DAC-Denoiser-LLaMA-1B-DAC-SE2_1B_1GPU_cont_v2
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.9397
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 490
- training_steps: 24527
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.9257 | 0.0083 | 200 | 8.9732 |
| 6.7348 | 0.0165 | 400 | 6.7776 |
| 6.6337 | 0.0248 | 600 | 6.6992 |
| 6.5831 | 0.0330 | 800 | 6.6427 |
| 6.5035 | 0.0413 | 1000 | 6.5786 |
| 6.3535 | 0.0496 | 1200 | 6.4691 |
| 6.0956 | 0.0578 | 1400 | 6.2538 |
| 5.7696 | 0.0661 | 1600 | 5.9178 |
| 5.4102 | 0.0743 | 1800 | 5.6647 |
| 5.2992 | 0.0826 | 2000 | 5.4859 |
| 4.9603 | 0.0909 | 2200 | 5.1943 |
| 4.8496 | 0.0991 | 2400 | 4.9846 |
| 4.7874 | 0.1074 | 2600 | 4.8687 |
| 4.6003 | 0.1156 | 2800 | 4.7883 |
| 4.5398 | 0.1239 | 3000 | 4.6783 |
| 4.4368 | 0.1321 | 3200 | 4.6156 |
| 4.5215 | 0.1404 | 3400 | 4.5750 |
| 4.5379 | 0.1487 | 3600 | 4.5496 |
| 4.4139 | 0.1569 | 3800 | 4.5098 |
| 4.3908 | 0.1652 | 4000 | 4.4832 |
| 4.3592 | 0.1734 | 4200 | 4.4558 |
| 4.2609 | 0.1817 | 4400 | 4.4330 |
| 4.3614 | 0.1900 | 4600 | 4.4146 |
| 4.2868 | 0.1982 | 4800 | 4.3926 |
| 4.2852 | 0.2065 | 5000 | 4.3767 |
| 4.2367 | 0.2147 | 5200 | 4.3632 |
| 4.2743 | 0.2230 | 5400 | 4.3406 |
| 4.3228 | 0.2313 | 5600 | 4.3304 |
| 4.0882 | 0.2395 | 5800 | 4.3177 |
| 4.2148 | 0.2478 | 6000 | 4.3025 |
| 4.1705 | 0.2560 | 6200 | 4.2915 |
| 4.1606 | 0.2643 | 6400 | 4.2806 |
| 4.1353 | 0.2726 | 6600 | 4.2699 |
| 4.0473 | 0.2808 | 6800 | 4.2593 |
| 4.1826 | 0.2891 | 7000 | 4.2465 |
| 4.0894 | 0.2973 | 7200 | 4.2371 |
| 4.1944 | 0.3056 | 7400 | 4.2275 |
| 4.0872 | 0.3139 | 7600 | 4.2171 |
| 4.0388 | 0.3221 | 7800 | 4.2084 |
| 4.0974 | 0.3304 | 8000 | 4.1970 |
| 4.0866 | 0.3386 | 8200 | 4.1915 |
| 4.0224 | 0.3469 | 8400 | 4.1837 |
| 4.0804 | 0.3552 | 8600 | 4.1749 |
| 4.1001 | 0.3634 | 8800 | 4.1703 |
| 3.9772 | 0.3717 | 9000 | 4.1605 |
| 4.0091 | 0.3799 | 9200 | 4.1510 |
| 3.9754 | 0.3882 | 9400 | 4.1478 |
| 4.0554 | 0.3964 | 9600 | 4.1413 |
| 3.973 | 0.4047 | 9800 | 4.1336 |
| 4.024 | 0.4130 | 10000 | 4.1262 |
| 3.9008 | 0.4212 | 10200 | 4.1203 |
| 3.9837 | 0.4295 | 10400 | 4.1165 |
| 3.9856 | 0.4377 | 10600 | 4.1082 |
| 4.05 | 0.4460 | 10800 | 4.1063 |
| 3.9868 | 0.4543 | 11000 | 4.0997 |
| 3.9677 | 0.4625 | 11200 | 4.0953 |
| 3.9456 | 0.4708 | 11400 | 4.0857 |
| 3.9515 | 0.4790 | 11600 | 4.0807 |
| 3.8984 | 0.4873 | 11800 | 4.0785 |
| 3.9625 | 0.4956 | 12000 | 4.0744 |
| 3.9627 | 0.5038 | 12200 | 4.0683 |
| 3.8908 | 0.5121 | 12400 | 4.0630 |
| 3.8739 | 0.5203 | 12600 | 4.0602 |
| 3.9079 | 0.5286 | 12800 | 4.0548 |
| 3.9565 | 0.5369 | 13000 | 4.0505 |
| 3.9753 | 0.5451 | 13200 | 4.0475 |
| 3.9373 | 0.5534 | 13400 | 4.0415 |
| 3.8967 | 0.5616 | 13600 | 4.0386 |
| 3.8869 | 0.5699 | 13800 | 4.0353 |
| 3.9242 | 0.5782 | 14000 | 4.0333 |
| 3.8214 | 0.5864 | 14200 | 4.0287 |
| 3.8305 | 0.5947 | 14400 | 4.0252 |
| 3.8868 | 0.5953 | 14600 | 4.0214 |
| 3.8755 | 0.6034 | 14800 | 4.0177 |
| 3.8983 | 0.6116 | 15000 | 4.0148 |
| 3.8179 | 0.6197 | 15200 | 4.0113 |
| 3.9368 | 0.6279 | 15400 | 4.0076 |
| 3.8364 | 0.6360 | 15600 | 4.0057 |
| 3.9392 | 0.6442 | 15800 | 4.0010 |
| 3.8992 | 0.6523 | 16000 | 3.9977 |
| 3.8775 | 0.6605 | 16200 | 3.9959 |
| 3.9484 | 0.6686 | 16400 | 3.9936 |
| 3.827 | 0.6768 | 16600 | 3.9890 |
| 3.9737 | 0.6850 | 16800 | 3.9865 |
| 3.8611 | 0.6931 | 17000 | 3.9835 |
| 3.8686 | 0.7013 | 17200 | 3.9811 |
| 3.8596 | 0.7094 | 17400 | 3.9789 |
| 3.7207 | 0.7176 | 17600 | 3.9752 |
| 3.8712 | 0.7257 | 17800 | 3.9737 |
| 3.8364 | 0.7339 | 18000 | 3.9716 |
| 3.9145 | 0.7420 | 18200 | 3.9699 |
| 3.8044 | 0.7502 | 18400 | 3.9677 |
| 3.7504 | 0.7583 | 18600 | 3.9654 |
| 3.8542 | 0.7665 | 18800 | 3.9634 |
| 3.8688 | 0.7747 | 19000 | 3.9614 |
| 3.8832 | 0.7828 | 19200 | 3.9593 |
| 3.834 | 0.7910 | 19400 | 3.9579 |
| 3.8855 | 0.7991 | 19600 | 3.9561 |
| 3.8744 | 0.8073 | 19800 | 3.9557 |
| 3.7769 | 0.8154 | 20000 | 3.9535 |
| 3.8508 | 0.8236 | 20200 | 3.9525 |
| 3.7791 | 0.8317 | 20400 | 3.9510 |
| 3.8041 | 0.8399 | 20600 | 3.9489 |
| 3.7265 | 0.8480 | 20800 | 3.9479 |
| 3.8421 | 0.8562 | 21000 | 3.9470 |
| 3.7523 | 0.8643 | 21200 | 3.9462 |
| 3.8736 | 0.8725 | 21400 | 3.9458 |
| 3.8183 | 0.8807 | 21600 | 3.9449 |
| 3.7868 | 0.8888 | 21800 | 3.9438 |
| 3.7659 | 0.8970 | 22000 | 3.9431 |
| 3.791 | 0.9051 | 22200 | 3.9427 |
| 3.7429 | 0.9133 | 22400 | 3.9416 |
| 3.7534 | 0.9214 | 22600 | 3.9414 |
| 3.7807 | 0.9296 | 22800 | 3.9409 |
| 3.771 | 0.9377 | 23000 | 3.9408 |
| 3.8103 | 0.9459 | 23200 | 3.9404 |
| 3.8326 | 0.9540 | 23400 | 3.9402 |
| 3.8351 | 0.9622 | 23600 | 3.9400 |
| 3.8171 | 0.9704 | 23800 | 3.9399 |
| 3.8372 | 0.9785 | 24000 | 3.9398 |
| 3.8325 | 0.9867 | 24200 | 3.9397 |
| 3.8412 | 0.9948 | 24400 | 3.9397 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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