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LWDCLS Research 44

@Lewdiculous

I think it might be worth exploring, finding a good balance between quality and speed.

I am currently experimenting with the config below:

# Used for everything not specified below.
ftype=IQ4_NL

token_embd.weight=Q8_0
output.weight=Q8_0

# These are quite small, keeping them in a higher quantization to help with context.
blk.*.attn_output.weight=F16
blk.*.attn_?.weight=F16

Edit: Seems the code above is 6.95 BPW, I will try reducing it. It is pretty fast though.

I never played with customizing layers as such.

I updated the llama.cpp but still only get degraded quants. Are there any tutorials or something like that to use llama.cpp for llama 3 models? I only know the convert.py method (python convert.py ./models/myllama3merge --vocab-type bpe)

@WesPro I added a notice about that in the GGUF script page.

https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script

More context:
https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script/discussions/27#66361fceccadfaaeacb0cdb5

Related Discussion:
https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script/discussions/26#66317fb30a84b77d96b0c4e6

@WesPro I added a notice about that in the GGUF script page.

https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script

More context:
https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script/discussions/27#66361fceccadfaaeacb0cdb5

Related Discussion:
https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script/discussions/26#66317fb30a84b77d96b0c4e6

thanks I figured it out now... this helped me more than reading the whole issue thread on github ;)

That's why we're here <3