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--- |
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base_model: rasyosef/phi-2-sft-openhermes-128k-v2-merged |
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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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- generated_from_trainer |
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model-index: |
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- name: phi-2-apo |
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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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# phi-2-apo |
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This model is a fine-tuned version of [rasyosef/phi-2-sft-openhermes-128k-v2-merged](https://huggingface.co/rasyosef/phi-2-sft-openhermes-128k-v2-merged) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3695 |
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- Rewards/chosen: 1.5931 |
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- Rewards/rejected: -3.0842 |
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- Rewards/accuracies: 0.9350 |
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- Rewards/margins: 4.6772 |
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- Logps/rejected: -173.1941 |
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- Logps/chosen: -253.6105 |
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- Logits/rejected: -0.4322 |
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- Logits/chosen: 0.1424 |
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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: 1e-06 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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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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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 0.3669 | 0.2041 | 250 | 0.3828 | 1.5010 | -2.9712 | 0.9450 | 4.4722 | -172.0644 | -254.5310 | -0.4930 | 0.0860 | |
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| 0.3514 | 0.4082 | 500 | 0.3786 | 1.5375 | -2.9788 | 0.9400 | 4.5163 | -172.1404 | -254.1665 | -0.4834 | 0.0968 | |
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| 0.3539 | 0.6122 | 750 | 0.3756 | 1.5549 | -3.0097 | 0.9400 | 4.5647 | -172.4500 | -253.9920 | -0.4690 | 0.1096 | |
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| 0.3562 | 0.8163 | 1000 | 0.3736 | 1.5759 | -3.0081 | 0.9450 | 4.5840 | -172.4332 | -253.7824 | -0.4558 | 0.1220 | |
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| 0.3437 | 1.0204 | 1250 | 0.3720 | 1.5665 | -3.0805 | 0.9350 | 4.6470 | -173.1577 | -253.8766 | -0.4445 | 0.1325 | |
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| 0.3503 | 1.2245 | 1500 | 0.3710 | 1.5889 | -3.0515 | 0.9400 | 4.6404 | -172.8680 | -253.6525 | -0.4406 | 0.1347 | |
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| 0.3427 | 1.4286 | 1750 | 0.3697 | 1.5903 | -3.0719 | 0.9450 | 4.6622 | -173.0719 | -253.6384 | -0.4355 | 0.1387 | |
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| 0.3353 | 1.6327 | 2000 | 0.3699 | 1.5881 | -3.0875 | 0.9400 | 4.6756 | -173.2272 | -253.6602 | -0.4333 | 0.1412 | |
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| 0.3441 | 1.8367 | 2250 | 0.3695 | 1.5931 | -3.0842 | 0.9350 | 4.6772 | -173.1941 | -253.6105 | -0.4322 | 0.1424 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
