this post was submitted on 07 Apr 2025
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LocalLLaMA

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General consensus seems to be that llama4 was a flop. A head of meta AI research division was let go.

Do you think it was a bad fp32 conversion, or just unerwhelming models all around?

2t parameters was a big increase without much gain. If throwing compute and parameters isnt working to stay competitive anymore, how do you think the next big performance gains will be made? Better CoT reasoning patterns? Omnimodal? something entirely new?

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[–] weker01 3 points 2 months ago (1 children)

Hmmn, never heard of QAT. What does it stand for?

[–] [email protected] 4 points 2 months ago (1 children)

https://pytorch.org/blog/quantization-aware-training/

I had heard of it but I'm not aware of public models implementing this

[–] pebbles 3 points 2 months ago (1 children)

Here is link for ollama for Gemma 3 QAT https://ollama.com/eramax/gemma-3-27b-it-qat:q4_0

There are ggufs around if you want to try it on another back end.

[–] [email protected] 1 points 2 months ago

Thanks. I'll try it out!