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Google releases Gemma 4 open models

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Re: Google releases Gemma 4 open models

#21

Post by danielhanchen »

l2dy wrote: post #19
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!

FYI, screenshot for the "Search and download Gemma 4" step on your guide is for qwen3.5, and when I searched for gemma-4 in Unsloth Studio it only shows Gemma 3 models.

We're still updating it haha! Sorry! It's been quite complex to support new models without breaking old ones

Re: Google releases Gemma 4 open models

#22

Post by adamtaylor_13 »

originalvichy wrote: post #14

The wait is finally over. One or two iterations, and I’ll be happy to say that language models are more than fulfilling my most common needs when self-hosting. Thanks to the Gemma team!

What sort of tasks are you using self-hosting for? Just curious as I've been watching the scene but not experimenting with self-hosting.

Re: Google releases Gemma 4 open models

#23

Post by pixl97 »

echelon wrote: post #17
rvz wrote: post #11

Open weight models once again marching on and slowly being a viable alternative to the larger ones. We are at least 1 year and at most 2 years until they surpass closed models for everyday tasks that can be done locally to save spending on tokens.

> We are at least 1 year and at most 2 years until they surpass closed models for everyday tasks that can be done locally to save spending on tokens. Until they pass what closed models today can do. By that time, closed models will be 4 years ahead. Google would not be giving this away if they believed local open models could win. Google is doing this to slow down Anthropic, OpenAI, and the Chinese, knowing that in t…

I mean, correct, but running open models locally will still massively drop your costs even if you still need to interface with large paid for models. Google will still make less money than if they were the only model that existed at the end of the day.

Re: Google releases Gemma 4 open models

#24

Post by irishcoffee »

adamtaylor_13 wrote: post #22
originalvichy wrote: post #14

The wait is finally over. One or two iterations, and I’ll be happy to say that language models are more than fulfilling my most common needs when self-hosting. Thanks to the Gemma team!

What sort of tasks are you using self-hosting for? Just curious as I've been watching the scene but not experimenting with self-hosting.

I would personally be much more interested in using LLMs if I didn’t need to depend on an internet connection and spending money on tokens.

Re: Google releases Gemma 4 open models

#26

Post by Imustaskforhelp »

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!

Daniel, I know you might hear this a lot but I really appreciate a lot of what you have been doing at Unsloth and the way you handle your communication, whether within hackernews/reddit.

I am not sure if someone might have asked this already to you, but I have a question (out of curiosity) as to which open source model you find best and also, which AI training team (Qwen/Gemini/Kimi/GLM) has cooperated the most with the Unsloth team and is friendly to work with from such perspective?

Re: Google releases Gemma 4 open models

#27

Post by jeffbee »

jwr wrote: post #3

Really looking forward to testing and benchmarking this on my spam filtering benchmark. gemma-3-27b was a really strong model, surpassed later by gpt-oss:20b (which was also much faster). qwen models always had more variance.

Does spam filtering really need a better model? My impression is that the whole game is based on having the best and freshest user-contributed labels.
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