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--- |
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library_name: peft |
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tags: |
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- trl |
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- dpo |
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- alignment-handbook |
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- generated_from_trainer |
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base_model: NbAiLab/nb-gpt-j-6B-v2 |
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model-index: |
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- name: aftonposten-6b-align-scan |
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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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# aftonposten-6b-align-scan |
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This model is a fine-tuned version of [NbAiLab/nb-gpt-j-6B-v2](https://huggingface.co./NbAiLab/nb-gpt-j-6B-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6820 |
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- Rewards/chosen: -0.2813 |
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- Rewards/rejected: -0.4578 |
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- Rewards/accuracies: 0.5743 |
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- Rewards/margins: 0.1765 |
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- Logps/rejected: -38.0889 |
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- Logps/chosen: -34.3862 |
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- Logits/rejected: -2.1265 |
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- Logits/chosen: -2.1312 |
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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-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Logits/chosen | Logits/rejected | Logps/chosen | Logps/rejected | Validation Loss | Rewards/accuracies | Rewards/chosen | Rewards/margins | Rewards/rejected | |
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|:-------------:|:-----:|:----:|:-------------:|:---------------:|:------------:|:--------------:|:---------------:|:------------------:|:--------------:|:---------------:|:----------------:| |
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| 0.6538 | 0.26 | 100 | -2.2339 | -2.2290 | -34.0302 | -37.5273 | 0.6955 | 0.5108 | 0.0035 | 0.0120 | -0.0085 | |
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| 0.6015 | 0.52 | 200 | -2.2322 | -2.2274 | -34.0607 | -37.5657 | 0.6956 | 0.5249 | -0.0209 | 0.0183 | -0.0393 | |
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| 0.5385 | 0.78 | 300 | -2.2294 | -2.2246 | -34.0909 | -37.5973 | 0.6957 | 0.5399 | -0.0451 | 0.0194 | -0.0645 | |
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| 0.4017 | 1.04 | 400 | 0.6775 | -0.0241 | -0.0945 | 0.6005 | 0.0703 | -37.6347 | -34.0647 | -2.2052 | -2.2100 | |
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| 0.3216 | 1.3 | 500 | 0.6820 | -0.1165 | -0.2131 | 0.5768 | 0.0966 | -37.7830 | -34.1802 | -2.1659 | -2.1707 | |
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| 0.336 | 1.56 | 600 | 0.6618 | -0.1839 | -0.3388 | 0.6242 | 0.1549 | -37.9401 | -34.2644 | -2.1554 | -2.1602 | |
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| 0.3559 | 1.82 | 700 | 0.6947 | -0.2571 | -0.3713 | 0.5341 | 0.1141 | -37.9807 | -34.3560 | -2.1535 | -2.1583 | |
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| 0.1978 | 2.08 | 800 | 0.6838 | -0.2324 | -0.3669 | 0.5835 | 0.1345 | -37.9753 | -34.3250 | -2.1501 | -2.1549 | |
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| 0.1619 | 2.34 | 900 | 0.6788 | -0.2463 | -0.4156 | 0.5860 | 0.1693 | -38.0361 | -34.3424 | -2.1384 | -2.1431 | |
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| 0.209 | 2.6 | 1000 | 0.6777 | -0.2767 | -0.4535 | 0.5918 | 0.1767 | -38.0835 | -34.3805 | -2.1309 | -2.1357 | |
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| 0.2513 | 2.86 | 1100 | 0.6897 | -0.2986 | -0.4591 | 0.5831 | 0.1605 | -38.0905 | -34.4077 | -2.1270 | -2.1317 | |
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| 0.1713 | 3.12 | 1200 | 0.6780 | -0.2775 | -0.4614 | 0.5947 | 0.1839 | -38.0934 | -34.3814 | -2.1270 | -2.1317 | |
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| 0.1199 | 3.38 | 1300 | 0.6740 | -0.2726 | -0.4645 | 0.5980 | 0.1919 | -38.0972 | -34.3753 | -2.1269 | -2.1317 | |
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| 0.1578 | 3.64 | 1400 | 0.6839 | -0.2867 | -0.4600 | 0.5860 | 0.1734 | -38.0917 | -34.3929 | -2.1266 | -2.1314 | |
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| 0.1614 | 3.9 | 1500 | 0.6820 | -0.2813 | -0.4578 | 0.5743 | 0.1765 | -38.0889 | -34.3862 | -2.1265 | -2.1312 | |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.37.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.1 |