161–170 of 421 posts
Earlier quoted context omitted.
Totally agree that it is "encoding" in the general sense, but I think they are referring to the lack of an "encoder" neural network.
In hindsight I may have been pedantic.
Earlier quoted context omitted.
They are effectively one and the same on Apple Silicon.
Which most people as a matter of fact don't use. A majority of people with laptop have separate memory pools and the VRAM of them is nowhere near that and even on most gaming laptops you aren't getting 16GB VRAM.
Majority of people with laptop have RAM and igpu using some of that as VRAM.
We are now entering the closed loop game. Google doesn't need anyone else to accelerate their models. This is their bread and butter. I'm both shocked but also not surprised that they continue to develop such efficiencies. Honestly it's like silicon and CPU architecture advancement. We kept shrinking it and shrinking it and it kept getting more and more powerful and here we are with AI and it's only going to be 100x…
> We kept shrinking it and shrinking it and it kept getting more and more powerful and here we are with AI and it's only going to be 100x more efficient with time. It's definitely an exciting time, but in terms of advancements in the state of the art, there is a lot of low-hanging fruit left to pick. There IS a bottom, however, as you can only encode so much "knowledge" in a small number of parameters. This feels to…
Unfortunately there's no gguf quants of the assistant model yet: https://huggingface.co/models?other=base_model:quantized:goo...
The big story here is the encoder-free part, which I still don't fully understand. > Vision: We replaced Gemma 4’s vision encoder with a lightweight embedding module consisting of a single matrix multiplication, positional embedding and normalizations. That's technically encoding, just without using a dedicated model for it like SigLIP? The Developer's Guide elaborates, it's still a 35M layer which I am curious is ro…
Still progress, but not quite democratic yet.
Weird though that Google might be cannibalising it's own AI subscription service?
I ran the Q4 quant (used with llama.cpp) though my "minesweeper" vibe-coding benchmark: https://senko.net/vibecode-bench/2026/minesweeper-gamma-4-12... The result is decent, but it had a few bizzare/trivial syntax errors I had to fix manually: it would do an extra closing bracket or paren a few times, and wanted to separate function definitions with comma. Not sure what that was about, but otherwise the output run ju…
Thank you for sharing this. Do you think the syntactical issues could be addressed with fine tuning or some other kind of parameter tweaking? That's frustrating hah.
What's Google's business case for releasing open models? Don't get me wrong, I am grateful and appreciative of these releases. I'm trying to understand how it fits into their bigger picture as a for profit company? Are they not helping competitors build on the novel technology they have developed? Is it simply goodwill and/or marketing? Or am I missing something strategic?
Android and Chrome need on-device AI capabilities. Google can't lock down those weights like it can with server-side ML. So it's easier to just release those models as open source and make it official, since someone would inevitably hack the weights out anyway.
What's Google's business case for releasing open models? Don't get me wrong, I am grateful and appreciative of these releases. I'm trying to understand how it fits into their bigger picture as a for profit company? Are they not helping competitors build on the novel technology they have developed? Is it simply goodwill and/or marketing? Or am I missing something strategic?
The big story here is the encoder-free part, which I still don't fully understand. > Vision: We replaced Gemma 4’s vision encoder with a lightweight embedding module consisting of a single matrix multiplication, positional embedding and normalizations. That's technically encoding, just without using a dedicated model for it like SigLIP? The Developer's Guide elaborates, it's still a 35M layer which I am curious is ro…
Either Google changed the text or you editorialised it a tiny bit - just for all others that got excited, they mean 16GB V RAM. So a premium graphics card requiring a >2500€ device is the minimum to run this. Still progress, but not quite democratic yet. Weird though that Google might be cannibalising it's own AI subscription service?
Its image processing is terrible. I ran several tests against it against Qwen 3.5 0.8b (yes, 7% the size) and Qwen beat it every time with Gemma often getting things entirely wrong. I even gave it a plain image saying "This is a test" and it thought for 6 minutes trying to analyze it and failed. Qwen 3.5 0.8b confidently got it in under a second . It may be that the Q6 quant I got is borked (or my LM Studio is), but…