LocalLLaMA

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Community to discuss about LLaMA, the large language model created by Meta AI.

This is intended to be a replacement for r/LocalLLaMA on Reddit.

founded 1 year ago

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SkySyrup@sh.itjust.works

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noneabove1182@sh.itjust.works

Beginner questions thread (sh.itjust.works)

submitted 1 year ago by noneabove1182@sh.itjust.works to c/localllama@sh.itjust.works

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Trying something new, going to pin this thread as a place for beginners to ask what may or may not be stupid questions, to encourage both the asking and answering.

Depending on activity level I'll either make a new one once in awhile or I'll just leave this one up forever to be a place to learn and ask.

When asking a question, try to make it clear what your current knowledge level is and where you may have gaps, should help people provide more useful concise answers!

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[–] noneabove1182@sh.itjust.works 3 points 11 months ago (1 children)

If you're using llama.cpp chances are you're already using a quantized model, if not then yes you should be. Unfortunately without crazy fast ram you're basically limited to 7B models if you want any amount of speed (5-10 tokens/s)

[–] stsquad@lemmy.ml 3 points 11 months ago (1 children)

Is there a standard for the suffixes? For example the OpenLlama models here: https://huggingface.co/SlyEcho/open_llama_7b_v2_gguf/tree/main have qN and and then a mix of K, M, 0 and 1 suffixes. The q I assume is the quantisation level but measured how? Does q2 mean t 2bits per weight? That seems very small - and what is it fixed float, integers?

[–] noneabove1182@sh.itjust.works 3 points 11 months ago

Yeah so those are mixed, definitely not putting each individual weight to 2 bits because as you said that's very small, i don't even think it averages out to 2 bits but more like 2.56

You can read some details here on bits per weight: https://huggingface.co/TheBloke/LLaMa-30B-GGML/blob/8c7fb5fb46c53d98ee377f841419f1033a32301d/README.md#explanation-of-the-new-k-quant-methods

Unfortunately this is not the whole story either, as they get further combined with other bits per weight, like q2_k is Q4_K for some of the weights and Q2_K for others, resulting in more like 2.8 bits per weight

Generally speaking you'll want to use Q4_K_M unless going smaller really benefits you (like you can fit the full thing on GPU)

Also, the bigger the model you have (70B vs 7B) the lower you can go on quantization bits before it degrades to complete garbage