Nexesenex/WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant.GGUF

Quants for Sao10K's model WinterGoddess 1.4 70b : https://huggingface.co./Sao10K/WinterGoddess-1.4x-70B-L2

With a twist : the model I used come from a third party, and has been tweaked with limarvp3 and a Linear Rope 8 training to go to 32k context (with even better results in rope 4 and rope 2, maybe other lesser ropes as well)

I don't know who did the job, only that I found this Q4_K_S quant of it hanging around without FP16 : https://huggingface.co./mishima/WinterGoddess-1.4x-limarpv3-70B-L2-32k.GGUF

So I made a Q8_0 out of it (best way to requantize after), and requantized it in :

Full offload possible on 48GB VRAM with a huge context size :

Q3_K_L

Full offload possible on 36GB VRAM with a variable context size (up to 7168 with Q3_K_M, for example)

Q3_K_M, Q3_K_S, Q3_K_XS,
IQ3_XXS SOTA (which is equivalent to a Q3_K_S with more context! (filename is partly wrong, ch2500 is the real values))
Lower quality : Q2_K, Q2_K_S

Full offload possible on 24GB VRAM with a decent context size.

IQ2_XS SOTA (filename is partly wrong, b2035 and ch2500 are the real values)

The higher ch number, the better the quality.

And a bonus to play with it, my KoboldCPP_-v1.55.1.b1933-_Frankenstein from the 21/01/2024 : https://github.com/Nexesenex/kobold.cpp/releases/tag/v1.55.1_b1933

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, 800 tokens). And good news, it lowers the perplexity by :

More than 3% with linear rope 8 (Pos Compress Embeddings) on Q2_K

WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,6.2489,512
WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,6.0482,512

More than 2% with linear ropee 4 on Q2_K

WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.8859,512
WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,4.7739,512

More than 1.5% with linear rope 2 on Q2_K

WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.5030,512
WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K.gguf,-,wikitext,4.42,512

More than 1% with linear rope 8 on Q3_K_S

WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q3_K_S.gguf,-,wikitext,5.6127,512
WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q3_K_S.gguf,-,wikitext,5.5461,512

Edit : A Q3_K_XS, new quant offered in LlamaCPP, is otw, with a iMatrix of ctx 32 with 2500 chunks (so, 80,000 tokens)

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 :

Linear rope 2.5 (max context 10240) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q3_K_S.gguf,-,wikitext,4.0509,512
Linear rope 2.5 (max context 10240) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1952-iMat-c32_ch2500-Q3_K_XS.gguf,-,wikitext,4.2327
Linear rope 2.5 (max context 10240) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.5246,512
Linear rope 2.5 (max context 10240) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-iMat-c32_ch25-Q2_K_S.gguf,-,wikitext,4.6789,512
Linear rope 3 (max context 12288) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.6203,512
Linear rope 3.2 (max context 13107) : WinterGoddess-1.4x-limarpv3-70B-L2-32k-Requant-AR-b1924-Q2_K.gguf,-,wikitext,4.6679,512

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

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

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.

All these results are reproducible with lowers deltas between them for Q3_K_S, and I suppose for other quants as well.

Then, I wonder about applying a NTK rope on the top of it to expend it further, even if it screws with the integrity of numbers in chat). Multiply a linear rope (2, 4, 8, whatever) by 5888 (Alpha 1.6, or RBF 16119.8), 6144 (Alpha 1.8, or RBF 18168.7) and even 7424 (Alpha 2.2, or RBF 22277). This to get a further boost in max context size. Ex with Linear 8 with Alpha 1.8/RBF22277 : 8*7424 = 59392. It's only theorical of course, but worth testing.

Original 70b 4k model perplexity :

WinterGoddess-1.4x-70B-L2.Q3_K_M.gguf,-,wikitext,3.7428,512,PEC1

Benchs of the original Q4_K_S quant I found :

Linear rope 8 10000

WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.2177,4096
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.1324,6144
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.3923,2048
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.4945,1536
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.6700,1024
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,5.2577,512
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,84.5,,400

Linear rope 4 10000

WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.5762,2048
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,4.1235,512
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,87.25,,400

Linear rope 2 10000

WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.3394 *327,2048
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,wikitext,3.8254,512
WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,88,,400

Linear rope 1 10000

WinterGoddess-1.4x-limarpv3-70B-L2-32k.Q4_K_S.gguf,-,hellaswag,85,,400