TheCornCollector@piefed.zip to

LocalLLaMA@sh.itjust.worksEnglish · 14 days ago

Qwen3.6 27B released

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Qwen3.6 27B released

TheCornCollector@piefed.zip to

LocalLLaMA@sh.itjust.worksEnglish · 14 days ago

Qwen/Qwen3.6-27B · Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Recently made a post about the 35b MOE. Now the dense 27b variant has been released.

Chat

venusaur@lemmy.world
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12 days ago
Cool I’ve heard of it but I know there are a lot of variable. What model and size are you running with what hardware?
- thedeadwalking4242@lemmy.world
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  12 days ago
  I’ve only ran super small models. I have a cheap gaming laptop with a Nvidia 3060 with like 8gb of vram.
  
  Gemma4 will probably be a good model to try on your hardware
  - venusaur@lemmy.world
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    12 days ago
    Thanks!

LocalLLaMA@sh.itjust.works

localllama@sh.itjust.works

You are not logged in. However you can subscribe from another Fediverse account, for example Lemmy or Mastodon. To do this, paste the following into the search field of your instance: !localllama@sh.itjust.works

Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.

Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.

As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.

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Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.

Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they’re still using the same algorithms since <over 10 years ago>.

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