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Gemma 3 270M: Compact model for hyper-efficient AI

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Re: Gemma 3 270M: Compact model for hyper-efficient AI

#111

Post by blitzar »

canyon289 wrote: post #85
jmorgan wrote: post #83

Earlier quoted context omitted.

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

Re: Gemma 3 270M: Compact model for hyper-efficient AI

#112

Post by patrickaljord »

canyon289 wrote: post #2

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…

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?

Re: Gemma 3 270M: Compact model for hyper-efficient AI

#113

Post by andrewstuart »

dist-epoch wrote: post #88
andrewstuart wrote: post #62

Earlier quoted context omitted.

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 f…

Here you go, one click installer - https://lmstudio.ai

I’m talking about the supplier doing the packaging.

Re: Gemma 3 270M: Compact model for hyper-efficient AI

#114

Post by dmayle »

perching_aix wrote: post #73

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 ( 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…

Did you finetune it before trying? Docs here:

https://ai.google.dev/gemma/docs/core/huggingface_text_full_...

Re: Gemma 3 270M: Compact model for hyper-efficient AI

#115

Post by floridianfisher »

dcreater wrote: post #13

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

Re: Gemma 3 270M: Compact model for hyper-efficient AI

#116

Post by teraflop »

cristyansv wrote: post #71
jefb wrote: post #64

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 Everes…

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?

Re: Gemma 3 270M: Compact model for hyper-efficient AI

#118

Post by imasl42 »

canyon289 wrote: post #2

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…

Awesome! I’m curious how is the team you built these models with? Is it great?

Re: Gemma 3 270M: Compact model for hyper-efficient AI

#120

Post by iib »

simonw wrote: post #31
marinhero wrote: post #14

Earlier quoted context omitted.

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?

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...

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