generated_from_trainer

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qwen_w1w2attnproj_tiny_textbooks_r16

This model is a fine-tuned version of Qwen/Qwen-14B on an unknown 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
2.3844 0.01 63 2.4861
2.409 0.02 126 2.4854
2.4198 0.03 189 2.4866
2.421 0.04 252 2.4875
2.4453 0.03 315 2.4575
2.441 0.03 378 2.4554
2.4694 0.04 441 2.4557
2.4546 0.04 504 2.4526
2.4488 0.05 567 2.4523
2.4392 0.05 630 2.4511
2.4684 0.06 693 2.4512
2.4409 0.06 756 2.4497
2.454 0.07 819 2.4497
2.4985 0.07 882 2.4494
2.4399 0.08 945 2.4489
2.432 0.08 1008 2.4487
2.4786 0.09 1071 2.4479
2.4552 0.09 1134 2.4491
2.4235 0.1 1197 2.4479
2.453 0.1 1260 2.4470
2.4243 0.11 1323 2.4494
2.4527 0.11 1386 2.4466
2.4585 0.12 1449 2.4463
2.4444 0.12 1512 2.4456
2.4511 0.13 1575 2.4456
2.4472 0.13 1638 2.4443
2.383 0.14 1701 2.4457
2.4525 0.14 1764 2.4454
2.4131 0.15 1827 2.4464
2.4107 0.15 1890 2.4431
2.4582 0.16 1953 2.4432
2.4864 0.16 2016 2.4438
2.3838 0.17 2079 2.4437
2.4234 0.17 2142 2.4442
2.4264 0.18 2205 2.4432
2.4545 0.18 2268 2.4445
2.4139 0.19 2331 2.4428
2.4687 0.19 2394 2.4437
2.4442 0.2 2457 2.4442
2.434 0.2 2520 2.4430
2.4308 0.21 2583 2.4426
2.471 0.21 2646 2.4431
2.4646 0.22 2709 2.4423
2.4552 0.22 2772 2.4427
2.4185 0.23 2835 2.4423
2.4542 0.23 2898 2.4418
2.4011 0.24 2961 2.4413
2.4482 0.24 3024 2.4413
2.4525 0.25 3087 2.4413
2.408 0.25 3150 2.4434
2.4191 0.26 3213 2.4418
2.4033 0.26 3276 2.4429
2.3819 0.27 3339 2.4407
2.4762 0.27 3402 2.4420
2.4616 0.28 3465 2.4409
2.4629 0.28 3528 2.4409
2.4333 0.29 3591 2.4421
2.385 0.29 3654 2.4410
2.44 0.3 3717 2.4416
2.4133 0.3 3780 2.4417
2.4344 0.31 3843 2.4417
2.452 0.31 3906 2.4407
2.438 0.32 3969 2.4419
2.4558 0.32 4032 2.4429
2.4647 0.33 4095 2.4410
2.4441 0.33 4158 2.4411
2.4563 0.34 4221 2.4429
2.4394 0.34 4284 2.4418
2.4319 0.35 4347 2.4407
2.3877 0.35 4410 2.4420
2.4482 0.36 4473 2.4417
2.4157 0.36 4536 2.4406
2.4566 0.37 4599 2.4416
2.4521 0.37 4662 2.4409
2.4647 0.38 4725 2.4411
2.4338 0.38 4788 2.4405
2.5055 0.39 4851 2.4423
2.4696 0.39 4914 2.4417
2.4031 0.4 4977 2.4411
2.4554 0.4 5040 2.4414
2.4009 0.41 5103 2.4405
2.4632 0.41 5166 2.4408
2.4518 0.42 5229 2.4402
2.5038 0.42 5292 2.4403
2.4748 0.43 5355 2.4401
2.4026 0.43 5418 2.4407
2.4788 0.44 5481 2.4401
2.4105 0.44 5544 2.4401
2.4177 0.45 5607 2.4407
2.4788 0.45 5670 2.4400
2.4809 0.46 5733 2.4401
2.4344 0.46 5796 2.4399
2.4275 0.47 5859 2.4392
2.435 0.47 5922 2.4392
2.3988 0.48 5985 2.4388
2.4626 0.48 6048 2.4386
2.4623 0.49 6111 2.4391
2.4529 0.49 6174 2.4389
2.4769 0.5 6237 2.4378
2.4646 0.5 6300 2.4389
2.4157 0.51 6363 2.4389
2.4269 0.51 6426 2.4387
2.4655 0.52 6489 2.4383
2.4145 0.52 6552 2.4381
2.4205 0.53 6615 2.4377
2.4176 0.53 6678 2.4389
2.4225 0.54 6741 2.4381
2.47 0.54 6804 2.4386
2.4348 0.55 6867 2.4382
2.4374 0.55 6930 2.4388
2.399 0.56 6993 2.4388
2.3887 0.56 7056 2.4383
2.4227 0.57 7119 2.4376
2.444 0.57 7182 2.4375
2.4646 0.58 7245 2.4370
2.4007 0.58 7308 2.4373
2.4717 0.59 7371 2.4377
2.4804 0.59 7434 2.4373
2.4753 0.6 7497 2.4375
2.4125 0.6 7560 2.4378
2.45 0.61 7623 2.4377
2.4177 0.61 7686 2.4372
2.4355 0.62 7749 2.4376
2.4414 0.62 7812 2.4380
2.4072 0.63 7875 2.4386
2.4581 0.64 7938 2.4378
2.4161 0.64 8001 2.4373
2.4596 0.65 8064 2.4375
2.3987 0.65 8127 2.4373
2.4152 0.66 8190 2.4372
2.4381 0.66 8253 2.4381
2.4604 0.67 8316 2.4378
2.4212 0.67 8379 2.4376
2.421 0.68 8442 2.4368
2.4346 0.68 8505 2.4370
2.4284 0.69 8568 2.4371
2.4525 0.69 8631 2.4371
2.4434 0.7 8694 2.4380
2.3519 0.7 8757 2.4374
2.4297 0.71 8820 2.4376
2.3884 0.71 8883 2.4378
2.4528 0.72 8946 2.4374
2.4313 0.72 9009 2.4372
2.4309 0.73 9072 2.4374
2.4178 0.73 9135 2.4373
2.4132 0.74 9198 2.4371
2.4062 0.74 9261 2.4372
2.4276 0.75 9324 2.4373
2.4452 0.75 9387 2.4370
2.4256 0.76 9450 2.4374
2.4125 0.76 9513 2.4377
2.4055 0.77 9576 2.4377
2.4655 0.77 9639 2.4379
2.4328 0.78 9702 2.4376
2.4632 0.78 9765 2.4372
2.4374 0.79 9828 2.4375
2.4247 0.79 9891 2.4378
2.417 0.8 9954 2.4375
2.4711 0.8 10017 2.4373
2.4041 0.81 10080 2.4376
2.4048 0.81 10143 2.4376
2.42 0.82 10206 2.4373
2.3942 0.82 10269 2.4376
2.4307 0.83 10332 2.4379
2.435 0.83 10395 2.4377
2.4647 0.84 10458 2.4377
2.4622 0.84 10521 2.4373
2.4204 0.85 10584 2.4373
2.4195 0.85 10647 2.4373
2.3934 0.86 10710 2.4373
2.4511 0.86 10773 2.4374
2.4431 0.87 10836 2.4372
2.4013 0.87 10899 2.4370
2.444 0.88 10962 2.4372
2.4355 0.88 11025 2.4371
2.4502 0.89 11088 2.4372
2.4418 0.89 11151 2.4369
2.4307 0.9 11214 2.4371
2.4342 0.9 11277 2.4372
2.4753 0.91 11340 2.4369
2.4044 0.91 11403 2.4372
2.4289 0.92 11466 2.4371
2.468 0.92 11529 2.4371
2.4546 0.93 11592 2.4372
2.4484 0.93 11655 2.4370
2.4566 0.94 11718 2.4372
2.4079 0.94 11781 2.4370
2.4264 0.95 11844 2.4371
2.4729 0.95 11907 2.4371
2.3787 0.96 11970 2.4372
2.3911 0.96 12033 2.4370
2.4216 0.97 12096 2.4371
2.4465 0.97 12159 2.4371
2.3939 0.98 12222 2.4370
2.419 0.98 12285 2.4371
2.4358 0.99 12348 2.4370
2.47 0.99 12411 2.4371
2.423 1.0 12474 2.4371

Framework versions