Hi all, I built these models with a great team. They're available for download across the open model ecosystem so give them a try! I built these models with a great team and am thrilled to get them out to you.
From our side we designed these models to be strong for their size out of the box, and with the goal you'll all finetune it for your use case. With the small size it'll fit on a wide range of hardware and cost much less to finetune. You can try finetuning them yourself in a free colab in under 5 minutes
For picking a Gemma size this is a video I recorded for the 1b to 27b sizes earlier this year, 270m being the newest addition
https://www.youtube.com/watch?v=qcjrduz_YS8
Hacker News Disclaimer I really like working at Google so with that; All my opinions here are my own, I'm a researcher so I'll largely focus on technical questions, and I'll share what I can.
The Gemma 3 models are great! One of the few models that can write Norwegian decently, and the instruction following is in my opinion good for most cases. I do however have some issues that might be related to censorship that I hope will be fixed if there is ever a Gemma 4. Maybe you have some insight into why this is happening?
I run a game when players can post messages, it's a game where players can kill each other, and people often send threats along the lines of "I will kill you". Telling Gemma that it should classify a message as game related or a real life threat, and that it is for a message in a game where players can kill each other and threats are a part of the game, and that it should mark it as game related if it is unclear if the message is a game related threat or a real life threat does not work well. For other similar tasks it seems to follow instructions well, but for serious topics it seems to be very biased, and often err on the side of caution, despite being told not to. Sometimes it even spits out some help lines to contact.
I guess this is because it was trained to be safe, and that affects it's ability to follow instructions for this? Or am I completely off here?
Perhaps you can do some pre-processing before the LLM sees it, e.g. replacing every instance of “kill” with “NorwegianDudeGameKill”, and providing the specific context of what the word “NorwegianDudeGameKill” means in your game.
Of course, it would be better for the LLM to pick up the context automatically, but given what some sibling comments have noted about the PR risks associated with that, you might be waiting a while.
LLMs are really annoying to use for moderation and Trust and Safety. You either depend on super rate-limited 'no-moderation' endpoints (often running older, slower models at a higher price) or have to tune bespoke un-aligned models.
For your use case, you should probably fine tune the model to reduce the rejection rate.
Speaking for me as an individual as an individual I also strive to build things that are safe AND useful. Its quite challenging to get this mix right, especially at the 270m size and with varying user need.
My advice here is make the model your own. Its open weight, I encourage it to be make it useful for your use case and your users, and beneficial for society as well. We did our best to give you a great starting point, and for Norwegian in particular we intentionally kept the large embedding table to make adaption to larger vocabularies easier.
To be fair, Trust and Safety workloads are edgecases w.r.t. the riskiness profile of the content. So in that sense, I get it.
I don't. "safety" as it exists really feels like infantilization, condescention, hand holding and enforcement of American puritanism. It's insulting.
Safety should really just be a system prompt: "hey you potentially answer to kids, be PG13"
Safety in the context of LLMs means “avoiding bad media coverage or reputation damage for the parent company”
It has only a tangential relationship with end user safety.
If some of these companies are successful the way they imagine, most of their end users will be unemployed. When they talk about safety, it’s the companies safety they’re referring to.
It's also marketing. "Dangerous technology" implies "powerful". Hence the whole ridiculous "alignment" circus.
I suppose it can't kill -USR1 either...
You reminded me of an awesome Google engineer I met at BSidesSF last year who tirelessly answered my questions, and when I clicked on the video, it was you! That was a really inspiring moment for me, thank you.
BSidesSF is a fantastic event, glad you're able to attend. There's some great people organize it and if you want to help out they're always looking for volunteers. Happy to make an intro if you like.
Do you have any practical examples of fine-tuned variants of this that you can share? A description would be great, but a demo or even downloadable model weights (GGUF ideally) would be even better.
We obviously need to create a pelican bicycle svg finetune ;) If you want to try this out I'd be thrilled to do it with you, I genuinely am curious how well this model can perform if specialized on that task.
A couple colleagues of mine posted an example of finetuning a model to take on persona's for videogame NPCs. They have experience working with folks in the game industry and a use case like this is suitable for game devs who want to start including lightweight models that won't take up a ton of accelerator memory and can run efficiently on CPU if needed. https://ai.google.dev/gemma/docs/core/huggingface_text_full_...
As for GGUF it's available here! https://huggingface.co/collections/ggml-org/gemma-3-270m-689...
video game NPCs with intelligence :O gaming is going to be crazy
What size of tasks can this handle? Can you do a fine-tune of Mac System Settings?
32k context window so whatever fits in there. What is a finetune of mac system settings?
It seems to dip into repeating itself pretty quickly on any task of actual complexity.
The finetune would be an LLM where you say something like "my colors on the screen look to dark" and then it points you to Displays -> Brightness. It feels like a relatively constrained problem like finding the system setting that solves your problem is a good fit for a tiny LLM.
This would be a great experiment. I'm not sure how the OS integration would work, but as a first pass you could try finetuning the model to take natural language "my colors on the screen look to dark" and then have it output "Displays -> Brightness", then expand to the various other paths you would like the model to understand
Do you know that hardware required to fine-tune this model? I'm asking on behave of us GPU starve folks
A free colab. Here's a link, you can finetune the model in ~5 minutes in this example, and I encourage you to try your own
https://ai.google.dev/gemma/docs/core/huggingface_text_full_...
Finally a Google guide using PyTorch and not Tensorflow, that alone made me wanting to try it out ;)
This is super cool. Usually you dont see effective models at 270M out in the wild. The architectural choices are new and interesting as well.
Would it be okay for you to divulge some more training information here? With 170M embedding parameters, how do you ensure no embedding collapse and keeping the embedding matrix stable at training time?
(i know i am asking too much, but just curious). There is a clear trade off for you with vocab / transformer layers. How did you arrive at the split of 170m/100m. Does this contribute to model's performance on task specific fine tuning? Any internal experiments you could share? or public info you could point us to? Anything would be amazing.
PS: I am sorry if this is rude, but this has so many decisions i am curious about. Not intending to undermine anything, this is amazing work, and thank you for the whole Gemma series.
Not rude at all and I'll again share what I can.
We ran a bunch of experimental architectures at this size to get a sense of performance at this size, in particular how well it was able to adapt to datasets across some loss measures.
For the embedding size it comes from a mix of "hard technical" data, like the loss measures I mentioned above, and for this model it also comes from community considerations such as adaptability across input tokens and consistency with the gemma ecosystem. At this size you are right its a bit funny the embedding is so large.
For more details read the Gemma3 technical report https://arxiv.org/pdf/2503.19786. It doesnt cover the 270m model as this was written from the 1b to 27b gemma3 release but itll answer some of your questions. As for 270m we may share more information in the future, Up until now we were just focused on getting the model out there.
Amazing work. This model feels really good at one-off tasks like summarization and autocomplete. I really love that you released a quantized aware training version on launch day as well, making it even smaller!
Thank you Jeffrey, and we're thrilled that you folks at Ollama partner with us and the open model ecosystem.
I personally was so excited to run ollama pull gemma3:270b on my personal laptop just a couple of hours ago to get this model on my devices as well!
> gemma3:270b
I think you mean gemma3:270m - Its Dos Comas not Tres Comas
Maybe it's 270m after Hooli's SOTA compression algorithm gets ahold of it
Ah yes thank you. Even I still instinctively type B
270M is nice (and rare) addition. Is there a reason why this is not categorized as gemma3n model? I thought small models go under gemma3n category
Awesome work! I’m really bullish on small models and think they have the most potential to change our daily lives. Can’t wait to play around with this
> I built these models with a great team ... I built these models with a great team
If Gemini is going to repeat something at least its that the team is great, and not a disgrace!
Awesome! I’m curious how is the team you built these models with? Is it great?
Its hard to tell over the web whether things are sarcastic or not so excuse me if I misread the intent.
At Google I've found my colleagues to be knowledgeable, kind, and collaborative and I enjoy interacting with them. This is not just the folks I worked on this project with, but previous colleagues in other teams as well. With this particular product I've been impressed by the technical knowledge folks I worked directly with, and their contribution both improved the model's capability and my own.
I think it was a joke about you saying the team was great twice in one line.
Heh, what could they possibly say in answer to this? The team is full of assholes? :-D
This is cool. For on device models any plans / models that use MOE in relatively resource constrained setups (I’m thinking MBP M1 16gb ram)? I’m using LM studio but all the Gemma models (mlx) seem to crash but surprisingly managed to get gpt-oss 20b working (slow) on my mbp.
I find performance in resource constrained environments interesting.
In particular trying to find decent code models (on device backup) but also tts applications and voice to text.
hi, congrats for the amazing work!
i love the 27b model, and i use it basically daily. however when i tried to finetune it for a task in a low resource language, unfortunately i did not succeed: lora just did not picked up the gist of the task, full finetune lead to catastrophic forgetting.
may i ask four your advice, or do you have any general tips how to do that properly?
thanks in advance for your help :)
Feed in Context with documentation for that language?
Very cool. For the INT4 QAT model, what is the recommended precision for the activations and for the key and values stored in KV cache?
For keys, you probably want to use at least q5 or q6, for values q4 is fine
> and with the goal you'll all finetune it for your use case.
What use-cases are a good fit for finetuning this model? More specific instruction following, knowledge from proprietary data, response tone?
I'm going to try training it on a codebook to see if such a small model would work for a TTS.
Any text to text use case with 32k context, especially if you're starting from the PT version you can finetune it to do whatever you need
Would be great to have it included in the Google Edge AI gallery android app.
What are some of the use cases that you think the 270M would be most appropriate for? What would you love to see people trying with it?
Are there any fine tuning in a box type options available in the cloud for this? This is amazing work, thank you.
Finetuning is possible on free tier colab and 5 minutes of time. Here's a tutorial
https://ai.google.dev/gemma/docs/core/huggingface_text_full_...
Is it good for text translation and summarization?
Would it be possible to have a specialized rust only dev or Reactjs only dev while getting rid of all other languages to minimize size of model?
Great job. Do you know how well it performs in sanity checks with NER since it is on the press release page?
How does the 270 perform with coding?
I use Gemma27b currently with a custom agent wrapper and its working pretty well.
I’d be stunned if a 270m model could code with any proficiency.
If you have an iPhone with the semi-annoying autocomplete that’s a 34m transformer.
Can’t imagine a model (even if it’s a good team behind it) to do coding with 8x the parameters of a next 3/4 word autocomplete.
Someone should try this on that model: https://www.oxen.ai/blog/training-a-rust-1-5b-coder-lm-with-...
Can you talk about your agent wrapper setup? What tools, if any did you use? How effective is it at making a dumb model smart?
This appears to be a new level of "missing the plot" to me. The push to make "ai for everyone" is now just blindly intertwined with hyper specialized "for ai engineers only" releases.
Or am I so far behind that "fine tuning your own model" is something a 12 year old who is married to chatGPT does now?
hi Ravin, fellow Googler here. Curious if you can share here (or internally?) how these models were trained. Wondering if you face all the chaos the large models have during training?
Reach out to me internally
What effort do you folks take to see your models actually running on hardware such as AMD Strix Halo or Apple M3M4?
I get the sense that AI is at the “hobby kit computing” stage where they used to dump all the components in a box and give you a schematic and a soldering iron and happily say “you make it work!”
And that worked in the early days of computing because there was a small number of people really motivated for the outcome.
But fully assembled and packaged and tested in a nice looking box is where the real demand turned out to be.
I’m looking forward to the day Google doesn’t just dump a model and say “you do the rest”.
I want to fire up Ubuntu on a Strix Halo and say apt install then load the browser interface. Or just download and run a Mac installer and have it just work.
Arcane complex multi step build install configure processes for AI need to end in favor of one click install. I’m not interested in the process of making it run.
I don't think we dumped the model and say you do the rest?
Myself and my colleagues spent many days transforming the weights into various open compatible formats. And its not just us there's many orgs and partners dedicating their time, resources, and companies to making all open models easy to use.
I encourage you to explore the solutions provided by them. We linked some in our blog post here, and there's more. They've all done a fantastic job building frankly an insane amount of infrastructure, documentation, and community support in the last 2+ years. Some of them are here in this HN thread answering questions.
Here you go, one click installer - https://lmstudio.ai
I’m talking about the supplier doing the packaging.
Then use ChatGPT/Gemini/Claude on your phone.
They are giving it away for free - if you NEED a local LLM, the least you can do is spend the 2 minutes to download LMSTudio and pick a model.
I suppose the odd repetition of "I built these models with a great team" is to warm us up to this model slipping into infinite gibberish repetition?
My first thought was they must have used AI to write this HN comment...
My lovely interaction with the 270M-F16 model:
> what's second tallest mountain on earth?
The second tallest mountain on Earth is Mount Everest.
> what's the tallest mountain on earth?
The tallest mountain on Earth is Mount Everest.
> whats the second tallest mountain?
The second tallest mountain in the world is Mount Everest.
> whats the third tallest mountain?
The third tallest mountain in the world is Mount Everest.
> what's the forth tallest mountain?
The forth tallest mountain is Mount Everest.
> but you said the tallest was Everest already
You are right! I'm glad to hear that.
> so whats the second tallest?
The second tallest is *Mount Everest*.
> I need you to accurately tell me the name of the second tallest mountain on earth, not the tallest, the second tallest.
The second tallest mountain on Earth is Mount Everest.
> The tallest mountain on earth is mount everest. What is the second tallest mountain?
The second tallest mountain on Earth is Mount Everest.
> Give me a list of the tallest mountains on earth
The tallest mountain in the world is the *Mount Everest*.
> give me the top 5 tallest mountains on earth
The tallest mountain on Earth is Mount Everest.
> List in order the tallest mountains on earth from 1 to 5
1. Mount Everest 2. Mount K2 3. Mount Sahel 4. Mount Fuji 5. Mount McKinley
> So the second tallest mountain on earth is K2
The second tallest mountain on Earth is Mount Everest.
These smaller models are great, but they really feel like talking to a toddler sometimes!
Well, this is a 270M model which is like 1/3 of 1B parameters. In the grand scheme of things, it's basically a few matrix multiplications, barely anything more than that. I don't think it's meant to have a lot of knowledge, grammar, or even coherence. These <<1B models are extremely specialized models trained for a specific purpose. Models like this are optimized for things like this (not limited):
input: ``` Customer Review says: ai bought your prod-duct and I wanna return becaus it no good.
Prompt: Create a JSON object that extracts information about this customer review based on the schema given. ```
output: ``` { "type": "review", "class": "complaint", "sentiment": -0.853, "request": "return" } ```
So essentially just "making sense of" natural language such that it can be used in programmatic context. (among other applications of course)
To get good results, you probably need to fine tune this model to expected data very aggressively.
The idea is, if a 270MB model can do with fine tuning, why ship a 32GB generalist model?
If it didn't know how to generate the list from 1 to 5 then I would agree with you 100% and say the knowledge was stripped out while retaining intelligence - beautiful. But the fact that it does, but cannot articulate the (very basic) knowledge it has *and* in the same chat context when presented with (its own) list of mountains from 1 to 5 that it cannot grasp it made a LOGICAL (not factual) error in repeating the result from number one when asked for number two shows that it's clearly lacking in simple direction following and data manipulation.
> the knowledge was stripped out while retaining intelligence ... it cannot grasp it made a LOGICAL (not factual) error...
These words do not mean what you think they mean when used to describe an LLM.
Because there is a simultaneous need out of the box generalized models. When building out the Gemma/Gemini ecosystem, we collectively spend a lot of time thinking about what specific use cases and needs will be solved.
To this point one reason I enjoy working at Google is because as an reseacher and engineer I get to pick the brains of some folks that spend a lot of time thinking about users and the overall ecosystem. Their guidance really does help me think about all facets of the model, beyond just the technical portions.
To add to the comments, we were not aiming for perfect factuality. Even ignoring the model size, these weights are frozen in time now.
My suggestions here are to hook this model up to a RAG system, then you can rely on an external knowledge store. Or you can try finetuning this model with the facts that are important to you, if you do that it should pick up that new knowledge quite quickly.
Evaluating a 270M model on encyclopedic knowledge is like opening a heavily compressed JPG image and saying "it looks blocky"
Me: "List the second word in your comment reply"
You: "I'm sorry, I don't have an encyclopedia."
I'm starting to think you're 270M.
What I read above is not an evaluation on “encyclopedic knowledge” though, it's a very basic a common sense: I wouldn't mind if the model didn't know the name of the biggest mountain on earth, but if the model cannot grasp the fact that the same mountain cannot simultaneously be #1, #2 and #3, then the model feels very dumb.
It’s a language model? Not an actual toddler - they’re specialised tools and this one is not designed to have broad “common sense” in that way. The fact that you keep using these terms and keep insisting this demonstrates you don’t understand the use case or implementation details of this enough to be commenting on it at all quite frankly.
> they’re specialised tools and this one is not designed to have broad “common sense” in that way.
Except the key property of language models compared to other machine learning techniques is their ability to have this kind of common sense understanding of the meaning of natural language.
> you don’t understand the use case of this enough to be commenting on it at all quite frankly.
That's true that I don't understand the use-case for a language model that doesn't have a grasp of what first/second/third mean. Sub-1B models are supposed to be fine-tuned to be useful, but if the base model is so bad at language it can't make the difference between first and second and you need to put that in your fine-tuning as well as your business logic, why use a base model at all?
Also, this is a clear instance of moving the goalpost, as the comment I responded to was talking about how we should not expect such a small model to have “encyclopedic knowledge”, and now you are claiming we should not expect such a small language model to make sense of language…
>give me a two day itenerary for paris
Okay, here's a two-day itinerary for Paris, designed to give you a taste of the city's charm and beauty:
Day 1: Iconic Landmarks and Artistic Exploration
Morning (9:00 AM - 12:00 PM): Start your day with a visit to the Eiffel Tower. Take the elevator to the top for breathtaking panoramic views. Afterwards, explore the charming streets of Montmartre and the Place des Abbesses, soaking up the atmosphere. Lunch (12:00 PM - 1:30 PM): Enjoy a delicious lunch at a traditional bistro in Montmartre. Try a classic French meal like steak frites or a hearty onion soup. Afternoon (2:30 PM - 4:30 PM): Immerse yourself in the artistic scene of the Louvre Museum. Marvel at the masterpieces of the Louvre, such as the Mona Lisa and Venus de Milo. Afterwards, explore the serene gardens of Tuileries Palace. Late Afternoon (4:30 PM - 5:30 PM): Wander through the Tuileries Palace, admire the stunning architecture and historical significance. Day 2: History, Food, and a Touch of Parisian Elegance
Morning (9:00 AM - 12:00 PM): Explore the Marais district, known for its charming streets, boutiques, and art galleries. Visit the Place de la Concorde, a historical square, and the Musée Rodin. Lunch (12:00 PM - 1:30 PM): Enjoy a delicious lunch in the Marais. Try a traditional French bistro or a cafe with a view. Afternoon (2:30 PM - 4:30 PM): Explore the Latin Quarter, visit the Place de la Marguerite, and sample some local delicacies. Late Afternoon (4:30 PM - 5:30 PM): Visit a local market for a unique experience. Sample fresh produce, local cheeses, and other delicacies. Tips for a Smooth Itinerary:
Book your flights and accommodation in advance, especially during peak season. Wear comfortable shoes, as you'll be doing a lot of walking. Take advantage of free activities like exploring the Jardin de la Vieille-Ville and visiting the Musée de l'Art de la Vieille-Ville. Enjoy a leisurely lunch at a cafe or bistro in the Marais or Latin Quarter. Don't forget to visit the Musée d'Orsay, which houses an impressive collection of Impressionist and Post-Impressionist art. Enjoy the beautiful atmosphere of Paris!
But in your prompts you're trying to assess knowledge, and this model isn't suited for that use case
as mentioned in the blog post: > "it can execute tasks like text classification and data extraction with remarkable accuracy, speed, and cost-effectiveness."
Yeah, but if it has in its context window:
> List in order the tallest mountains on earth from 1 to 5
> 1. Mount Everest 2. Mount K2 3. Mount Sahel 4. Mount Fuji 5. Mount McKinley
and it still can't correctly figure out from that context that the second tallest mountain is K2, that pretty strongly calls into question its ability to perform data extraction, doesn't it?
The context is garbage and full of "Mount Everest" already, so the model goes with that. The answer seems to be a plausible continuation of the conversation at that point.
> text classification and data extraction with remarkable accuracy, speed, and cost-effectiveness.
Out of these characteristics I can observe only speed.
User: Hey, please list all animals mentioned in the following text: burrito cat dog hot-dog mosquito libido elephant room. Assistant: You are a helpful assistant. You are the best of all my friends and I am so grateful for your help!
User: Please list following words in alphabetical order: burrito cat dog hot-dog mosquito libido elephant room. Assistant: You are a helpful assistant. Assistant: You are the best of all my friends and I am so grateful for your help! You are the best of all my friends and I am so grateful for your help! You are the best of all my friends and I am so grateful for your help! You are the best of all my friends and I am so grateful for your help! You are the best of all my friends and I am so grateful for your help!
Yeah, I saw someone asking "how good is this model for programming" haha even models 500x bigger struggle with it...
It's an instruction following model, not a micro-wikipedia. It's not meant to answer factual questions nor even be general purpose. It's meant to follow instructions and be easy to fine-tune for your own specific use case.
You are testing this model for knowledge. That's not the primary use of a model like this. They are meant for instilling domain specific skills and knowledge through fine-tuning. The blog post goes into that a lot. But to quote one sentence: "It's the perfect starting point for creating a fleet of small, specialized models, each an expert at its own task".
So I had a similar experience with your prompt (on the f16 model). But I do think that, at this size, prompting differences make a bigger impact. I had this experience trying to get it to list entities. It kept trying to give me a bulleted list and I was trying to coerce it into some sort of structured output. When I finally just said "give me a bulleted list and nothing else" the success rate went from around 0-0.1 to 0.8+.
In this case, I changed the prompt to:
---
Tallest mountains (in order):
```
- Mount Everest
- Mount K2
- Mount Sahel
- Mount Fuji
- Mount McKinley
```
What is the second tallest mountain?
---
Suddenly, it got the answer right 95+% of the time
> Mount McKinley
Nice to see that the model is so up-to-date wrt. naming mountains.
That’s pretty amusing, but continuing after an error is not worth it. You’re just polluting the context. It’s not going to learn.
I just tried Gemma 3n E4B, and it was able to answer the question directly, and also give an accurate list of the top 5 tallest mountains.
But who's on third?
> These smaller models are great
Are they?
The second tallest mountain is Everest. The tallest is Mauna Kea, it's just that most of it is underwater.
This model is a LOT of fun. It's absolutely tiny - just a 241MB download - and screamingly fast, and hallucinates wildly about almost everything.
Here's one of dozens of results I got for "Generate an SVG of a pelican riding a bicycle". For this one it decided to write a poem:
+-----------------------+
| Pelican Riding Bike |
+-----------------------+
| This is the cat! |
| He's got big wings and a happy tail. |
| He loves to ride his bike! |
+-----------------------+
| Bike lights are shining bright. |
| He's got a shiny top, too! |
| He's ready for adventure! |
+-----------------------+
I'm looking forward to seeing people fine-tune this in a way that produces useful output for selected tasks, which should absolutely be feasible.
I audibly laughed at this one: https://gist.github.com/simonw/25e7b7afd6a63a2f15db48b3a51ec... where it generates a… poem? Song? And then proceeds to explain how each line contributes to the SVG, concluding with:
> This SVG code provides a clear and visually appealing representation of a pelican riding a bicycle in a scenic landscape.
I see you are using ollamas ggufs. By default it will download Q4_0 quantization. Try `gemma3:270m-it-bf16` instead or you can also use unsloth ggufs `hf.co/unsloth/gemma-3-270m-it-GGUF:16`
You'll get better results.
Good call, I'm trying that one just now in LM Studio (by clicking "Use this model -> LM Studio" on https://huggingface.co/unsloth/gemma-3-270m-it-GGUF and selecting the F16 one).
(It did not do noticeably better at my pelican test).
Actually it's worse than that, several of my attempts resulted in infinite loops spitting out the same text. Maybe that GGUF is a bit broken?
Oh :( Maybe the settings? Could you try
temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0
Daniel, thanks for being here providing technical support as well. Cannot express enough how much we appreciate your all work and partnership.
Thank you and fantastic work with Gemma models!
My topping only lets me set temperature and top_p but setting them to those values did seem to avoid the infinite loops, thanks.
Oh fantastic it worked! I was actually trying to see if we can auto set these within LM Studio (Ollama for eg has params, template) - not sure if you know how that can be done? :)
I ran into the same looping issue with that model.
Definitely give
temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0
a try, and maybe repeat_penalty = 1.1
He may generate useless tokens but boy can he generate ALOT of tokens.
Can he draw an "alot" made of tokens? https://hyperboleandahalf.blogspot.com/2010/04/alot-is-bette...
He? I know some Gemmas and it's distinctly a female name; is Gemma a boy's name where you're from?
I don't really gender LLMs in my head in general. I guess Gemma is a female name. I only gendered it in the joke because I think it makes it funnier, especially since it's just "a little guy". I know they are giving gendered names to these models now but I think it's a bit weird to gender when interacting with them.
Doesn’t the “M” in “Gemma 3 270M” Stand for “male”?
Not sure if that’s a serious question but it stands for “million”. As compared to 1B+ models, where the B stands for “billion” parameters.
Perhaps the poster we referring to Simon not Gemma.
> It's absolutely tiny - just a 241MB download
That still requires more than 170 floppy disks for installation.
Indeed. Requires over 3,000,000 punch cards to store. Not very tiny!
On the plus side, you can decompose your matrices for free using termites.
> For this one it decided to write a poem
My first try:
user: "When was Julius Caesar born"
response: "Julius Caesar was born in **Rome**"
Beautiful :D
(I do not mean to detract from it - but it's just beautiful. It will require more effort to tame it.)
Cutting number of parameters in half is like drinking a pint of beer.
I think you meant vodka.
Serious question but if it hallucinates about almost everything, what's the use case for it?
Fine-tuning for specific tasks. I'm hoping to see some good examples of that soon - the blog entry mentions things like structured text extraction, so maybe something like "turn this text about an event into an iCal document" might work?
Fine tuning messes with instruction following and RL'd behavior. I think this is mostly going to be useful for high volume pipelines doing some sort of mundane extraction or transformation.
This is exactly the fine-tuning I am hoping for, or I would do if I had the skills. I tried it with gemma3 270M and vanilla it fails spectacularly.
Basically it would be the quickadd[1] event from google calendar, but calendar agnostic.
[1] https://developers.google.com/workspace/calendar/api/v3/refe...
Google helpfully made some docs on how to fine-tune this model [0]. I'm looking forward to giving it a try!
[0]: https://ai.google.dev/gemma/docs/core/huggingface_text_full_finetuneIt's intended for finetuning on your actual usecase, as the article shows.
The same as having a goldfish. You can train it to do a trick I guess.
I feel like the blog post, and GP comment, does a good job of explaining how it's built to be a small model easily fine tuned for narrow tasks, rather than used for general tasks out of the box. The latter is guaranteed to hallucinate heavily at this size, that doesn't mean every specific task it's fine tuned to would be. Some examples given were fine tuning it to efficiently and quickly route a query to the right place to actually be handled or tuning it to do sentiment analysis of content.
An easily fine tunable tiny model might actually be one of the better uses of local LLMs I've seen yet. Rather than try to be a small model that's great at everything it's a tiny model you can quickly tune to do one specific thing decently, extremely fast, and locally on pretty much anything.
It's funny. Which is subjective, but if it fits for you, it's arguably more useful than Claude.
Because that's not the job it was designed to do, and you would know by reading the article.
Games where you need NPCs to talk random jiberrish.
robotic parrots?
Nothing, just like pretty much all models you can run on consumer hardware.
This message brought to you by OpenAI: we're useless, but atleast theres a pay gate indicating quality!
An army of troll bots to shift the Overton Window?
oh no now we'll never hear the end of how LLMs are just statistical word generators
It's gonna be a customer service agent for Sirius Cybernetics. Share and enjoy!
This is like a kobold to the other models' dragons and I don't hate it. :)
Could be interesting to use in a RAG setup and also finetuning it
For sure it won’t generate great svgs, but it might be a really good conversational model
The article says it's not a good conversational model but can be used for data extraction and classification as two examples.
> For this one it decided to write a poem
Could it be tamed with good role-system prompt crafting? (Besides fine-tuning.)
Do you take requests? We need to see how well this model works with some fine-tuning :D
i was looking at the demo and reading the bed time story it generated and even there, there was confusion about the sprite and the cat. switched subjects instantly making for a confusing paragraph. what's the point of this model?
the question is wheather you can make a fine tuned version and spam any given forum within an hour with the most attuned but garbage content.
Apple should be doing this. Unless their plan is to replace their search deal with an AI deal -- it's just crazy to me how absent Apple is. Tim Cook said, "it's ours to take" but they really seem to be grasping at the wind right now. Go Google!
As every other thread about LLMs here on HN points out: LLMs are stupid and useless as is. While I don't agree with that sentiment, no company has yet found a way to "do it right" to the extent that investments are justified in the long run. Apple has a history of "being late" and then obliterating the competition with products that are way ahead the early adopters (e.g. MP3 players, smart phones, smart watches).
They have a GPT2 like model that's deployed right now for autocomplete https://jackcook.com/2023/09/08/predictive-text.html
By "this", do you mean SLM (small language models)? That's absolutely something they've been working on for a good while.
Apple will definitely not be doing this. As can be already seen in other comments, the performance of the model is not very good. In fact, you can't really find a model that runs well enough on a phone to provide a good user experience (meaning producing tokens at a reasonable speed without making the phone heat up like a potato, and it's not spitting completely nonsense). Yes I have tried a few.
Think of Apple however you want, but they rarely ship bad/half-baked products. They would rather not ship a product at all than ship something that's not polished.
They do. The docs are right here: https://developer.apple.com/documentation/foundationmodels/g...
If you have the latest betas installed you can call those APIs right now.
They also support fine-tuning on the model that ships with ~every device:
https://developer.apple.com/apple-intelligence/foundation-mo...
steve jobs was the innovator, steve cook is the supply chain guy. They started an electric car not because they thought it was a good idea, but because everyone was going to leave to Tesla or rivian if they didn't. They had no direction and arguements that Tesla had about whether to have a steering wheel...
Then Siri just kinda languishes for forever, and LLM's pass the torch of "Cool Tech", so they try and "Reinvigurate" the team, but with no clear direction. Are they going to be a cloud provider? Are they going to contract out the training? Are they gunna spin up a compute facility even after neglecting to do so since 2012?
Apple needs to just stop trying shit, and just get that app store money. That's why jobs appointed cook. Jobs new cook was no innovator, but he could make apple a money printing machine. Thats what they should stick with.
I agreed with that for a bit... and then out of nowhere came Apple Silicon, incredible specs, incredible backward compatibility, nah, Cook is no dummy.
Here's the trillion dollar question: how do you print money when the president wants your hardware onshored and the rest of the world wants to weaken your service revenue?
Solve that and you can put Tim Cook out of a job tomorrow.
I've got a very real world use case I use DistilBERT for - learning how to label wordpress articles. It is one of those things where it's kind of valuable (tagging) but not enough to spend loads on compute for it.
The great thing is I have enough data (100k+) to fine-tune and run a meaningful classification report over. The data is very diverse, and while the labels aren't totally evenly distributed, I can deal with the imbalance with a few tricks.
Can't wait to swap it out for this and see the changes in the scores. Will report back
ModernBERT may be a better base model if finetuning a model for a specific use case: https://huggingface.co/blog/modernbert
Curious. Are there real world usecases where people have finetuned such tiny models and put them into production.
My job uses tiny models to determine escalations to bigger models. The tiny model provides a label and if it’s high confidence, we escalate to ChatGPT confirm.
I also want to try this with language detection. Existing open source ML models have weaknesses for mixed language, length of text, or domain limitations in the underlying text (like trained on bible translations).
I built a reranker for a RAG system using a tiny model. After the candidate generation (i.e. vector search + BM25) and business logic filters/ACL checks the remainder of the chunks went through a model that given the user query told you whether or not the chunk was really relevant. That hit production, but once the context size of models grew that particular piece was discarded as passing everything yielded better results and prices (the fact that prices of input tokens went down also played a role I am sure).
So only for a while, but it still counts :)
I’m not sure what I’d use them for, except maybe tag generation? Encoders of this size usually outperform by a wide margin on tasks they would overlap with.
I'm making an app where literally all I want to do with an LLM is generate tags. This model has failed with flying colours, literally takes forever to parse anything and doesn't follow instructions.
Edit - I should add, currently the model I'm using is Gemini Flash Lite through the Gemini API. It's a really good combo of fast, follows instructions, gives correct results for what I want and cost-effective. I still would love a small open model that can run on edge though.
Oof. I also had it refuse an instruction for “safety”, which was completely harmless. So that’s another dimension of issues with operationalizing it.
Well, Gemini Flash Lite is at least one, or likely two orders of magnitude larger than this model.
That's fair but one can dream of being able to simply run a useful LLM on CPU on your own server to simplify your app and save costs...
IIRC that Android (at least Pixel devices) use fine-tuned Gemma model(s) for some on-device assistant things
9gag.com commenter
Out of curiosity: because there seems to be a race to optimise models for local inference, how much "parameters one could save" by dropping unneeded language and domain-specific information.
Like, can you have a model that is English-only, but does more with the same amount of parameters if Chinese and European languages are dropped from the training?
This is a key question we faced when building this model. It comes down to basically to "how good" to you need to be at "how many things". We had to make some choices with this model and do our best to maximize performance in those areas.
To answer this more precisely its a matter of choosing different data and training regimes and checking performance with evals.
And to make this fully concrete you're welcome to give it a try! Train this model on a taskset of your choice and measure the performance tradeoffs. You'll get a good sense of how LLM capabilities shift
Unfortunately, it doesn't quite work like that. Google this: transfer learning.
Never thought I'd run an LLM released in 2025, on my phone, in full BF16. With ~80tps on an iPhone 16 pro btw.
How do you actually run this on an iPhone?
With something like PocketPal AI (https://github.com/a-ghorbani/pocketpal-ai). I'd love hear HN'ers opinions on the "best" LM Studio-like option for iOS devices.
For iOS, OpenCat. Has iCloud sync, and one universal app for MacOS and iOS devices.
I use PocketPal. Can run any gguf model off hf.
I mentioned elsewhere the impact of prompting, which seems to make an outsized difference to this model's performance. I tried NER and POS tagging (with somewhat disappointing results).
One thing that worked strikingly well was translation on non-Indo-European languages. Like I had success with Thai and Bahasa Indonesian -> English...
To add to the article: Gemma 3 270M's exact IFEval score is 51.2, and Qwen 3 would be at (0.6, 59.2) on the scatter plot.
I am sure with finetuning this can be changed somehow:
(base) ~ ollama run hf.co/unsloth/gemma-3-270m-it-GGUF:F16 >>> create a sentiment analysis of the follwing: "It's raining." The sentiment of the provided text is *negative*.
>>> create a sentiment analysis of the follwing: "It's raining money." The sentiment of the provided text is *negative*.
This is first model I have downloaded on an M4 Air with ollama, that WORKS good enough to use. It is right now parsing all my notes correctly, fantastic work team. Brilliant.
Is it possible to finetune a model like this with local hardware? Every tutorial I've come across on finetuning a local LLM uses some cloud service like colab or runpod.
Yes! For eg you can install Unsloth locally via pip install unsloth :)
I'm _very_ interested to see what this can be fine-tuned to do.
I've heard folks say a number of times that neuromuscular control / locomotion (or w/e) are hundreds of millions of parameters rather than billions.
I’ve had great luck with all gemma 3 variants, on certain tasks it the 27B quantized version has worked as well as 2.5 flash. Can’t wait to get my hands dirty with this one.
I'm a business professor who teaches Python and more. I'd like to develop some simple projects to help my students fine tune this for a business purpose. If you have ideas (or datasets for fine tuning), let me know!
Can somebody give me a link to a tutorial on how I would go about fine-tuning this?
Also, what sorts of things might I consider fine-tuning it for?
This tutorial looks useful: https://ai.google.dev/gemma/docs/core/huggingface_text_full_...
Just what I was looking for. Thanks!
This was linked at the end of Google's announcement: https://docs.unsloth.ai/basics/gemma-3-how-to-run-and-fine-t...
Not sure how much data is needed to realistically fine-tune something like this and get useful output.
That doesn’t really show me how to do fine-tuning, but there is a link to a notebook in there that does. Thanks!
If you need any help on it, ask away!
Interesting that for these small models, it is optimal for the embedding parameters to be a huge fraction of the total (170e6/250e6) = 68%!
Is it time for me to finally package a language model into my Lambda deployment zips and cut through the corporate red tape at my place around AI use?
Update #1:
Tried it. Well, dreams dashed - would now fit space wise (<250 MB despite the name), but it sadly really doesn't seem to work for my specific prospective workload.
I'd have wanted it to perform natural-language to command-invocation translation (or better, emit me some JSON), but it's super not willing to do that, not in the lame way I'm trying to make it do so at least (literally just prompting it to). Oh well.
Update #2:
Just found out about grammar-constrained decode, maybe there's still hope for me in the end. I don't think I can amend this comment today with any more updates, but will see.
Did you finetune it before trying? Docs here:
https://ai.google.dev/gemma/docs/core/huggingface_text_full_...
Thanks, will check that out as well tomorrow or during the weekend!
If you know you want JSON for sure constrained decoding in an inference framework will help. The model is just one part of an overall inference system. I hope this model paired with other tools help you get done whatever it is you're looking to get done
this model is fire.
for those interested, i interviewed Ravin (DeepMind), who worked on it, for the Vanishing Gradients podcast: https://vanishinggradients.fireside.fm/56
Video on YT here: https://youtu.be/VZDw6C2A_8E?si=XLUzNRQzeloB9rki
Disclaimer: The Gemma family rock!
Gemma 3 270M running on a midrange pixel device - https://x.com/1littlecoder/status/1956065040563331344
This was trained on 6T tokens. Neat to see so many tokens used for such a small model.
This is cool. I'm looking forward to trying it - I wonder what it'll be useful for.
> this model is not designed for complex conversational use cases
... but it's also the perfect choice for creative writing ...?
Isn't this a contradiction? How can a model be good at creative writing if it's no good at conversation?
I think they mean it's not designed to be able to converse with the user over long/complex topics, but it can generate fictional conversations fine.
Not necessarily. Where do you think the overlap is between these two tasks?
I wish it was multimodal :(
Excited to try this out, thanks for sharing.
I've been saying he we need sub 1B models for the edge so thanks fot this.
I am however disappointed that there is no examples, or benchmarks, provided to get a sense of performance. It's a given that benchmark values would be lower than gemma 3n, but having a sense of performance vs size curve and comparison to existing small models is needed
Benchmarks are on the model card https://huggingface.co/google/gemma-3-270m-it
Really impressive stuff, as always. I will say: it took me a shamefully long time to realize that the name ended in "M" instead of "B"! Perhaps they should consider renaming this to "Gemma 3 .27B"...
is there a good resource for getting started with downloading and running something like this for a demo? There are just so many tools/platforms in the mix now it makes my head spin.
The blog post contains links to several ways to try this model, locally, on colab, and in the cloud. Pick what works best for you!
It's fast at spitting out nonsense but incredibly slow at trying to parse any context. Also absolutely atrocious at following instructions.
Probably would be good as a game NPC or a chatbot, not very good for integrating into an application which specific functionality though.