Update README.md
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README.md
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@@ -18,39 +18,39 @@ And a bonus to play with it, my KoboldCPP_-_v1.55.1.b1933_-_Frankenstein from th
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Edit : Due to a poor CPU (i7-6700k) for AI purpose, and only 36GB of VRAM, I remade Q3_K_S and Q2_K with an small iMatrix of ctx 32 with 25 chunks (so, 640 tokens).
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And good news, it lowers the perplexity by :
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More than 3%
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,6.2489,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,6.0482,512
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More than 2%
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.8859,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,4.7739,512
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More than 1.5%
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.5030,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,4.42,512
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More than 1% with
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q3_K_S.gguf,-,wikitext,5.6127,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q3_K_S.gguf,-,wikitext,5.5461,512
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A Q3_K_M with iMatrix has been added as well.
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Interestingly,
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And for the adventurous,
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- Minus 3% With my Q2_K with c32ch25 iMatrix : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,6.9405,512
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So the linear rope, at least on this model, is flexible, and you can lower it to have the best peplexity for your max context.
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@@ -66,7 +66,7 @@ It's only theorical of course, but worth testing.
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Benchs of the original Q4_K_S quant I found :
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.2177,4096
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.1324,6144
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@@ -76,18 +76,18 @@ WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.6700,1024
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,5.2577,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,84.5,,400
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.5762,2048
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.1235,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,87.25,,400
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.3394 *327,2048
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.8254,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,88,,400
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,85,,400
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Edit : Due to a poor CPU (i7-6700k) for AI purpose, and only 36GB of VRAM, I remade Q3_K_S and Q2_K with an small iMatrix of ctx 32 with 25 chunks (so, 640 tokens).
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And good news, it lowers the perplexity by :
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More than 3% with linear rope 8 on Q2_K
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,6.2489,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,6.0482,512
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More than 2% with linear ropee 4 on Q2_K
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.8859,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,4.7739,512
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More than 1.5% with linear rope 2 on Q2_K
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.5030,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,4.42,512
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More than 1% with linear rope 8 on Q3_K_S
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q3_K_S.gguf,-,wikitext,5.6127,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q3_K_S.gguf,-,wikitext,5.5461,512
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A Q3_K_M with iMatrix has been added as well, and a Q2_K_S is otw.
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Interestingly, linear rope 2.5 (and linear rope 1.6 as well after further testing) is almost without loss compared to linear rope 2, while 3 and 3.2 are quite good. Here are the values with the normal Q2_K :
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Linear rope 2.5 (max context 10240) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.5246,512
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Linear rope 3 (max context 12288) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.6203,512
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Linear rope 3.2 (max context 13107) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.6679,512
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And for the adventurous, linear rope 10 : (max context 40960) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,7.1577,512
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- Minus 3% With my Q2_K with c32ch25 iMatrix : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,6.9405,512
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So the linear rope, at least on this model, is flexible, and you can lower it to have the best peplexity for your max context.
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Benchs of the original Q4_K_S quant I found :
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Linear rope 8 10000
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.2177,4096
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.1324,6144
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,5.2577,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,84.5,,400
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Linear rope 4 10000
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.5762,2048
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.1235,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,87.25,,400
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Linear rope 2 10000
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.3394 *327,2048
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.8254,512
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,88,,400
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Linear rope 1 10000
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WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,85,,400
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