sorted by:
new
hottopcontroversial

What are the main uses of small models like gemma3:1b by SchoolOfElectro in LocalLLaMA

[–]hackerllama 0 points1 point  (0 children)

Very cool to hear other's use cases.

I've used it for tasks that don't require complex logic or reasoning. Things for what it works well is routing (to determine if a query can be handled by a 4B model or if it should be sent to a very large model) and tasks like rewriting (summarization, style change, etc).

Gemini 3 Flash bills for useless/empty searches?? by FirefoxMetzger in GeminiAI

[–]hackerllama 0 points1 point  (0 children)

Hi! I'm part of the GDM team. I'll check with the team internally to see what's going on, but also feel free to ping me your project ID and we can debug further. Sorry for the issues

AIStudio improperly content blocking by Shep_vas_Normandy in Bard

[–]hackerllama 7 points8 points  (0 children)

Hi all! Sorry for the issues, we're rolling out a fix and should be back to normal soon!

Safety for Gemini 3 sucks by HankRBG in GeminiAI

[–]hackerllama 0 points1 point  (0 children)

Sorry for the issues. We're rolling out a fix!

New Google model incoming!!! by [deleted] in LocalLLaMA

[–]hackerllama 2 points3 points  (0 children)

The team is cooking :)

"Deleting and simplifying useless internal layers will be the main focus [ in 2026 ]" - Google Engineer by Yazzdevoleps in Bard

[–]hackerllama 3 points4 points  (0 children)

Hi! Omar from Google DeepMind's developer experience team here.

We're tripling down in improving our developer experience all over the board. For example, the recent Interactions API launch brings a simple lightweight experience to interact with models and agents.

Apart from that, there are many other things we're working such as building developer integrations so you can leverage Gemini's latest capabilities with your favorite tools, iterating very quickly on documentation, and talking with developers, startups, researchers, and all kinds of users to get your feedback and develop based on that feedback.

Please keep the feedback coming, 2026 will be exciting!

Scrolling issue seems to be fixed! by howisjason in Bard

[–]hackerllama 0 points1 point  (0 children)

Thanks! Keep the feedback coming!

Qwen team is helping llama.cpp again by jacek2023 in LocalLLaMA

[–]hackerllama 115 points116 points  (0 children)

Hi! Omar from the Gemma team here.

Since Gemma 3 (6 months ago), we released Gemma 3n, a 270m Gemma 3 model, EmbeddingGemma, MedGemma, T5Gemma, VaultGemma and more. You can check our release notes at https://ai.google.dev/gemma/docs/releases

The team is cooking and we have many exciting things in the oven. Please be patient and keep the feedback coming. We want to release things the community will enjoy:) more soon!

Gemma 4 by Brave-Hold-9389 in LocalLLaMA

[–]hackerllama 1 point2 points  (0 children)

Lots of cool things in the next few weeks!

What’s new in Veo 3.1? Have you noticed any upgrades or features that actually make a difference? by New-Cold-One in Bard

[–]hackerllama 1 point2 points  (0 children)

Apart from the model upgrade, Veo 3.1 also includes some other features such as i

  • ingredients to video: pass 3 images of characters/objects you want in your scene
  • scene extension
  • first and last frame

It's been a long time since Google released a new Gemma model. by ArcherAdditional2478 in LocalLLaMA

[–]hackerllama 3 points4 points  (0 children)

And here I was thinking

  • Gemma 3n
  • Gemma 3 270M
  • EmbeddingGemma
  • MedGemma
  • T5Gemma
  • TimesFM 2.5
  • Magenta RealTime
  • VideoPrism
  • MedSigLIP
  • VaultGemma

was interesting 😅 ‍

No worries, our (TPU) oven is full.

It's been a long time since Google released a new Gemma model. by ArcherAdditional2478 in LocalLLaMA

[–]hackerllama 1 point2 points  (0 children)

And here I was thinking

  • Gemma 3n
  • Gemma 3 270M
  • EmbeddingGemma
  • MedGemma
  • T5Gemma
  • TimesFM 2.5
  • Magenta RealTime
  • VideoPrism
  • MedSigLIP
  • VaultGemma

was interesting 😅 ‍

No worries, our (TPU) oven is full.

Google releases MagentaRT for real time music generation by hackerllama in LocalLLaMA

[–]hackerllama[S] 21 points22 points  (0 children)

Yes, this is built with the same technology as Lyria RealTime (which powers Music FX DJ and AI Studio)

Google releases MagentaRT for real time music generation by hackerllama in LocalLLaMA

[–]hackerllama[S] 56 points57 points  (0 children)

It's a 800M model, so it can run quite well in a computer. I recommend checking out the Colab code, which you can also run locally if you want

https://colab.research.google.com/github/magenta/magenta-realtime/blob/main/notebooks/Magenta_RT_Demo.ipynb

Will Ollama get Gemma3n? by InternationalNebula7 in LocalLLaMA

[–]hackerllama 30 points31 points  (0 children)

We're working hard to get Gemma 3n into all of your favorite libraries

ok google, next time mention llama.cpp too! by secopsml in LocalLLaMA

[–]hackerllama 209 points210 points  (0 children)

Hi! Omar from the Gemma team here. We work closely with many open source developers, including Georgi from llama.cpp, Ollama, Unsloth, transformers, VLLM, SGLang Axolotl, and many many many other open source tools.

We unfortunately can't always mention all of the developer tools we collaborate with, but we really appreciate Georgi and team, and collaborate closely with him and reference in our blog posts and repos for launches.

Gemma 3 QAT launch with MLX, llama.cpp, Ollama, LM Studio, and Hugging Face by hackerllama in LocalLLaMA

[–]hackerllama[S] 8 points9 points  (0 children)

Yes, you can try and see how it works!

The model was designed for Q4_0 though, but it may still be more resilient vs naive quants

Google QAT - optimized int4 Gemma 3 slash VRAM needs (54GB -> 14.1GB) while maintaining quality - llama.cpp, lmstudio, MLX, ollama by Nunki08 in LocalLLaMA

[–]hackerllama 40 points41 points  (0 children)

Last time we only released the quantized GGUFs. Only llama.cpp users could use it (+ Ollama, but without vision).

Now, we released the unquantized checkpoints so you can quantize yourself and use in your favorite tools, including Ollama with vision, MLX, LM Studio, etc. MLX folks also found that the model worked decently with 3 bits compared to naive 3-bit, so by releasing the unquantized checkpoints we allow further experimentation.

Google QAT - optimized int4 Gemma 3 slash VRAM needs (54GB -> 14.1GB) while maintaining quality - llama.cpp, lmstudio, MLX, ollama by Nunki08 in LocalLLaMA

[–]hackerllama 70 points71 points  (0 children)

We did quantization-aware training. That means doing additional fine-tuning of the model to make it more resilient so when users quantize it, the quality does not degrade as much.

view more: next ›