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--- |
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library_name: transformers |
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tags: |
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- trl |
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- cpo |
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- alignment-handbook |
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- generated_from_trainer |
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model-index: |
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- name: OpenELM-1_1B-SimPO |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# OpenELM-1_1B-SimPO |
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This model was trained from scratch on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Logits/chosen: -0.5781 |
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- Logits/rejected: 1.2422 |
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- Logps/chosen: -113.0 |
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- Logps/rejected: -171.0 |
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- Loss: 0.8496 |
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- Nll Loss: 0.0 |
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- Rewards/accuracies: 0.6680 |
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- Rewards/chosen: -1.1328 |
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- Rewards/margins: 0.5742 |
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- Rewards/rejected: -1.7031 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Logits/chosen | Logits/rejected | Logps/chosen | Logps/rejected | Validation Loss | Nll Loss | Rewards/accuracies | Rewards/chosen | Rewards/margins | Rewards/rejected | |
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|:-------------:|:------:|:----:|:-------------:|:---------------:|:------------:|:--------------:|:---------------:|:--------:|:------------------:|:--------------:|:---------------:|:----------------:| |
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| 0.9346 | 0.1047 | 100 | -8.5625 | -7.9688 | -33.25 | -41.75 | 0.9349 | 0.0 | 0.6133 | -0.3320 | 0.0864 | -0.4180 | |
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| 0.9139 | 0.2093 | 200 | -3.4531 | -2.4375 | -48.5 | -63.5 | 0.9069 | 0.0 | 0.6270 | -0.4844 | 0.1504 | -0.6367 | |
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| 0.907 | 0.3140 | 300 | -5.1875 | -4.0 | -69.5 | -83.5 | 0.9099 | 0.0 | 0.6055 | -0.6914 | 0.1416 | -0.8359 | |
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| 0.901 | 0.4186 | 400 | -1.7422 | 0.0164 | -84.0 | -101.0 | 0.8957 | 0.0 | 0.6328 | -0.8359 | 0.1748 | -1.0156 | |
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| 0.8752 | 0.5233 | 500 | -0.5625 | 0.8555 | -72.5 | -95.5 | 0.8768 | 0.0 | 0.6582 | -0.7266 | 0.2324 | -0.9570 | |
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| 0.8808 | 0.6279 | 600 | 2.1562 | 3.2344 | -86.0 | -109.5 | 0.8742 | 0.0 | 0.6445 | -0.8633 | 0.2334 | -1.0938 | |
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| 0.8277 | 0.7326 | 700 | -0.7930 | 0.3496 | -52.0 | -77.5 | 0.8679 | 0.0 | 0.6445 | -0.5195 | 0.2520 | -0.7734 | |
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| 0.8341 | 0.8373 | 800 | 0.2188 | 1.3047 | -80.5 | -108.5 | 0.8503 | 0.0 | 0.6602 | -0.8047 | 0.2773 | -1.0859 | |
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| 0.8333 | 0.9419 | 900 | 0.6406 | 1.8438 | -90.0 | -121.5 | 0.8454 | 0.0 | 0.6660 | -0.8984 | 0.3184 | -1.2188 | |
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| 0.8071 | 1.0466 | 1000 | 0.1504 | 1.3516 | -100.0 | -133.0 | 0.8441 | 0.0 | 0.6699 | -1.0 | 0.3340 | -1.3359 | |
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| 0.7845 | 1.1512 | 1100 | -1.5078 | 0.3301 | -84.5 | -122.5 | 0.8307 | 0.0 | 0.6660 | -0.8477 | 0.3809 | -1.2266 | |
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| 0.7483 | 1.2559 | 1200 | -0.4160 | 0.9805 | -94.5 | -133.0 | 0.8353 | 0.0 | 0.6758 | -0.9453 | 0.3809 | -1.3281 | |
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| 0.7802 | 1.3605 | 1300 | -1.5859 | 0.3418 | -62.0 | -100.5 | 0.8363 | 0.0 | 0.7051 | -0.6211 | 0.3828 | -1.0 | |
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| 0.7499 | 1.4652 | 1400 | -0.1719 | 1.4531 | -97.0 | -141.0 | 0.8228 | 0.0 | 0.7012 | -0.9727 | 0.4414 | -1.4141 | |
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| 0.6966 | 1.5699 | 1500 | -0.3301 | 1.5 | -106.0 | -152.0 | 0.8231 | 0.0 | 0.6836 | -1.0625 | 0.4609 | -1.5234 | |
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| 0.6921 | 1.6745 | 1600 | 0.6133 | 2.25 | -107.0 | -155.0 | 0.8222 | 0.0 | 0.6875 | -1.0703 | 0.4766 | -1.5469 | |
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| 0.7162 | 1.7792 | 1700 | 0.6992 | 2.4688 | -103.0 | -154.0 | 0.8106 | 0.0 | 0.6953 | -1.0312 | 0.5078 | -1.5391 | |
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| 0.714 | 1.8838 | 1800 | 0.0579 | 2.1875 | -109.5 | -162.0 | 0.8183 | 0.0 | 0.6855 | -1.0938 | 0.5312 | -1.625 | |
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| 0.7068 | 1.9885 | 1900 | 0.3184 | 1.9922 | -97.5 | -151.0 | 0.8164 | 0.0 | 0.7031 | -0.9727 | 0.5352 | -1.5078 | |
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| 0.4781 | 2.0931 | 2000 | 0.0977 | 1.7344 | -119.0 | -171.0 | 0.8475 | 0.0 | 0.6797 | -1.1875 | 0.5273 | -1.7109 | |
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| 0.4964 | 2.1978 | 2100 | -0.9258 | 0.9219 | -100.0 | -155.0 | 0.8455 | 0.0 | 0.6875 | -1.0 | 0.5547 | -1.5547 | |
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| 0.4723 | 2.3025 | 2200 | -0.4648 | 1.2969 | -110.0 | -166.0 | 0.8475 | 0.0 | 0.6934 | -1.1016 | 0.5586 | -1.6562 | |
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| 0.5051 | 2.4071 | 2300 | -0.2891 | 1.4141 | -113.0 | -170.0 | 0.8480 | 0.0 | 0.6895 | -1.1328 | 0.5664 | -1.6953 | |
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| 0.4647 | 2.5118 | 2400 | -0.3496 | 1.4531 | -114.0 | -171.0 | 0.8463 | 0.0 | 0.6758 | -1.1406 | 0.5742 | -1.7188 | |
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| 0.4442 | 2.6164 | 2500 | -0.1436 | 1.5859 | -123.5 | -180.0 | 0.8527 | 0.0 | 0.6680 | -1.2344 | 0.5664 | -1.7969 | |
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| 0.4349 | 2.7211 | 2600 | -0.5898 | 1.2422 | -112.0 | -169.0 | 0.8505 | 0.0 | 0.6699 | -1.1172 | 0.5742 | -1.6953 | |
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| 0.4514 | 2.8257 | 2700 | -0.6406 | 1.1953 | -112.0 | -169.0 | 0.8493 | 0.0 | 0.6738 | -1.1172 | 0.5781 | -1.6953 | |
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| 0.459 | 2.9304 | 2800 | -0.5781 | 1.2422 | -113.0 | -171.0 | 0.8496 | 0.0 | 0.6680 | -1.1328 | 0.5742 | -1.7031 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.3.0 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
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