GGML转换脚本详见:

https://github.com/LinkSoul-AI/Chinese-Llama-2-7b/tree/main/ggml

量化配置的定义:

转自: https://www.reddit.com/r/LocalLLaMA/comments/139yt87/notable_differences_between_q4_2_and_q5_1/

• q4_0 = 32 numbers in chunk, 4 bits per weight, 1 scale value at 32-bit float (5 bits per value in average), each weight is given by the common scale * quantized value.

• q4_1 = 32 numbers in chunk, 4 bits per weight, 1 scale value and 1 bias value at 32-bit float (6 bits per value in average), each weight is given by the common scale * quantized value + common bias.

• q4_2 = same as q4_0, but 16 numbers in chunk, 4 bits per weight, 1 scale value that is 16-bit float, same size as q4_0 but better because chunks are smaller.

• q4_3 = already dead, but analogous: q4_1 but 16 numbers in chunk, 4 bits per weight, scale value that is 16 bit and bias also 16 bits, same size as q4_1 but better because chunks are smaller.

• q5_0 = 32 numbers in chunk, 5 bits per weight, 1 scale value at 16-bit float, size is 5.5 bits per weight

• q5_1 = 32 numbers in a chunk, 5 bits per weight, 1 scale value at 16 bit float and 1 bias value at 16 bit, size is 6 bits per weight.

• q8_0 = same as q4_0, except 8 bits per weight, 1 scale value at 32 bits, making total of 9 bits per weight.