this post was submitted on 02 Oct 2023
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LocalLLaMA
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Community to discuss about LLaMA, the large language model created by Meta AI.
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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