hours ago [-]
I'm able to sweet talk the gemma-4-e2b-it model in an iphone 15 to solve a hcaptcha screenshot. This small model is surprisingly very capable!
dwa3592 8 hours ago [-]
I think with this google starts a new race- best local model that runs on phones.
dwa3592 8 hours ago [-]
I wonder why the cut off date for 3n-E4B-it is Oct, 2023. That's really far in the past.
I assume it is the 26B A4B one, if it runs locally?
adrian17 6 hours ago [-]
No, only E2B and E4B.
rotexo 5 hours ago [-]
E4B is pretty good for extracting tables of items from receipt scans and inferring categories, wish this could be called from within a shortcut to just select a photo and add the extracted table to the clipboard
mc7alazoun 5 hours ago [-]
Would it work locally on a Mac Pro M4 24gb? If so I'd really appreciate a step-by-step guide.
weberer 4 hours ago [-]
These E2B and E4B models are very small so that they can fit into phones with around 8gb of RAM. You can get away with a much larger model. Just run:
brew install ollama
ollama run gemma4:26b-a4b-it-q4_K_M
8 hours ago [-]
Waterluvian 5 hours ago [-]
I see a phenomenal opportunity for old phone re-use by arraying them in some dock and making them be my "home AI."
tithos 4 hours ago [-]
Most of the models are not available. I’m guessing they will become available soon enough… At least I hope.
rickdg 8 hours ago [-]
How do these compare to Apple's Foundation Models, btw?
simonw 7 hours ago [-]
So much better. Hard to quantify, but even the small Gemma 4 models have that feels-like-ChatGPT magic that Apple's models are lacking.
snarkyturtle 7 hours ago [-]
AFM had a 4096 token context window and this can be configured to have a 32k+ token context window, for one.
garff 7 hours ago [-]
How new of an iPhone model is needed?
__natty__ 8 hours ago [-]
That's a great project! I just wondered whether Google would have a problem with you using their trademark
tech234a 7 hours ago [-]
This is an app published by Google itself
dzhiurgis 7 hours ago [-]
I recently got to a first practical use of it. I was on a plane, filling landing card (what a silly thing these are). I looked up my hotel address using qwen model on my iPhone 16 Pro. It was accurate. I was quite impressed.
After some back and forth the chat app started to crash tho, so YMMV.
beeflet 7 hours ago [-]
Isn't this already possible in a much more open-ended way with PocketPal?
I don't normally do the whole "abliterated" thing (dealignment) but after discovering https://github.com/p-e-w/heretic , I was too tempted to try it with this model a couple days ago (made a repo to make it easier, actually) https://github.com/pmarreck/gemma4-heretical and... Wow. It worked. And... Not having a built-in nanny is fun!
It's also possible to make an MLX version of it, which runs a little faster on Macs, but won't work through Ollama unfortunately. (LM Studio maybe.)
Runs great on my M4 Macbook Pro w/128GB and likely also runs fine under 64GB... smaller memories might require lower quantizations.
I specifically like dealigned local models because if I have to get my thoughts policed when playing in someone else's playground, like hell am I going to be judged while messing around in my own local open-source one too. And there's a whole set of ethically-justifiable but rule-flagging conversations (loosely categorizable as things like "sensitive", "ethically-borderline-but-productive" or "violating sacred cows") that are now possible with this, and at a level never before possible until now.
Note: I tried to hook this one up to OpenClaw and ran into issues
To answer the obvious question- Yes, this sort of thing enables bad actors more (as do many other tools). Fortunately, there are far more good actors out there, and bad actors don't listen to rules that good actors subject themselves to, anyway.
[1] https://github.com/jundot/omlx
I checked the abliterate script and I don't yet understand what it does or what the result is. What are the conversations this enables?
In my experience the latest batch of models are a lot better at transcribing the text verbatim without moralizing about it (i.e. at "understanding" that they're fulfilling a neutral role as a transcriber), but it was a really big issue in the GPT-3/4 era.
My prompt states that their job is to extract the text exactly as it appears in the PDF. One data point to be extracted is the race of each person listed. In one case, someone's race was "Indian". Gemini decided to extract it as "Native American". So ridiculous.
Basically anything that showed any “skin” on a mannequin it would refuse to interact with. Even just a top, unless she put pants on the mannequin.
It was infuriating.
In my experience, though, it's necessary to do anything security related. Interestingly, the big models have fewer refusals for me when I ask e.g. "in <X> situation, how do you exploit <Y>?", but local models will frequently flat out refuse, unless the model has been abliterated.
But it does refuse being critical of the usual topics: israel, islam, trans, or race.
So wanting to discuss one of those is the real reason people would use an uncensored model.
2) Asking questions about sketchy things. Simply asking should not be censored.
3) I don't use it for this, but porn or foul language.
4) Imitating or representing a public figure is often blocked.
5) Asking security-related questions when you are trying to do security.
6) For those who have had it, people who are trying to use AI to deal with traumatic experiences that are illegal to even describe.
Many other instances.
When’s the last time you tried this? ChatGPT and Gemini have no trouble responding with all the common criticisms of Islam.
I guess there are things it's better at?
I'm not sure if I can make the 35B-A3B work with my 32GB machine
So far gemma 4 seems excellent at role playing, document analysis, and decent at making agentic decisions.
On Android the sandbox loads an index.html into a WebView, with standardized string I/O to the harness via some window properties. You can even return a rendered HTML page.
Definitely hacked together, but feels like an indication of what an edge compute agentic sandbox might look like in future.
Mind giving us a few of the examples that you plan to run in your local LLM? I am curious.
https://news.ycombinator.com/item?id=47654013
Not to mention that doing what the big model makers do literally dumbs the model down.
They should at least allow something like letting you prove your age and identity to give you access to better/unaligned models, maybe even requiring a license of some sort. Because you know what? SOMEONE in there absolutely has access to the completely uncensored versions of the latest models.
I just made a real-time AI (audio/video in, voice out) on an M3 Pro with Gemma E2B. I posted it on /r/LocalLLaMA a few hours ago and it's gaining some traction [0]. Here's the repo [1]
I'm running it on a Macbook instead of an iPhone, but based on the benchmark here [2], you should be able to run the same thing on an iPhone 17 Pro.
[0] https://www.reddit.com/r/LocalLLaMA/comments/1sda3r6/realtim...
[1] https://github.com/fikrikarim/parlor
[2] https://huggingface.co/litert-community/gemma-4-E2B-it-liter...
[0] https://github.com/google-ai-edge/LiteRT-LM
1) I am able to run the model on my iPhone and get good results. Not as good as Gemini in the cloud, but good.
2) I love the “mobile actions” tool calls that allow the LLM to turn on the flashlight, open maps, etc. It would be fun if they added Siri Shortcuts support. I want the personal automation that Apple promised but never delivered.
3) I am so excited for local models to be normalized. I build little apps for teachers and there are stringent privacy laws involved that mean I strongly prefer writing code that runs fully client-side when possible. When I develop apps and websites, I want easy API access to on-device models for free. I know it sort of exists on iOS and Chrome right now, but as far as I’m aware it’s not particularly good yet.
It’s very impressive that this can run locally. And I hope we will continue to be able to run couple-year-old-equivalent models locally going forward.
It's their E2B and E4B variants (so 2B and 4B but also quantized)
https://ai.google.dev/gemma/docs/core/model_card_4#dense_mod...
So much so that this was what made Apple increase their base sizes.
The latter option will only bemusedly for tasks that humans are more expensive or much slower in.
This Gemma 4 model gives me hope for a future Siri or other with iPhone and macOS integration, “Her” (as in the movie) style.
Why? It's widely understood that the big players are making profit on inference. The only reason they still have losses is because training is so expensive, but you need to do that no matter whether the models are running in the cloud or on your device.
If you think about it, it's always going to be cheaper and more energy-efficient to have dedicated cloud hardware to run models. Running them on your phone, even if possible, is just going to suck up your battery life.
This is most definitely not widely understood. We still don't know yet. There's tons of discussions about people disagreeing on whether it really is profitable. Unless you have proof, don't say "this is widely understood".
We need to see the cash flows.
Also while datacenter-based scaleout of a model over multiple GPUs running large batches is more energy efficient, it ultimately creates a single point of failure you may wish to avoid.
If you add in the cost of training, it’s not profitable.
Not including the cost of training is a bit like saying the only cost of a cup of coffee is the paper cup it’s in. The only way OpenAI gets to charge for inference is by selling a product people can’t get elsewhere for much cheaper, which means billions in R&D costs. But because of competition, each model effectively has a “shelf life”.
Obviously that doesn’t help them turn a profit, until they can stop growing training costs exponentially.
So it’s really a race to see whether growth in revenue or training costs decelerates first.
Are they? Or are they just saying that to make their offerings more attractive to investors?
Plus I think most people using agents for coding are using subscriptions which they are definitely not profitable in.
Locally running models that are snappy and mostly as capable as current sota models would be a dream. No internet connection required, no payment plans or relying on a third party provider to do your job. No privacy concerns. Etc etc.
Where on earth do people get this idea? Subscriptions that are based around obscure, vendor defined "credits" are the perfect business model for vendors. They can change the amount you can use whenever they want.
It's likely they occasionally make a loss on some users but in general they are highly profitable for AI companies:
> Anthropic last month projected it would generate a 40% gross profit margin from selling AI to businesses and application developers in 2025
and
> OpenAI projected a gross margin of around 46% in 2025, including inference costs of both paying and nonpaying ChatGPT users.
https://archive.is/aKFYZ#selection-1075.0-1083.119
This assessment might change if local AI frameworks start working seriously on support for tensor-parallel distributed inference, then you might get away with cheaper homelab-class hardware and only mildly unreasonable amounts of money.
I love the whole “they are making money if you ignore training costs” bit. It is always great to see somebody say something like “if you look at the amount of money that they’re spending it looks bad, but if you look away it looks pretty good” like it’s the money version of a solar eclipse
But if they're losing money on inference, they will lose more money when people use their services more. There's no way to turn that around at that price.
It may be physically "local" but not in spirit.
Seriously????
The big benefit of moving compute to edge devices is to distribute the inference load on the grid. Powering and cooling phones is a lot easier than powering and cooling a datacenter
That’s like using someone’s face in an app and then saying “how can you steal pixels?”
Or rather, what does “ownership” mean? What does it mean to own light waves? What does it mean to own sound waves? Etc
You can get all existential about it if you want - I just know that if someone used my face or my voice to shill for a product without my permission i’d be pissed. I’m pretty sure you would be too.
Also on Android: https://play.google.com/store/apps/details?id=com.google.ai....
It's a demo app for Google's Edge project: https://ai.google.dev/edge
Saw this one on X the other day updated with Gemma 4 and they have the built-in Apple Foundation model, Qwen3.5, and other models:
Locally AI - https://locallyai.app/
I’ve been to a few tech conferences and saw the term used there for the first time. It took me a little bit to see the pattern and understand what it meant. I have never heard the term used outside of those circles. It seems like “local” would be the term average users would be familiar with. Normal people don’t call their stuff “edge devices”.
> We collect information about your activity in our services
Source: https://policies.google.com/privacy#infocollect
[0] https://github.com/google-ai-edge/gallery
Although the phone got considerably hot while inferencing. It’s quite an impressive performance and cannot wait to try it myself in one of my personal apps.
Still, absolutely fabulous. What a time to be alive!
I’m sure very fast TPUs in desktops and phones are coming.
The design quality is still poor. But that's the new Apple. Design is no longer one of their core strengths.
If you just go to https://apps.apple.com/ it does look better, but I agree, still a bit "off".
On my iPhone it opens on the App Store app, so it looks fine to me.
Screenshot of the header: https://i.imgur.com/4abfGYF.png
Edit: Seems like mix-blend-mode: plus-lighter is bugged in Firefox on Windows https://jsfiddle.net/bjg24hk9/
I'm curious/worried about the audio capability, I'm still using Whisper as the audio support hasn't landed in llama.cpp, and I'm not excited enough to temporarily rewire my stuff to use vLLM or whatever their reference impl is. The vision capabilities of Gemma are notably (thus far, could be impl specific issues?) much much worse than Qwen (even the big moe and dense gemma are much worse), hopefully the audio is at least on par with medium whisper.
I assume it is the 26B A4B one, if it runs locally?
After some back and forth the chat app started to crash tho, so YMMV.
https://github.com/a-ghorbani/pocketpal-ai
https://apps.apple.com/us/app/pocketpal-ai/id6502579498
https://play.google.com/store/apps/details?id=com.pocketpala...