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3D_Qwen3.6-35B-A3B

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cmh commited on May 1

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.gitattributes +2 -0
3d_Qwen3.6-35B-A3B.html +7 -0
Qwen3.6-35B-A3B-logits.bin.meta +1 -0
Qwen3.6-35B-A3B.log +10 -0
Qwen3.6-35B-A3B_report.md +93 -0
Qwen3.6-35B-A3B_results.csv +86 -0
notes.txt +98 -0
size_kld_plot_Qwen3.6-35B-A3B.png +3 -0
size_ppl_plot_Qwen3.6-35B-A3B.png +3 -0

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- KLD vs PPL vs Size"},"scene":{"xaxis":{"title":{"font":{"color":"#228833","size":12},"text":"Size (GiB) → lower is better"},"type":"linear","gridcolor":"rgba(255,255,255,0.08)","zerolinecolor":"rgba(255,255,255,0.08)","showbackground":true,"backgroundcolor":"#1a1a2e"},"yaxis":{"title":{"font":{"color":"#4477AA","size":12},"text":"KLD (log) → lower is better"},"type":"log","gridcolor":"rgba(255,255,255,0.08)","zerolinecolor":"rgba(255,255,255,0.08)","showbackground":true,"backgroundcolor":"#1a1a2e"},"zaxis":{"title":{"font":{"color":"#EE6677","size":12},"text":"PPL (log) → lower is 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+</body>
+</html>

Qwen3.6-35B-A3B-logits.bin.meta ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"dataset": "C:\\Users\\Windows\\Desktop\\backup\\eval_dataset_260428-0143.txt", "dataset_size": 299472}

Qwen3.6-35B-A3B.log ADDED Viewed

	@@ -0,0 +1,10 @@

+# kld-sweep log - Qwen3.6-35B-A3B
+- [SKIP] Thireus_4.0579bpw - already in results
+- [SKIP] Thireus_4.5077bpw - already in results
+- [SKIP] Thireus_4.8273bpw - already in results
+- [SKIP] Thireus_5.1174bpw - already in results
+- [SKIP] Thireus_5.5079bpw - already in results
+- [SKIP] Thireus_5.7548bpw - already in results
+- [SKIP] Thireus_6.0569bpw - already in results
+- [SKIP] Thireus_6.7626bpw - already in results

Qwen3.6-35B-A3B_report.md ADDED Viewed

	@@ -0,0 +1,93 @@

+# KLD-Sweep Report - Qwen3.6-35B-A3B
+MDL_norm = Size_GiB x 8 + log2(PPL) [bits/token, amortised over 1B tokens]
+## Results (sorted by MDL_norm)
+| Rank | Quantization | Size (GiB) | BPW | PPL | KLD | KLD 99.9% | MDL_norm |
+|------|--------------|------------|-----|-----|-----|-----------|----------|
+| 1 | mudler_APEX-I-Mini | 13.333 | 3.302 | 7.7776 | 0.087271 | 3.065409 | 109.623 |
+| 2 | mudler_APEX-I-Compact | 16.105 | 3.989 | 7.4799 | 0.043432 | 1.820640 | 131.743 |
+| 3 | mudler_APEX-Compact | 16.105 | 3.989 | 7.6257 | 0.062095 | 2.529670 | 131.771 |
+| 4 | Thireus_4.0579bpw | 16.384 | 4.058 | 7.7631 | 0.066857 | 2.273288 | 134.029 |
+| 5 | AesSedai_IQ4_XS | 16.405 | 4.063 | 7.5700 | 0.035996 | 1.669282 | 134.160 |
+| 6 | unsloth_UD-IQ4_XS | 16.513 | 4.090 | 7.5991 | 0.036342 | 1.619864 | 135.030 |
+| 7 | unsloth_UD-IQ4_NL | 16.802 | 4.161 | 7.6022 | 0.036445 | 1.898526 | 137.342 |
+| 8 | mradermacher_i1-IQ4_XS | 17.443 | 4.320 | 7.4260 | 0.035131 | 1.520988 | 142.437 |
+| 9 | batiai_IQ4_XS | 17.443 | 4.320 | 7.4511 | 0.036061 | 1.912124 | 142.441 |
+| 10 | bartowski_IQ4_XS | 17.515 | 4.338 | 7.4094 | 0.032072 | 1.813745 | 143.009 |
+| 11 | unsloth_UD-IQ4_NL_XL | 18.161 | 4.498 | 7.4770 | 0.025694 | 1.354680 | 148.190 |
+| 12 | Thireus_4.5077bpw | 18.262 | 4.523 | 7.6018 | 0.046067 | 1.014056 | 149.022 |
+| 13 | sphaela_Q4_0 | 18.361 | 4.548 | 7.4595 | 0.062128 | 3.098408 | 149.787 |
+| 14 | 0xSero_Q4_0 | 18.361 | 4.548 | 7.4866 | 0.068531 | 3.092771 | 149.792 |
+| 15 | sphaela_Q4_K_M | 18.361 | 4.548 | 7.5156 | 0.038733 | 1.833427 | 149.798 |
+| 16 | sphaela_Q4_K_S | 18.361 | 4.548 | 7.5156 | 0.038733 | 1.833427 | 149.798 |
+| 17 | mradermacher_i1-Q4_0 | 18.440 | 4.567 | 7.4737 | 0.059360 | 3.349420 | 150.422 |
+| 18 | bartowski_IQ4_NL | 18.497 | 4.582 | 7.4112 | 0.031247 | 1.591901 | 150.866 |
+| 19 | mradermacher_i1-Q4_K_S | 18.524 | 4.588 | 7.4998 | 0.035967 | 1.891432 | 151.099 |
+| 20 | worthdoing_Q4_K_S | 18.524 | 4.588 | 7.7884 | 0.068062 | 3.560123 | 151.153 |
+| 21 | bartowski_Q4_0 | 18.573 | 4.600 | 7.3794 | 0.040132 | 2.292114 | 151.467 |
+| 22 | 0xSero_IQ4_NL | 18.578 | 4.602 | 7.4506 | 0.047391 | 2.141885 | 151.521 |
+| 23 | MXFP4_MOE | 18.875 | 4.675 | 7.3723 | 0.037835 | 1.747079 | 153.882 |
+| 24 | 0xSero_DYNAMIC | 19.075 | 4.725 | 7.4837 | 0.042753 | 2.160737 | 155.504 |
+| 25 | bartowski_Q4_K_S | 19.178 | 4.750 | 7.4696 | 0.027690 | 1.507139 | 156.325 |
+| 26 | unsloth_UD-Q4_K_S | 19.458 | 4.820 | 7.4220 | 0.020062 | 1.255272 | 158.556 |
+| 27 | Thireus_4.8273bpw | 19.551 | 4.843 | 7.5792 | 0.043324 | 0.879808 | 159.330 |
+| 28 | mradermacher_i1-Q4_K_M | 19.713 | 4.883 | 7.4637 | 0.031677 | 1.500736 | 160.604 |
+| 29 | batiai_Q4_K_M | 19.713 | 4.883 | 7.4642 | 0.031505 | 1.606371 | 160.604 |
+| 30 | lmstudio_Q4_K_M | 19.713 | 4.883 | 7.6538 | 0.055895 | 2.917688 | 160.640 |
+| 31 | 0xSero_Q4_K_M | 19.713 | 4.883 | 7.6538 | 0.055895 | 2.917688 | 160.640 |
+| 32 | worthdoing_Q4_K_M | 19.713 | 4.883 | 7.6606 | 0.056077 | 2.750742 | 160.641 |
+| 33 | bartowski_Q4_K_M | 19.922 | 4.935 | 7.4335 | 0.025066 | 1.385922 | 162.270 |
+| 34 | steampunque_Q4_K_H | 19.925 | 4.935 | 7.5384 | 0.041208 | 2.585083 | 162.314 |
+| 35 | unsloth_MXFP4_MOE | 20.215 | 5.007 | 7.3468 | 0.026420 | 1.186455 | 164.597 |
+| 36 | bartowski_Q4_K_L | 20.274 | 5.022 | 7.3952 | 0.022723 | 1.161044 | 165.079 |
+| 37 | sphaela_Q4_1 | 20.348 | 5.040 | 7.5230 | 0.082678 | 3.324150 | 165.695 |
+| 38 | mradermacher_i1-Q4_1 | 20.348 | 5.040 | 7.5298 | 0.038358 | 2.079206 | 165.697 |
+| 39 | bartowski_Q4_1 | 20.465 | 5.069 | 7.4388 | 0.030026 | 1.434557 | 166.615 |
+| 40 | unsloth_UD-Q4_K_M | 20.614 | 5.106 | 7.4003 | 0.016585 | 0.791560 | 167.800 |
+| 41 | AesSedai_Q4_K_M | 20.624 | 5.109 | 7.3671 | 0.016405 | 0.962259 | 167.873 |
+| 42 | Thireus_5.1174bpw | 20.659 | 5.117 | 7.5287 | 0.041167 | 0.928671 | 168.184 |
+| 43 | unsloth_UD-Q4_K_XL | 20.825 | 5.158 | 7.3936 | 0.016808 | 1.070779 | 169.486 |
+| 44 | mudler_APEX-I-Quality | 21.252 | 5.264 | 7.3348 | 0.017030 | 1.277997 | 172.891 |
+| 45 | mudler_APEX-Quality | 21.252 | 5.264 | 7.3965 | 0.018793 | 1.241888 | 172.903 |
+| 46 | Thireus_5.5079bpw | 22.235 | 5.508 | 7.5635 | 0.039310 | 0.820752 | 180.799 |
+| 47 | mradermacher_i1-Q5_K_S | 22.334 | 5.533 | 7.3590 | 0.020913 | 1.305873 | 181.552 |
+| 48 | worthdoing_Q5_K_S | 22.334 | 5.533 | 7.4318 | 0.027020 | 1.514734 | 181.566 |
+| 49 | sphaela_Q5_0 | 22.334 | 5.533 | 7.4779 | 0.030836 | 2.128659 | 181.575 |
+| 50 | sphaela_Q5_K_M | 22.334 | 5.533 | 7.7613 | 0.063122 | 2.978244 | 181.628 |
+| 51 | sphaela_Q5_K_S | 22.334 | 5.533 | 7.7613 | 0.063122 | 2.978244 | 181.628 |
+| 52 | bartowski_Q5_K_S | 22.497 | 5.573 | 7.3770 | 0.016080 | 0.877640 | 182.859 |
+| 53 | mradermacher_i1-Q5_K_M | 23.031 | 5.705 | 7.3551 | 0.020523 | 1.355754 | 187.127 |
+| 54 | batiai_Q5_K_M | 23.031 | 5.705 | 7.3827 | 0.018182 | 1.007116 | 187.132 |
+| 55 | worthdoing_Q5_K_M | 23.031 | 5.705 | 7.4204 | 0.025840 | 1.453986 | 187.139 |
+| 56 | 0xSero_Q5_K_M | 23.031 | 5.705 | 7.4258 | 0.026154 | 1.687057 | 187.141 |
+| 57 | unsloth_UD-Q5_K_S | 23.229 | 5.754 | 7.3434 | 0.012991 | 0.971944 | 188.708 |
+| 58 | Thireus_5.7548bpw | 23.231 | 5.755 | 7.5646 | 0.038382 | 0.957410 | 188.767 |
+| 59 | bartowski_Q5_K_M | 23.299 | 5.772 | 7.3819 | 0.014800 | 0.816861 | 189.276 |
+| 60 | bartowski_Q5_K_L | 23.592 | 5.844 | 7.3517 | 0.014274 | 1.021982 | 191.614 |
+| 61 | mudler_APEX-I-Balanced | 23.865 | 5.912 | 7.3451 | 0.015009 | 1.151828 | 193.797 |
+| 62 | mudler_APEX-Balanced | 23.865 | 5.912 | 7.3853 | 0.016136 | 0.978521 | 193.805 |
+| 63 | sphaela_Q5_1 | 24.321 | 6.025 | 7.3615 | 0.033787 | 2.046225 | 197.448 |
+| 64 | Thireus_6.0569bpw | 24.450 | 6.057 | 7.3701 | 0.010921 | 0.621259 | 198.482 |
+| 65 | AesSedai_Q5_K_M | 24.452 | 6.057 | 7.3560 | 0.011521 | 0.808359 | 198.495 |
+| 66 | unsloth_UD-Q5_K_M | 24.639 | 6.104 | 7.3397 | 0.011592 | 0.777676 | 199.988 |
+| 67 | unsloth_UD-Q5_K_XL | 24.766 | 6.135 | 7.3527 | 0.011761 | 0.770376 | 201.006 |
+| 68 | mradermacher_i1-Q6_K | 26.556 | 6.579 | 7.3649 | 0.013445 | 1.010739 | 215.329 |
+| 69 | batiai_Q6_K | 26.556 | 6.579 | 7.3888 | 0.014525 | 0.874168 | 215.333 |
+| 70 | 0xSero_Q6_K | 26.556 | 6.579 | 7.3970 | 0.014817 | 0.819281 | 215.335 |
+| 71 | lmstudio_Q6_K | 26.556 | 6.579 | 7.3971 | 0.014816 | 0.819281 | 215.335 |
+| 72 | AesSedai_Q6_K | 27.108 | 6.716 | 7.3691 | 0.010935 | 0.785779 | 219.745 |
+| 73 | unsloth_UD-Q6_K | 27.296 | 6.762 | 7.3622 | 0.010109 | 0.751166 | 221.248 |
+| 74 | Thireus_6.7626bpw | 27.298 | 6.763 | 7.3693 | 0.010479 | 0.699136 | 221.266 |
+| 75 | bartowski_Q6_K | 27.989 | 6.934 | 7.3830 | 0.010854 | 0.731767 | 226.796 |
+| 76 | bartowski_Q6_K_L | 28.219 | 6.991 | 7.3683 | 0.010529 | 0.764405 | 228.633 |
+| 77 | unsloth_UD-Q6_K_XL | 29.657 | 7.347 | 7.3781 | 0.009570 | 0.658147 | 240.139 |
+| 78 | sphaela_Q8_0 | 34.369 | 8.515 | 7.3491 | 0.009761 | 0.676150 | 277.830 |
+| 79 | 0xSero_Q8_0 | 34.369 | 8.515 | 7.3553 | 0.009181 | 0.640894 | 277.831 |
+| 80 | lmstudio_Q8_0 | 34.369 | 8.515 | 7.3554 | 0.009182 | 0.640894 | 277.831 |
+| 81 | unsloth_Q8_0 | 34.369 | 8.515 | 7.3606 | 0.009532 | 0.783307 | 277.832 |
+| 82 | batiai_Q8_0 | 34.369 | 8.515 | 7.3624 | 0.009635 | 0.875084 | 277.832 |
+| 83 | worthdoing_Q8_0 | 34.369 | 8.515 | 7.3633 | 0.009321 | 0.691923 | 277.832 |
+| 84 | bartowski_Q8_0 | 34.379 | 8.518 | 7.3544 | 0.009451 | 0.733638 | 277.911 |
+| 85 | unsloth_UD-Q8_K_XL | 35.810 | 8.872 | 7.3609 | 0.009616 | 0.747915 | 289.360 |

Qwen3.6-35B-A3B_results.csv ADDED Viewed

	@@ -0,0 +1,86 @@

+Quantization,Size_GiB,PPL_Score,KLD_Score,MDL_norm,Num_Tokens,KLD_99,BPW
+mradermacher_i1-IQ4_XS,17.443,7.426015,0.035131,142.43658822933267,72192,1.520988,4.320
+mradermacher_i1-Q4_0,18.44,7.473715,0.059360,150.42182554954604,72192,3.349420,4.567
+mradermacher_i1-Q4_K_S,18.524,7.499771,0.035967,151.09884654464742,72192,1.891432,4.588
+mradermacher_i1-Q4_K_M,19.713,7.463653,0.031677,160.603881914001,72192,1.500736,4.883
+mradermacher_i1-Q4_1,20.348,7.529849,0.038358,165.6966209340691,72192,2.079206,5.040
+mradermacher_i1-Q5_K_S,22.334,7.359026,0.020913,181.55151483177727,72192,1.305873,5.533
+mradermacher_i1-Q5_K_M,23.031,7.355089,0.020523,187.12674279886716,72192,1.355754,5.705
+mradermacher_i1-Q6_K,26.556,7.364891,0.013445,215.3286641736083,72192,1.010739,6.579
+batiai_IQ4_XS,17.443,7.451128,0.036061,142.44145884662444,72192,1.912124,4.320
+batiai_Q4_K_M,19.713,7.464193,0.031505,160.60398629012096,72192,1.606371,4.883
+batiai_Q5_K_M,23.031,7.382705,0.018182,187.13214951209437,72192,1.007116,5.705
+batiai_Q6_K,26.556,7.388789,0.014525,215.33333793042567,72192,0.874168,6.579
+batiai_Q8_0,34.369,7.362404,0.009635,277.8321769174488,72192,0.875084,8.515
+sphaela_Q4_0,18.361,7.459525,0.062128,149.7870837669467,72192,3.098408,4.548
+sphaela_Q4_K_M,18.361,7.515641,0.038733,149.79789615511754,72192,1.833427,4.548
+sphaela_Q4_K_S,18.361,7.515641,0.038733,149.79789615511754,72192,1.833427,4.548
+0xSero_Q4_0,18.361,7.486566,0.068531,149.79230412311435,72192,3.092771,4.548
+worthdoing_Q4_K_S,18.524,7.788397,0.068062,151.15332642480467,72192,3.560123,4.588
+0xSero_IQ4_NL,18.578,7.450601,0.047391,151.5213568047356,72192,2.141885,4.602
+MXFP4_MOE,18.875,7.372258,0.037835,153.8821065604976,72192,1.747079,4.675
+0xSero_DYNAMIC,19.075,7.483691,0.042753,155.50374999123892,72192,2.160737,4.725
+0xSero_Q4_K_M,19.713,7.653804,0.055895,160.64017695668338,72192,2.917688,4.883
+worthdoing_Q4_K_M,19.713,7.660640,0.056077,160.64146492560403,72192,2.750742,4.883
+steampunque_Q4_K_H,19.925,7.538357,0.041208,162.31425011948275,72192,2.585083,4.935
+sphaela_Q4_1,20.348,7.522974,0.082678,165.69530310430446,72192,3.324150,5.040
+sphaela_Q5_0,22.334,7.477889,0.030836,181.57463105633408,72192,2.128659,5.533
+sphaela_Q5_K_M,22.334,7.761252,0.063122,181.62828939835262,72192,2.978244,5.533
+sphaela_Q5_K_S,22.334,7.761252,0.063122,181.62828939835262,72192,2.978244,5.533
+worthdoing_Q5_K_S,22.334,7.431824,0.027020,181.56571633630966,72192,1.514734,5.533
+0xSero_Q5_K_M,23.031,7.425827,0.026154,187.14055170501902,72192,1.687057,5.705
+worthdoing_Q5_K_M,23.031,7.420394,0.025840,187.13949579153004,72192,1.453986,5.705
+sphaela_Q5_1,24.321,7.361528,0.033787,197.4480052512312,72192,2.046225,6.025
+0xSero_Q6_K,26.556,7.397018,0.014817,215.3349437864628,72192,0.819281,6.579
+sphaela_Q8_0,34.369,7.349076,0.009761,277.8295628711712,72192,0.676150,8.515
+0xSero_Q8_0,34.369,7.355320,0.009181,277.8307881086277,72192,0.640894,8.515
+worthdoing_Q8_0,34.369,7.363255,0.009321,277.83234366495816,72192,0.691923,8.515
+AesSedai_IQ4_XS,16.405,7.570040,0.035996,134.16030092341435,72192,1.669282,4.063
+AesSedai_Q4_K_M,20.624,7.367143,0.016405,167.87310524634236,72192,0.962259,5.109
+AesSedai_Q5_K_M,24.452,7.355996,0.011521,198.4949206952499,72192,0.808359,6.057
+AesSedai_Q6_K,27.108,7.369134,0.010935,219.7454950877988,72192,0.785779,6.716
+bartowski_IQ4_XS,17.515,7.409366,0.032072,143.0093501001846,72192,1.813745,4.338
+bartowski_IQ4_NL,18.497,7.411187,0.031247,150.86570462780347,72192,1.591901,4.582
+bartowski_Q4_0,18.573,7.379371,0.040132,151.46749784963876,72192,2.292114,4.600
+bartowski_Q4_K_S,19.178,7.469551,0.027690,156.32502152420034,72192,1.507139,4.750
+bartowski_Q4_K_M,19.922,7.433477,0.025066,162.27003718752047,72192,1.385922,4.935
+bartowski_Q4_K_L,20.274,7.395230,0.022723,165.07859501746626,72192,1.161044,5.022
+bartowski_Q4_1,20.465,7.438778,0.030026,166.615065643089,72192,1.434557,5.069
+bartowski_Q5_K_S,22.497,7.377050,0.016080,182.8590440139536,72192,0.877640,5.573
+bartowski_Q5_K_M,23.299,7.381860,0.014800,189.2759843765403,72192,0.816861,5.772
+bartowski_Q5_K_L,23.592,7.351717,0.014274,191.61408123200306,72192,1.021982,5.844
+bartowski_Q6_K,27.989,7.383049,0.010854,226.7962167334627,72192,0.731767,6.934
+bartowski_Q6_K_L,28.219,7.368314,0.010529,228.63333454305118,72192,0.764405,6.991
+bartowski_Q8_0,34.379,7.354382,0.009451,277.9106041146963,72192,0.733638,8.518
+unsloth_UD-IQ4_XS,16.513,7.599112,0.036342,135.02983084118173,72192,1.619864,4.090
+unsloth_UD-IQ4_NL,16.802,7.602229,0.036445,137.3424224837882,72192,1.898526,4.161
+unsloth_UD-IQ4_NL_XL,18.161,7.476980,0.025694,148.1904556740163,72192,1.354680,4.498
+unsloth_UD-Q4_K_S,19.458,7.422022,0.020062,158.5558122774089,72192,1.255272,4.820
+unsloth_MXFP4_MOE,20.215,7.346848,0.026420,164.5971254268056,72192,1.186455,5.007
+unsloth_UD-Q4_K_M,20.614,7.400288,0.016585,167.79958141778033,72192,0.791560,5.106
+unsloth_UD-Q4_K_XL,20.825,7.393556,0.016808,169.4862684089794,72192,1.070779,5.158
+unsloth_UD-Q5_K_S,23.229,7.343419,0.012991,188.7084519194575,72192,0.971944,5.754
+unsloth_UD-Q5_K_M,24.639,7.339658,0.011592,199.98771284056178,72192,0.777676,6.104
+unsloth_UD-Q5_K_XL,24.766,7.352658,0.011761,201.0062658812704,72192,0.770376,6.135
+unsloth_UD-Q6_K,27.296,7.362207,0.010109,221.2481383139267,72192,0.751166,6.762
+unsloth_UD-Q6_K_XL,29.657,7.378086,0.009570,240.13924660538547,72192,0.658147,7.347
+unsloth_Q8_0,34.369,7.360562,0.009532,277.83181592438814,72192,0.783307,8.515
+unsloth_UD-Q8_K_XL,35.81,7.360872,0.009616,289.3598766841569,72192,0.747915,8.872
+lmstudio_Q4_K_M,19.713,7.653804,0.055895,160.64017695668338,72192,2.917688,4.883
+lmstudio_Q6_K,26.556,7.397056,0.014816,215.33495119786434,72192,0.819281,6.579
+lmstudio_Q8_0,34.369,7.355369,0.009182,277.83079771960604,72192,0.640894,8.515
+mudler_APEX-I-Mini,13.333,7.777609,0.087271,109.6233267086806,72192,3.065409,3.302
+mudler_APEX-Compact,16.105,7.625708,0.062095,131.7708712891263,72192,2.529670,3.989
+mudler_APEX-I-Compact,16.105,7.479947,0.043432,131.74302804777227,72192,1.820640,3.989
+mudler_APEX-Quality,21.252,7.396510,0.018793,172.90284470406928,72192,1.241888,5.264
+mudler_APEX-I-Quality,21.252,7.334849,0.017030,172.89076726594044,72192,1.277997,5.264
+mudler_APEX-Balanced,23.865,7.385297,0.016136,193.80465594019267,72192,0.978521,5.912
+mudler_APEX-I-Balanced,23.865,7.345131,0.015009,193.79678822139624,72192,1.151828,5.912
+Thireus_4.0579bpw,16.384,7.763078,0.066857,134.02862878319652,72192,2.273288,4.058
+Thireus_4.5077bpw,18.262,7.601767,0.046067,149.02233480616593,72192,1.014056,4.523
+Thireus_4.8273bpw,19.551,7.579173,0.043324,159.330040437593,72192,0.879808,4.843
+Thireus_5.1174bpw,20.659,7.528659,0.041167,168.18439291584815,72192,0.928671,5.117
+Thireus_5.5079bpw,22.235,7.563483,0.039310,180.7990507515971,72192,0.820752,5.508
+Thireus_5.7548bpw,23.231,7.564565,0.038382,188.76725712270814,72192,0.957410,5.755
+Thireus_6.0569bpw,24.45,7.370104,0.010921,198.48168497742424,72192,0.621259,6.057
+Thireus_6.7626bpw,27.298,7.369260,0.010479,221.2655197552863,72192,0.699136,6.763

notes.txt ADDED Viewed

	@@ -0,0 +1,98 @@

+Evaluated on eval_dataset_260428-0143.txt 141 chunks, n_ctx=512, batch_size=2048, n_seq=4
+Windows 11 26200.8246
+Nvidia drivers 596.21
+ik_llama.cpp
+https://github.com/Thireus/ik_llama.cpp/releases/tag/main-b4660-04d5ead
+Windows x64 (CUDA 12.8) AVX2 - With Libraries
+----
+Perplexity (PPL)
+Measures how surprised the model is by a dataset (prediction quality). Lower is better. It can be converted into bits per token, which is used for MDL.
+Kullback–Leibler Divergence (KLD)
+Measures how much the probability distribution of outputs changed vs the original FP16 model (faithfulness). Lower is better.
+Minimum Description Length (MDL)
+Total cost to represent the dataset using the model (efficiency):
+☝️🤓  Error cost = log₂(PPL) × N  ☝️🤓
+Simply put MDL = Model Cost (bits) + Error Cost (bits) where:
+- Model cost: size of quantized weights (bits)
+- Error cost: extra bits needed due to imperfect predictions (computed from PPL as bits per token) over a lifetime of N tokens (I picked 1 Billion tokens).
+A quantization scheme optimized for low PPL may still shift the probability distribution, causing a higher KLD even if perplexity looks fine (rare but not impossible).  So a good quant would have both a low MDL (efficient) and a low KLD (faithful).
+Points on the frontier are "Pareto-optimal", no other quant in the dataset beats them on both axes simultaneously. Others are either larger, less faithful, or both.
+Choose a point on this line based on your VRAM and how much fidelity you want.
+There is no single "best" quant, the right choice depends on whether you prioritize efficiency or fidelity.
+Note: MDL ranking is context-dependent that's why I share my pseudo-randomly generated dataset for reproducibility (141 chunks  at -c 512 generated from eaddario/imatrix-calibration on HF).
+----
+download script
+mkdir ~/Desktop/backup/models/quants/repo
+cd ~/Desktop/backup/models/quants/repo
+hf download --local-dir . --exclude "*.png" --exclude  README.md --exclude  .gitattributes --exclude "mmproj*" --exclude "*Q1*.gguf" --exclude "*Q2*.gguf" --exclude "*Q3*.gguf" --exclude "*F16*.gguf" --exclude "*imatrix*" --exclude "*assets*" repo/model-GGUF
+test script
+cd C:\Users\Windows\Desktop\backup\references\kld-sweep
+python .\kld_sweep.py --exe C:\Users\Windows\Desktop\backup\ik\llama-perplexity.exe --baseline C:\Users\Windows\Desktop\backup\models\baseline\Qwen_Qwen3.6-35B-A3B-bf16\Qwen_Qwen3.6-35B-A3B-bf16-00001-of-00002.gguf --quants C:\Users\Windows\Desktop\backup\models\quants\ --dataset C:\Users\Windows\Desktop\backup\eval_dataset_260428-0143.txt --output C:\Users\Windows\Desktop\backup\output\ --args="-t 7 -c 512 -ngl 99 -ncmoe 32 --no-mmap" --model-name Qwen3.6-35B-A3B --logit C:\Users\Windows\Desktop\backup\output\Qwen3.6-35B-A3B-logits.bin
+----
+MDL vs Size:
+$ python3 -c "
+import pandas as pd
+import numpy as np
+df = pd.read_csv('/mnt/c/Users/Windows/Desktop/backup/output/Qwen3.6-35B-A3B_results.csv')
+df = df[df['PPL_Score'] != 'ERROR']
+df['Size_GiB'] = pd.to_numeric(df['Size_GiB'], errors='coerce')
+df['PPL_Score'] = pd.to_numeric(df['PPL_Score'], errors='coerce')
+df['KLD_Score'] = pd.to_numeric(df['KLD_Score'], errors='coerce')
+df['MDL_norm'] = pd.to_numeric(df['MDL_norm'], errors='coerce')
+df = df.dropna(subset=['Size_GiB', 'PPL_Score', 'KLD_Score', 'MDL_norm'])
+# pearson correlation
+corr = df['Size_GiB'].corr(df['MDL_norm'])
+print(f'Pearson(Size, MDL_norm) = {corr:.6f}')
+# spearman (rank) correlation
+scorr = df['Size_GiB'].corr(df['MDL_norm'], method='spearman')
+print(f'Spearman(Size, MDL_norm) = {scorr:.6f}')
+# linear regression r-squared
+from numpy.polynomial.polynomial import polyfit
+b, m = polyfit(df['Size_GiB'].values, df['MDL_norm'].values, 1)
+predicted = b + m * df['Size_GiB'].values
+ss_res = np.sum((df['MDL_norm'].values - predicted) ** 2)
+ss_tot = np.sum((df['MDL_norm'].values - np.mean(df['MDL_norm'].values)) ** 2)
+r2 = 1 - ss_res / ss_totjust paste
+print(f'R²(Size → MDL_norm) = {r2:.6f}')
+print(f'MDL_norm ≈ {m:.3f} × Size + {b:.3f}')
+# range check
+print(f'Size range: {df[\"Size_GiB\"].min():.1f} - {df[\"Size_GiB\"].max():.1f} GiB')
+print(f'MDL range: {df[\"MDL_norm\"].min():.1f} - {df[\"MDL_norm\"].max():.1f}')
+print(f'PPL range: {df[\"PPL_Score\"].min():.4f} - {df[\"PPL_Score\"].max():.4f}')
+print(f'log2(PPL) range: {np.log2(df[\"PPL_Score\"].min()):.3f} - {np.log2(df[\"PPL_Score\"].max()):.3f}')
+" 2>&1
+Pearson(Size, MDL_norm) = 1.000000
+Spearman(Size, MDL_norm) = 0.999256
+R²(Size → MDL_norm) = 1.000000
+MDL_norm ≈ 7.998 × Size + 2.942
+Size range: 16.4 - 34.4 GiB
+MDL range: 134.2 - 277.9
+PPL range: 7.3397 - 7.7884
+log2(PPL) range: 2.876 - 2.961
+MDL_norm ≈ 8×Size + 2.9 because log2(PPL) is nearly constant across all quants (range 2.88-2.96), so MDL_norm carries not much information beyond size for this model because it's an MoE (with only 8 routed experts out of 256, 8.6% activation ratio). A sparse model like good ol mixtral at 25% would see more variance and a dense model would show much wider PPL.

size_kld_plot_Qwen3.6-35B-A3B.png ADDED Viewed

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SHA256: ca408280b062f0213c36da4ed083faed75988ae7a00a2c4c0802ffce2402af32
Pointer size: 131 Bytes
Size of remote file: 400 kB

size_ppl_plot_Qwen3.6-35B-A3B.png ADDED Viewed

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SHA256: 11331ecdc46ee06cdb12497bc43006ac81edfe29e281f2ec7744ae30152b4c7d
Pointer size: 131 Bytes
Size of remote file: 383 kB