I want to like the vision capabilities of the model. However, when I gave it an image which Gemma 26B A4B and Qwen 3.6 35B A3B has no problem correctly describing in detail, including identifying the Taj Mahal in the background it utterly failed. Its sense of the image was that it was a "distorted wide panorama" and even when I asked directly if it was the Taj Mahal it said no. The reference models saw it correctly as a normal square image taken from a fairly rectilinear lens (iPhone main camera).
Gemma 4 12B: A unified, encoder-free multimodal model
271–280 of 421 posts
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#272good one, wanna try on Cerebras inference in Agentic Browsing
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#273I 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…
It was almost certainly not trained for coding, as it's got both audio and vision input, is only 12B, and nowhere in the announcement is coding mentioned. It will likely not have good performance on coding in general, compared to other small models like Qwen 3.6 35B A3B, Gemma 4 26B A4B, Nvidia Nemotron 3 Nano 30B-A3B, gpt-oss-20b. For 16GB laptops, Qwen 3.5 9B is the undisputed champ. Gemma 4 31B is the top dog at s…
> For 16GB laptops, Qwen 3.5 9B is the undisputed champ.
Thanks
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#274What quantisation do the creators intend this to be run at? They talk about 16GB of ram, so should it be run at 8 bit? People here are talking about using q4, but I would have thought a smaller model like this wouldn't perform well at such low bits per parameter. Edit, it looks like their bechmarks would have been done at 16 bit float, as the hugging face release is that size: https://huggingface.co/google/gemma-4-12…
They haven't made one for this new model, but Unsloth has a comprehensive quant KLD map of Gemma 4 26B A4B here: https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-p...
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#275Earlier quoted context omitted.
You can actually get the gemma-4 models on a per-token API basis, you just have to click some extra buttons (in GCP). Not the same for other open weight models. For those they make you run your own hardware. Use OpenCode Go instead: https://opencode.ai/go
That doesn't have the Gemma models by the looks
They only host models they have evaluated and found good at coding
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#276Earlier quoted context omitted.
I gotta say, having both "gemma4:12b-mlx-bf16" and "gemma4:12b-nvfp4" be MLX-specific, and not labeling all of the MLX-specific ones as such, is a bit different than "little confusing" and more "set up to be confusing" :) > You'll also need to upgrade to version 0.30.4 which we're just about to release Interesting, wasn't Google coordinating today's release with you? Considering the blog post seems to have gone out w…
Given the model was just republished by Google 15 minutes ago and we're going to have to redo everything (and everyone will have to redownload for all platforms -- not just Ollama), I'll just say that sometimes things don't work out exactly the way you want them to. :-D That said, I think the gemma4:12b-nvfp4 model is pretty solid. It's been tuned with Nvidia's model optimizer. I've been waiting on the results for MM…
> Given the model was just republished by Google 15 minutes ago
Hah, missed that! Guess that's slightly neat though, you get a second chance ;) NVFP4 been a blast to use across a wide range of models, seems to work really well, at least with vLLM and a nvidia card.
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#277Earlier quoted context omitted.
> Have you found Gemma 4 31B better than Qwen 3.6 27B Q8? Which quant of Gemma? For coding Qwen seems to be pretty far ahead, but generally Gemma seems to have a "vaster" set of knowledge, but armed with a search tool it doesn't really matter, and Qwen 3.6 been really great for all sorts of tool calling. I mostly do programming and related things though, fwiw. > I was going off of peoples' opinions on reddit It's ext…
It took me way too long to realize you were referring to r/localllama.
Why the obfuscation in the first place?
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#278Do Gemma 4 models compete with Gemini 3.1 Flash-Lite? I would assume even the smallest Gemini model would outperform even Gemma 4 31B, but I can't really get a sense of performance or output quality difference.
Gemma 4 31b outperformed Gemini 3.1 Flash-Lite in our app benchmarks (agentic tool use via api in our application as a part of various workflows). But google won't let you pay to use Gemma models, you have to go elsewhere, I think this may be because it would cannabilize Flash-lite.
Curious logic. Does Google want you to use it or not? Do they want to be paid for tokens or not? why segregate open and closed?
It's not parameter size - there is apparently such a thing as "Gemini Nano", which famously is downloaded automatically by Chrome. How similar is it to Gemma E4B? And how strange - you have the weights, but you don't "have" them?
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#279I want to like the vision capabilities of the model. However, when I gave it an image which Gemma 26B A4B and Qwen 3.6 35B A3B has no problem correctly describing in detail, including identifying the Taj Mahal in the background it utterly failed. Its sense of the image was that it was a "distorted wide panorama" and even when I asked directly if it was the Taj Mahal it said no. The reference models saw it correctly a…
I have now also tried it on this scatter plot: https://3215535692-files.gitbook.io/~/files/v0/b/gitbook-x-p...
Similarly, the 26B A4B Gemma 4 and the 35B A3B Qwen 3.6 identify it clearly, give me the title and trends analysis fairly accurately. While this 12B spits out gobbledygook about it having something to do with hard-drive capacity. It's like it can barely see, gets the very broad strokes (knows it's looking at some kind of chart), but can't identify any details clearly.
Re: Gemma 4 12B: A unified, encoder-free multimodal model
#280Earlier quoted context omitted.
Because a model like this can't be as easily obfuscated as image processing. Image processing is a bundle of many moving parts, a lot of functions each with it's own inputs and outputs. A model is a single function which can be easily extracted and reused, in comparison
Arguably, but that's not the point. Take image (e.g. png) files on a CD-ROM shipped by a game vendor, which can be trivially copied even by my grandma. That doesn't move the game vendor to release them as freely distributable under the Apache license
Good point but still, why would Google police this model? If they had a restrictive licence on it do you think it would be worth it for them to enforce it? This way they at least buy some good will and mindshare