UTI_L3_1000steps_1e6rate_SFT
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9883
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-06
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.5921 | 0.3333 | 25 | 2.4381 |
1.8551 | 0.6667 | 50 | 1.5631 |
1.2769 | 1.0 | 75 | 1.1985 |
1.1027 | 1.3333 | 100 | 1.1215 |
1.0509 | 1.6667 | 125 | 1.1006 |
0.9917 | 2.0 | 150 | 1.0852 |
0.9325 | 2.3333 | 175 | 1.0986 |
0.9627 | 2.6667 | 200 | 1.0883 |
0.9724 | 3.0 | 225 | 1.0865 |
0.7795 | 3.3333 | 250 | 1.1249 |
0.7455 | 3.6667 | 275 | 1.1105 |
0.7684 | 4.0 | 300 | 1.1214 |
0.6135 | 4.3333 | 325 | 1.1762 |
0.5911 | 4.6667 | 350 | 1.2296 |
0.6302 | 5.0 | 375 | 1.2176 |
0.4435 | 5.3333 | 400 | 1.3544 |
0.4558 | 5.6667 | 425 | 1.3765 |
0.4538 | 6.0 | 450 | 1.3526 |
0.2966 | 6.3333 | 475 | 1.5173 |
0.2836 | 6.6667 | 500 | 1.5129 |
0.3147 | 7.0 | 525 | 1.4603 |
0.2252 | 7.3333 | 550 | 1.6120 |
0.2143 | 7.6667 | 575 | 1.6538 |
0.1922 | 8.0 | 600 | 1.6461 |
0.1429 | 8.3333 | 625 | 1.7717 |
0.1491 | 8.6667 | 650 | 1.8011 |
0.1707 | 9.0 | 675 | 1.8125 |
0.1189 | 9.3333 | 700 | 1.8928 |
0.1274 | 9.6667 | 725 | 1.9053 |
0.1289 | 10.0 | 750 | 1.9127 |
0.111 | 10.3333 | 775 | 1.9630 |
0.1082 | 10.6667 | 800 | 1.9689 |
0.1139 | 11.0 | 825 | 1.9652 |
0.1062 | 11.3333 | 850 | 1.9791 |
0.1071 | 11.6667 | 875 | 1.9866 |
0.1053 | 12.0 | 900 | 1.9890 |
0.1087 | 12.3333 | 925 | 1.9848 |
0.1079 | 12.6667 | 950 | 1.9866 |
0.0994 | 13.0 | 975 | 1.9883 |
0.1007 | 13.3333 | 1000 | 1.9883 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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