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llama3-prot_drug_v2

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 6.2640

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-07
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 150
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss
10.7153 0.001 10 10.7478
10.8484 0.002 20 10.7517
10.7727 0.003 30 10.7217
10.6983 0.004 40 10.6736
10.4947 0.005 50 10.5232
10.3766 0.006 60 10.4352
10.2218 0.007 70 10.0634
10.0225 0.008 80 9.9000
9.6064 0.009 90 9.5760
9.3867 0.01 100 9.3304
8.888 0.011 110 9.0533
8.8849 0.012 120 8.8523
8.6139 0.013 130 8.6901
8.7757 0.014 140 8.5438
8.686 0.015 150 8.4592
8.4797 0.016 160 8.3546
8.157 0.017 170 8.2745
8.2998 0.018 180 8.2309
8.4686 0.019 190 8.1551
8.2645 0.02 200 8.1347
8.1665 0.021 210 8.1392
8.0443 0.022 220 8.0785
8.2958 0.023 230 8.0715
8.2119 0.024 240 8.0218
8.4549 0.025 250 8.0328
8.1364 0.026 260 8.0321
7.9616 0.027 270 7.9609
8.1311 0.028 280 7.9735
8.1387 0.029 290 7.9459
7.584 0.03 300 7.9298
7.7707 0.031 310 7.9249
7.9065 0.032 320 7.8802
8.0087 0.033 330 7.8810
7.9511 0.034 340 7.8577
7.9773 0.035 350 7.8493
7.8037 0.036 360 7.8506
8.0945 0.037 370 7.8463
8.017 0.038 380 7.8009
7.9568 0.039 390 7.7834
7.8262 0.04 400 7.8061
7.8334 0.041 410 7.7970
7.9111 0.042 420 7.7959
8.1254 0.043 430 7.7688
7.931 0.044 440 7.7839
7.9224 0.045 450 7.7457
7.7798 0.046 460 7.7710
7.7813 0.047 470 7.7617
7.8688 0.048 480 7.7574
7.8181 0.049 490 7.7576
7.7763 0.05 500 7.7110
7.5074 0.051 510 7.7316
7.9088 0.052 520 7.7298
8.1888 0.053 530 7.7068
7.9626 0.054 540 7.7003
7.696 0.055 550 7.6885
7.8581 0.056 560 7.7178
7.615 0.057 570 7.6757
8.0202 0.058 580 7.6819
7.7913 0.059 590 7.6960
7.8504 0.06 600 7.6510
7.7702 0.061 610 7.6753
7.8348 0.062 620 7.6430
7.8706 0.063 630 7.6671
8.0807 0.064 640 7.6521
7.6805 0.065 650 7.6517
7.6537 0.066 660 7.6361
7.6313 0.067 670 7.6464
7.6202 0.068 680 7.6558
7.7252 0.069 690 7.6546
7.6741 0.07 700 7.6762
7.7034 0.071 710 7.6547
7.784 0.072 720 7.6168
7.596 0.073 730 7.6501
7.8139 0.074 740 7.6211
7.6019 0.075 750 7.6373
7.6452 0.076 760 7.6198
7.599 0.077 770 7.5939
7.555 0.078 780 7.5730
7.557 0.079 790 7.5882
7.772 0.08 800 7.5986
7.9055 0.081 810 7.5987
7.8603 0.082 820 7.6084
7.6425 0.083 830 7.5835
7.8842 0.084 840 7.5684
7.6837 0.085 850 7.5814
7.4685 0.086 860 7.5834
7.6788 0.087 870 7.5973
7.6038 0.088 880 7.5924
7.3791 0.089 890 7.5411
7.7098 0.09 900 7.5682
7.7011 0.091 910 7.5865
7.9424 0.092 920 7.5621
7.705 0.093 930 7.5840
7.7356 0.094 940 7.5597
7.2699 0.095 950 7.5555
7.5929 0.096 960 7.5714
7.654 0.097 970 7.5509
7.4402 0.098 980 7.5660
7.5828 0.099 990 7.5815
7.3925 0.1 1000 7.5767
7.5815 0.101 1010 7.5630
7.6269 0.102 1020 7.5391
7.3241 0.103 1030 7.5381
7.415 0.104 1040 7.5709
7.6907 0.105 1050 7.5296
7.5834 0.106 1060 7.5505
7.4632 0.107 1070 7.5154
7.6087 0.108 1080 7.5336
7.3708 0.109 1090 7.5293
7.4298 0.11 1100 7.5476
7.3913 0.111 1110 7.5289
7.6552 0.112 1120 7.5263
7.6986 0.113 1130 7.5225
7.5495 0.114 1140 7.5424
7.6483 0.115 1150 7.5533
7.5431 0.116 1160 7.5334
7.5996 0.117 1170 7.5310
7.5914 0.118 1180 7.5250
7.5007 0.119 1190 7.4764
7.5333 0.12 1200 7.5183
7.413 0.121 1210 7.4818
7.6432 0.122 1220 7.5253
7.763 0.123 1230 7.5414
7.8872 0.124 1240 7.5230
7.6547 0.125 1250 7.5066
7.3595 0.126 1260 7.4929
7.3557 0.127 1270 7.5040
7.5156 0.128 1280 7.4922
7.4351 0.129 1290 7.5077
7.8876 0.13 1300 7.5163
7.4806 0.131 1310 7.5112
7.6503 0.132 1320 7.4812
7.5374 0.133 1330 7.5105
7.6491 0.134 1340 7.4796
7.6354 0.135 1350 7.4870
7.2903 0.136 1360 7.4715
7.5178 0.137 1370 7.4815
7.5023 0.138 1380 7.4957
7.4124 0.139 1390 7.5131
7.4322 0.14 1400 7.4926
7.588 0.141 1410 7.4883
7.8409 0.142 1420 7.4956
7.3885 0.143 1430 7.4751
7.3501 0.144 1440 7.4603
7.6181 0.145 1450 7.4990
7.3009 0.146 1460 7.5157
7.5914 0.147 1470 7.4773
7.6125 0.148 1480 7.4636
7.4893 0.149 1490 7.4668
7.6807 0.15 1500 7.4954
7.5776 0.151 1510 7.4751
7.3967 0.152 1520 7.4331
7.5283 0.153 1530 7.4692
7.8052 0.154 1540 7.4290
7.5834 0.155 1550 7.4616
7.7283 0.156 1560 7.4860
7.6791 0.157 1570 7.4585
7.6158 0.158 1580 7.4375
7.2222 0.159 1590 7.4374
7.4985 0.16 1600 7.4558
7.1561 0.161 1610 7.4761
7.5376 0.162 1620 7.4723
7.6661 0.163 1630 7.4035
7.4487 0.164 1640 7.4427
7.3298 0.165 1650 7.4271
7.7285 0.166 1660 7.4546
7.4483 0.167 1670 7.4312
7.4408 0.168 1680 7.4606
7.7282 0.169 1690 7.4336
7.3813 0.17 1700 7.4430
7.6532 0.171 1710 7.4525
7.5396 0.172 1720 7.4221
7.4411 0.173 1730 7.4154
7.6833 0.174 1740 7.4107
7.5491 0.175 1750 7.4290
7.7662 0.176 1760 7.4199
7.4044 0.177 1770 7.4154
7.2812 0.178 1780 7.4084
7.5331 0.179 1790 7.4473
7.5071 0.18 1800 7.3930
7.3872 0.181 1810 7.3740
7.5602 0.182 1820 7.4292
7.4895 0.183 1830 7.4198
7.31 0.184 1840 7.4204
7.6234 0.185 1850 7.4274
7.415 0.186 1860 7.4118
7.3878 0.187 1870 7.4142
7.6544 0.188 1880 7.3904
7.7437 0.189 1890 7.3894
7.3989 0.19 1900 7.3890
7.6972 0.191 1910 7.3925
7.2125 0.192 1920 7.4105
7.1969 0.193 1930 7.4042
7.3622 0.194 1940 7.4185
7.693 0.195 1950 7.3802
7.5049 0.196 1960 7.3813
7.0685 0.197 1970 7.3970
7.39 0.198 1980 7.3837
7.4584 0.199 1990 7.3862
7.6313 0.2 2000 7.3914
7.3997 0.201 2010 7.3487
7.5565 0.202 2020 7.3780
7.5577 0.203 2030 7.3561
7.5122 0.204 2040 7.3778
7.3475 0.205 2050 7.3351
7.412 0.206 2060 7.3532
7.2934 0.207 2070 7.3822
7.3301 0.208 2080 7.3666
7.4315 0.209 2090 7.3707
7.7669 0.21 2100 7.3648
7.6001 0.211 2110 7.3520
7.0449 0.212 2120 7.3441
7.4397 0.213 2130 7.3558
7.4177 0.214 2140 7.3436
7.6794 0.215 2150 7.3536
7.3904 0.216 2160 7.3351
7.4649 0.217 2170 7.3132
7.0639 0.218 2180 7.3468
7.3941 0.219 2190 7.3219
7.3677 0.22 2200 7.3388
7.5333 0.221 2210 7.3258
7.4513 0.222 2220 7.3193
7.2358 0.223 2230 7.3134
7.3681 0.224 2240 7.3509
7.5112 0.225 2250 7.3377
7.4465 0.226 2260 7.3180
7.2742 0.227 2270 7.3591
7.248 0.228 2280 7.3304
7.3309 0.229 2290 7.2906
7.2835 0.23 2300 7.3381
7.4633 0.231 2310 7.3111
7.1982 0.232 2320 7.3266
7.6039 0.233 2330 7.3058
7.4073 0.234 2340 7.2858
7.1252 0.235 2350 7.3229
7.2292 0.236 2360 7.3053
7.2017 0.237 2370 7.3098
7.3403 0.238 2380 7.2783
7.1068 0.239 2390 7.2664
7.0333 0.24 2400 7.2739
7.3126 0.241 2410 7.2890
7.3593 0.242 2420 7.2576
7.1044 0.243 2430 7.2867
7.2255 0.244 2440 7.2751
7.2589 0.245 2450 7.2672
7.1443 0.246 2460 7.2453
7.1106 0.247 2470 7.3028
7.2194 0.248 2480 7.2538
7.3261 0.249 2490 7.2444
7.1151 0.25 2500 7.2434
7.1233 0.251 2510 7.2444
7.2017 0.252 2520 7.2277
7.3119 0.253 2530 7.2234
7.1351 0.254 2540 7.2609
6.9466 0.255 2550 7.2526
7.0605 0.256 2560 7.2365
7.2975 0.257 2570 7.1872
7.1271 0.258 2580 7.2360
7.1416 0.259 2590 7.2138
7.0027 0.26 2600 7.2107
7.3571 0.261 2610 7.2442
7.4913 0.262 2620 7.2466
7.0479 0.263 2630 7.2182
7.2408 0.264 2640 7.1999
7.0558 0.265 2650 7.2130
7.059 0.266 2660 7.2023
6.9034 0.267 2670 7.2047
7.1502 0.268 2680 7.2319
7.0909 0.269 2690 7.2057
6.9092 0.27 2700 7.2245
7.1582 0.271 2710 7.2067
7.1501 0.272 2720 7.1925
7.3803 0.273 2730 7.1920
7.0324 0.274 2740 7.2052
7.1564 0.275 2750 7.1872
6.8679 0.276 2760 7.1801
7.2205 0.277 2770 7.1791
6.5563 0.278 2780 7.1739
7.1523 0.279 2790 7.1443
7.0036 0.28 2800 7.1952
7.1959 0.281 2810 7.1859
7.1017 0.282 2820 7.1973
7.1912 0.283 2830 7.1364
7.2652 0.284 2840 7.1460
7.0246 0.285 2850 7.1496
6.7301 0.286 2860 7.1165
6.9913 0.287 2870 7.1515
7.0614 0.288 2880 7.1297
6.9976 0.289 2890 7.1502
7.2083 0.29 2900 7.1473
7.0215 0.291 2910 7.1703
6.9291 0.292 2920 7.1625
7.0786 0.293 2930 7.1223
7.231 0.294 2940 7.1365
6.7236 0.295 2950 7.1230
7.099 0.296 2960 7.1161
7.1999 0.297 2970 7.1528
6.8423 0.298 2980 7.1498
7.0126 0.299 2990 7.1175
6.7405 0.3 3000 7.1065
6.9625 0.301 3010 7.1274
6.7771 0.302 3020 7.1201
7.0296 0.303 3030 7.0997
6.9656 0.304 3040 7.1281
7.0897 0.305 3050 7.1500
7.1378 0.306 3060 7.0717
6.8894 0.307 3070 7.1097
7.0826 0.308 3080 7.0986
6.741 0.309 3090 7.0848
6.7094 0.31 3100 7.0835
6.819 0.311 3110 7.1146
7.1074 0.312 3120 7.0770
7.1401 0.313 3130 7.0798
6.6294 0.314 3140 7.1002
7.1775 0.315 3150 7.0458
7.0666 0.316 3160 7.0722
6.9717 0.317 3170 7.0497
6.788 0.318 3180 7.0732
6.6694 0.319 3190 7.0531
6.8921 0.32 3200 7.0869
6.9714 0.321 3210 7.0291
6.7787 0.322 3220 7.0897
6.874 0.323 3230 7.0680
6.8782 0.324 3240 7.0931
7.0355 0.325 3250 7.0691
6.6516 0.326 3260 7.0353
6.9629 0.327 3270 7.0548
7.0852 0.328 3280 7.0085
7.0322 0.329 3290 7.0377
6.7027 0.33 3300 7.0388
6.9557 0.331 3310 7.0367
6.9216 0.332 3320 7.0302
7.1981 0.333 3330 7.0218
6.7753 0.334 3340 7.0254
6.6969 0.335 3350 7.0234
6.971 0.336 3360 7.0268
6.8622 0.337 3370 7.0414
6.811 0.338 3380 7.0454
6.9242 0.339 3390 6.9996
6.8626 0.34 3400 6.9979
6.7542 0.341 3410 7.0077
7.1513 0.342 3420 7.0145
6.947 0.343 3430 7.0517
6.803 0.344 3440 6.9955
6.7884 0.345 3450 7.0055
7.0018 0.346 3460 7.0156
6.8531 0.347 3470 6.9680
6.8673 0.348 3480 7.0051
6.8755 0.349 3490 6.9947
7.0871 0.35 3500 6.9881
7.03 0.351 3510 6.9770
6.9457 0.352 3520 7.0005
6.5092 0.353 3530 6.9656
6.9303 0.354 3540 6.9949
6.677 0.355 3550 6.9722
6.6224 0.356 3560 6.9965
7.0308 0.357 3570 6.9702
6.8805 0.358 3580 6.9616
6.5842 0.359 3590 6.9814
6.7305 0.36 3600 6.9897
6.6668 0.361 3610 6.9681
6.7719 0.362 3620 6.9603
6.7692 0.363 3630 6.9514
6.814 0.364 3640 6.9316
6.8503 0.365 3650 6.9549
6.5591 0.366 3660 6.9324
6.7495 0.367 3670 6.9453
6.848 0.368 3680 6.9242
6.974 0.369 3690 6.9285
6.4794 0.37 3700 6.9474
6.6943 0.371 3710 6.9217
6.9663 0.372 3720 6.9320
6.6084 0.373 3730 6.9199
6.9094 0.374 3740 6.9508
6.5826 0.375 3750 6.9329
6.64 0.376 3760 6.9281
6.5511 0.377 3770 6.9117
6.9234 0.378 3780 6.9525
6.5332 0.379 3790 6.9178
6.5591 0.38 3800 6.8913
6.841 0.381 3810 6.9262
6.5806 0.382 3820 6.8962
6.447 0.383 3830 6.9004
6.7942 0.384 3840 6.9030
6.6851 0.385 3850 6.9269
6.5883 0.386 3860 6.9115
6.8588 0.387 3870 6.8956
6.8614 0.388 3880 6.8866
6.8001 0.389 3890 6.8999
6.5746 0.39 3900 6.9014
6.6247 0.391 3910 6.8866
6.1422 0.392 3920 6.9351
6.7211 0.393 3930 6.8727
6.601 0.394 3940 6.8862
7.0574 0.395 3950 6.8733
6.6875 0.396 3960 6.9217
6.6279 0.397 3970 6.8672
6.5105 0.398 3980 6.9008
6.7921 0.399 3990 6.8818
6.3581 0.4 4000 6.8805
6.6342 0.401 4010 6.8706
6.3855 0.402 4020 6.8934
6.4562 0.403 4030 6.8719
6.3999 0.404 4040 6.9205
6.5494 0.405 4050 6.8435
6.4438 0.406 4060 6.8579
6.5268 0.407 4070 6.8731
6.8204 0.408 4080 6.8445
6.6391 0.409 4090 6.8325
6.8333 0.41 4100 6.8310
6.7776 0.411 4110 6.8685
6.6876 0.412 4120 6.8661
6.6659 0.413 4130 6.8695
6.5316 0.414 4140 6.8616
6.7156 0.415 4150 6.8339
6.889 0.416 4160 6.9000
6.5882 0.417 4170 6.8368
6.7253 0.418 4180 6.8506
6.3431 0.419 4190 6.8993
6.9911 0.42 4200 6.8795
6.596 0.421 4210 6.8326
6.5461 0.422 4220 6.8634
6.6031 0.423 4230 6.8529
6.8492 0.424 4240 6.8142
6.3502 0.425 4250 6.8278
6.1662 0.426 4260 6.8478
6.6488 0.427 4270 6.8318
6.6603 0.428 4280 6.8391
6.6868 0.429 4290 6.8282
6.7484 0.43 4300 6.8345
6.6902 0.431 4310 6.7963
6.7096 0.432 4320 6.8266
6.4327 0.433 4330 6.8306
6.4964 0.434 4340 6.8072
6.4143 0.435 4350 6.7842
6.3455 0.436 4360 6.7772
6.1908 0.437 4370 6.8191
6.282 0.438 4380 6.8038
6.6324 0.439 4390 6.7859
6.5153 0.44 4400 6.7751
6.5381 0.441 4410 6.8090
6.574 0.442 4420 6.7698
6.2106 0.443 4430 6.7646
6.7623 0.444 4440 6.7697
6.3966 0.445 4450 6.8005
6.6611 0.446 4460 6.7574
6.5096 0.447 4470 6.7926
6.0819 0.448 4480 6.7778
6.5803 0.449 4490 6.7571
6.2392 0.45 4500 6.7605
6.4955 0.451 4510 6.7729
6.6035 0.452 4520 6.7653
6.246 0.453 4530 6.7628
6.4857 0.454 4540 6.7549
6.722 0.455 4550 6.7270
6.2988 0.456 4560 6.7226
6.5153 0.457 4570 6.7333
6.2837 0.458 4580 6.7708
6.5796 0.459 4590 6.7358
6.2638 0.46 4600 6.7666
6.1523 0.461 4610 6.7370
6.4837 0.462 4620 6.7493
6.445 0.463 4630 6.7357
6.3575 0.464 4640 6.7453
6.4376 0.465 4650 6.7841
6.3799 0.466 4660 6.7367
6.4111 0.467 4670 6.7044
6.2899 0.468 4680 6.7553
6.4641 0.469 4690 6.7094
6.3559 0.47 4700 6.7302
6.2232 0.471 4710 6.7245
6.3231 0.472 4720 6.7323
6.7453 0.473 4730 6.7369
6.3101 0.474 4740 6.7323
6.3967 0.475 4750 6.7307
6.2528 0.476 4760 6.7421
6.3045 0.477 4770 6.7426
6.0914 0.478 4780 6.7399
6.2238 0.479 4790 6.7295
6.4785 0.48 4800 6.7384
6.1362 0.481 4810 6.6928
6.1418 0.482 4820 6.7248
6.3808 0.483 4830 6.7407
6.1625 0.484 4840 6.6976
6.6465 0.485 4850 6.7349
6.1045 0.486 4860 6.7338
6.4278 0.487 4870 6.7007
6.3234 0.488 4880 6.7089
6.1495 0.489 4890 6.7042
6.2501 0.49 4900 6.6693
6.2616 0.491 4910 6.6948
6.4488 0.492 4920 6.7030
6.4287 0.493 4930 6.6950
6.4889 0.494 4940 6.7201
6.0918 0.495 4950 6.6938
6.1205 0.496 4960 6.6986
6.317 0.497 4970 6.6727
6.2713 0.498 4980 6.6976
6.11 0.499 4990 6.6758
6.235 0.5 5000 6.6724
6.3996 0.501 5010 6.6798
6.3699 0.502 5020 6.6742
6.1624 0.503 5030 6.6830
6.2088 0.504 5040 6.6419
6.3496 0.505 5050 6.6581
6.4158 0.506 5060 6.6618
6.2157 0.507 5070 6.6742
6.1371 0.508 5080 6.6751
6.4713 0.509 5090 6.6452
6.2813 0.51 5100 6.6667
6.2622 0.511 5110 6.6461
6.2141 0.512 5120 6.6409
6.249 0.513 5130 6.6116
6.1079 0.514 5140 6.6085
6.2329 0.515 5150 6.6520
6.2326 0.516 5160 6.6470
6.1445 0.517 5170 6.6167
6.4773 0.518 5180 6.6583
6.3782 0.519 5190 6.6269
5.9545 0.52 5200 6.6428
6.1238 0.521 5210 6.5993
5.9592 0.522 5220 6.5813
6.5282 0.523 5230 6.6343
5.9429 0.524 5240 6.6143
6.0343 0.525 5250 6.6292
6.2044 0.526 5260 6.6080
6.2448 0.527 5270 6.6477
6.3944 0.528 5280 6.6043
6.0342 0.529 5290 6.6013
5.9049 0.53 5300 6.6107
6.018 0.531 5310 6.6163
6.1528 0.532 5320 6.5823
6.1172 0.533 5330 6.6173
6.1223 0.534 5340 6.5931
6.0701 0.535 5350 6.5945
6.2737 0.536 5360 6.5844
6.2929 0.537 5370 6.5679
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5.3617 0.998 9980 6.2671
5.5834 0.999 9990 6.2722
5.7009 1.0 10000 6.2640

Framework versions

  • Transformers 4.44.0.dev0
  • Pytorch 2.1.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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