generated_from_trainer

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llama7b_rulm_spm_unigram_10_mean_init_tie_rulm_small_1e_12_10_23

This model is a fine-tuned version of llama7b_rulm_spm_unigram_10_mean_init_tie_12_10_23 on the None dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Accuracy
3.5018 0.01 1000 3.5095 0.3768
3.3375 0.02 2000 3.3603 0.3918
3.2694 0.02 3000 3.2995 0.3982
3.236 0.03 4000 3.2626 0.4022
3.2131 0.04 5000 3.2350 0.4048
3.1934 0.05 6000 3.2147 0.4073
3.177 0.06 7000 3.2006 0.4089
3.1653 0.07 8000 3.1890 0.4097
3.1548 0.07 9000 3.1779 0.4109
3.152 0.08 10000 3.1699 0.4119
3.1416 0.09 11000 3.1622 0.4130
3.1387 0.1 12000 3.1561 0.4139
3.1192 0.11 13000 3.1495 0.4143
3.1221 0.12 14000 3.1431 0.4150
3.1136 0.12 15000 3.1391 0.4155
3.106 0.13 16000 3.1348 0.4160
3.1023 0.14 17000 3.1312 0.4165
3.1062 0.15 18000 3.1265 0.4172
3.1007 0.16 19000 3.1230 0.4177
3.0979 0.16 20000 3.1201 0.4178
3.0897 0.17 21000 3.1168 0.4179
3.0863 0.18 22000 3.1128 0.4189
3.0898 0.19 23000 3.1097 0.4191
3.0825 0.2 24000 3.1074 0.4191
3.0808 0.21 25000 3.1037 0.4200
3.0774 0.21 26000 3.1032 0.4197
3.0652 0.22 27000 3.0980 0.4202
3.0693 0.23 28000 3.0968 0.4207
3.0665 0.24 29000 3.0944 0.4209
3.0657 0.25 30000 3.0920 0.4210
3.0608 0.26 31000 3.0911 0.4213
3.0647 0.26 32000 3.0896 0.4213
3.0604 0.27 33000 3.0861 0.4217
3.0577 0.28 34000 3.0845 0.4221
3.0606 0.29 35000 3.0814 0.4220
3.0515 0.3 36000 3.0801 0.4227
3.0527 0.31 37000 3.0772 0.4225
3.0507 0.31 38000 3.0758 0.4228
3.0433 0.32 39000 3.0739 0.4234
3.0546 0.33 40000 3.0717 0.4234
3.0484 0.34 41000 3.0697 0.4236
3.0441 0.35 42000 3.0694 0.4236
3.0292 0.35 43000 3.0662 0.4242
3.0384 0.36 44000 3.0643 0.4244
3.0367 0.37 45000 3.0629 0.4240
3.0337 0.38 46000 3.0622 0.4246
3.0385 0.39 47000 3.0599 0.4245
3.0319 0.4 48000 3.0574 0.4250
3.0255 0.4 49000 3.0573 0.4249
3.021 0.41 50000 3.0557 0.4253
3.0305 0.42 51000 3.0530 0.4254
3.0248 0.43 52000 3.0528 0.4257
3.0269 0.44 53000 3.0495 0.4261
3.0136 0.45 54000 3.0488 0.4259
3.0156 0.45 55000 3.0468 0.4262
3.022 0.46 56000 3.0454 0.4268
3.0193 0.47 57000 3.0442 0.4269
3.0222 0.48 58000 3.0417 0.4270
3.0111 0.49 59000 3.0393 0.4276
3.0148 0.49 60000 3.0384 0.4273
3.0077 0.5 61000 3.0364 0.4276
3.0167 0.51 62000 3.0358 0.4276
3.0049 0.52 63000 3.0343 0.4280
3.016 0.53 64000 3.0322 0.4281
3.0103 0.54 65000 3.0297 0.4285
3.0066 0.54 66000 3.0290 0.4284
2.9958 0.55 67000 3.0281 0.4285
3.0062 0.56 68000 3.0266 0.4288
2.9985 0.57 69000 3.0245 0.4289
3.0031 0.58 70000 3.0224 0.4292
2.9894 0.59 71000 3.0214 0.4295
2.9929 0.59 72000 3.0193 0.4296
2.9904 0.6 73000 3.0176 0.4296
2.9989 0.61 74000 3.0171 0.4301
2.9959 0.62 75000 3.0153 0.4301
2.9847 0.63 76000 3.0142 0.4306
2.9892 0.63 77000 3.0127 0.4308
2.9924 0.64 78000 3.0110 0.4310
2.991 0.65 79000 3.0096 0.4312
2.9824 0.66 80000 3.0080 0.4311
2.9879 0.67 81000 3.0060 0.4315
2.9764 0.68 82000 3.0042 0.4321
2.9827 0.68 83000 3.0030 0.4315
2.9769 0.69 84000 3.0012 0.4324
2.9788 0.7 85000 3.0002 0.4322
2.9734 0.71 86000 2.9987 0.4325
2.9769 0.72 87000 2.9975 0.4328
2.9676 0.73 88000 2.9959 0.4326
2.9677 0.73 89000 2.9943 0.4330
2.9739 0.74 90000 2.9933 0.4330
2.9691 0.75 91000 2.9914 0.4334
2.969 0.76 92000 2.9901 0.4336
2.9602 0.77 93000 2.9889 0.4337
2.965 0.78 94000 2.9872 0.4339
2.9627 0.78 95000 2.9853 0.4341
2.9542 0.79 96000 2.9844 0.4340
2.9552 0.8 97000 2.9822 0.4344
2.9576 0.81 98000 2.9812 0.4347
2.9579 0.82 99000 2.9802 0.4348
2.9508 0.82 100000 2.9784 0.4349
2.9551 0.83 101000 2.9771 0.4353
2.9535 0.84 102000 2.9759 0.4357
2.9479 0.85 103000 2.9743 0.4357
2.9542 0.86 104000 2.9732 0.4359
2.9481 0.87 105000 2.9715 0.4360
2.941 0.87 106000 2.9697 0.4362
2.9435 0.88 107000 2.9684 0.4365
2.9403 0.89 108000 2.9674 0.4368
2.9453 0.9 109000 2.9661 0.4367
2.9396 0.91 110000 2.9644 0.4372
2.9375 0.92 111000 2.9633 0.4372
2.9284 0.92 112000 2.9621 0.4374
2.9418 0.93 113000 2.9606 0.4376
2.934 0.94 114000 2.9594 0.4377
2.9374 0.95 115000 2.9583 0.4380
2.9302 0.96 116000 2.9569 0.4382
2.9273 0.96 117000 2.9560 0.4382
2.9338 0.97 118000 2.9548 0.4384
2.9304 0.98 119000 2.9539 0.4385
2.9361 0.99 120000 2.9531 0.4385
2.927 1.0 121000 2.9526 0.4387

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