As requested, this is the subreddit’s second megathread for model discussion. This thread will now be hosted at least once a month to keep the discussion updated and help reduce identical posts.
I also saw that we hit 80,000 members recently! Thanks to every member for joining and making this happen.
Welcome to the r/LocalLLaMA Models Megathread
What models are you currently using and why? Do you use 7B, 13B, 33B, 34B, or 70B? Share any and all recommendations you have!
Examples of popular categories:
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Assistant chatting
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Chatting
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Coding
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Language-specific
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Misc. professional use
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Role-playing
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Storytelling
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Visual instruction
Have feedback or suggestions for other discussion topics? All suggestions are appreciated and can be sent to modmail.
^(P.S. LocalLLaMA is looking for someone who can manage Discord. If you have experience modding Discord servers, your help would be welcome. Send a message if interested.)
EXL2 runs fast and the quantization process implements some fancy logic behind the scenes to do something similar to k_m quants for GGUF models. Instead of quantizing every slice of the model to the same bits per weight (bpw), it determines which slices are more important and uses a higher bpw for those slices and a lower bpw for the less-important slices where the effects of quantization won’t matter as much. The result is the average bits per weight across all the layers works out to be what you specified, say 4.0 bits per weight, but the performance hit to the model is less severe than its level of quantization would suggest because the important layers are maybe 5.0 bpw or 5.5 bpw, something like that.
In short, EXL2 quants tend to punch above their weight class due to some fancy logic going on behind the scenes.
Thank you! I’m reminded of variable bit rate encoding used in various audio and video formats, this sounds not dissimilar.