Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Google releases Gemma 4 open models

deepmind.google

Post reply on HN
Print view View original

301–310 of 507 posts

Re: Google releases Gemma 4 open models

#301

Post by stavros »

neonstatic wrote: post #228

Prompt: > what is the Unix timestamp for this: 2026-04-01T16:00:00Z Qwen 3.5-27b-dwq > Thought for 8 minutes 34 seconds. 7074 tokens. > The Unix timestamp for 2026-04-01T16:00:00Z is: > 1775059200 (my comment: Wednesday, 1 April 2026 at 16:00:00) Gemma-4-26b-a4b > Thought for 33.81 seconds. 694 tokens. > The Unix timestamp for 2026-04-01T16:00:00Z is: > 1775060800 (my comment: Wednesday, 1 April 2026 at 16:26:40) Gem…

To clarify, the parent here didn't actually give the model a way to run the commands. The model just wrote the script/command and then, being unable to run anything, just mentally calculated what the result would probably be (and got it wrong).

Yes the answer was wrong, but so was the setup (the model should have had access to a command runner tool).

Re: Google releases Gemma 4 open models

#302

Post by girvo »

suprjami wrote: post #283
coder543 wrote: post #135

Earlier quoted context omitted.

For the many DGX Spark and Strix Halo users with 128GB of memory, I believe the ideal model size would probably be a MoE with close to 200B total parameters and a low active count of 3B to 10B. I would personally love to see a super sparse 200B A3B model, just to see what is possible. These machines don't have a lot of bandwidth, so a low active count is essential to getting good speed, and a high total parameter cou…

Following the current rule of thumb MoE = `sqrt(param*active)` a 200B-A3B would have the intelligence of a ~24B dense model. That seems pointless. You can achieve that with a single 24G graphics card already. I wonder if it would even hold up at that level, as 3B active is really not a lot to work with. Qwen 3.5 uses 122B-A10B and still is neck and neck with the 27B dense model. I don't see any value proposition for…

The value prop for the Nvidia one is simple: playing with CUDA with wide enough RAM at okay enough speeds, then running your actual workload on a server someone running the same (not really, lol Blackwell does not mean Blackwell…) architecture.

They’re fine tuning and teaching boxes, not inference boxes. IMO anyway, that’s what mine is for.

Re: Google releases Gemma 4 open models

#303

Post by XCSme »

antirez wrote: post #10

Featuring the ELO score as the main benchmark in chart is very misleading. The big dense Gemma 4 model does not seem to reach Qwen 3.5 27B dense model in most benchmarks. This is obviously what matters. The small 2B / 4B models are interesting and may potentially be better ASR models than specialized ones (not just for performances but since they are going to be easily served via llama.cpp / MLX and front-ends). Also…

It does quite well on my limited/not-so-scientific private tests (note the tests don't include coding tests): https://aibenchy.com/compare/google-gemma-4-31b-it-medium/go...

Re: Google releases Gemma 4 open models

#304

Post by girvo »

markab21 wrote: post #267
coder68 wrote: post #197

Earlier quoted context omitted.

I gave it a whirl but was unenthused. I'll try it again, but so far have not really enjoyed any of the nvidia models, though they are best in class for execution speed.

I'll pipe in here as someone working on an agentic harness project using mastra as the harness. Nemotron3-super is, without question, my favorite model now for my agentic use cases. The closest model I would compare it to, in vibe and feel, is the Qwen family but this thing has an ability to hold attention through complicated (often noisy) agentic environments and I'm sometimes finding myself checking that i'm not on…

> I'm not sure why more people aren't jumping on it

Simple: most of the people you’re talking to aren’t setting these things up. They’re running off the shelf software and setups and calling it a day. They’re not working with custom harnesses or even tweaking temperature or templates, most of them.

Re: Google releases Gemma 4 open models

#305

Post by dom96 »

retinaros wrote: post #223
lostmsu wrote: post #182

Earlier quoted context omitted.

Not exactly what you asked for but try https://pelicans.borg.games/

what the sorcery is that https://static.simonwillison.net/static/2024/recraft-ai-peli... I tried their model and asking a few different svg of pelicans. it is INSANE.

This is also interesting: https://static.simonwillison.net/static/2025/pov-pelican-opu...

Re: Google releases Gemma 4 open models

#307

Post by VadimPR »

snthpy wrote: post #125
VadimPR wrote: post #44

Gemma 3 E4E runs very quick on my Samsung S26, so I am looking forward to trying Gemma 4! It is fantastic to have local alternatives to frontier models in an offline manner.

What's the easiest way to install these on an Android phone/Samsung?

I use LM Studio, but there's a comment here offering another tool as well.

Re: Google releases Gemma 4 open models

#308

Post by nnucera »

danielhanchen wrote: post #2

Thinking / reasoning + multimodal + tool calling. We made some quants at https://huggingface.co/collections/unsloth/gemma-4 for folks to run them - they work really well! Guide for those interested: https://unsloth.ai/docs/models/gemma-4 Also note to use temperature = 1.0, top_p = 0.95, top_k = 64 and the EOS is " ". " thought\n" is also used for the thinking trace!

Wow! Thank you very much!

Re: Google releases Gemma 4 open models

#309

Post by BloondAndDoom »

refulgentis wrote: post #183
scrlk wrote: post #15

Comparison of Gemma 4 vs. Qwen 3.5 benchmarks, consolidated from their respective Hugging Face model cards: | Model | MMLUP | GPQA | LCB | ELO | TAU2 | MMMLU | HLE-n | HLE-t | |----------------|-------|-------|-------|------|-------|-------|-------|-------| | G4 31B | 85.2% | 84.3% | 80.0% | 2150 | 76.9% | 88.4% | 19.5% | 26.5% | | G4 26B A4B | 82.6% | 82.3% | 77.1% | 1718 | 68.2% | 86.3% | 8.7% | 17.2% | | G4 E4B |…

Reversing the X and Y axis, adding in a few other random models, and dropping all the small Qwens makes this worse than useless as a Qwen 3.5 comparison, it’s actively misleading. If you’re using AI, please don’t rush to copy paste output :/ EDIT: Lordy, the small models are a shadow of Qwen's smalls. See https://huggingface.co/Qwen/Qwen3.5-4B versus https://www.reddit.com/r/LocalLLaMA/comments/1salgre/gemma_4...

Small qwen models are magical
Post reply on HN