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--- |
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license: mit |
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library_name: peft |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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- trl |
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- dpo |
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base_model: microsoft/phi-2 |
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datasets: |
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- HuggingFaceH4/ultrafeedback_binarized |
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model-index: |
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- name: phi-2-gpo-renew2-b0.001-extra-v2-i1 |
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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-gpo-renew2-b0.001-extra-v2-i1 |
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This model is a fine-tuned version of [DUAL-GPO/phi-2-gpo-renew2-b0.001-i0](https://huggingface.co/DUAL-GPO/phi-2-gpo-renew2-b0.001-i0) on the HuggingFaceH4/ultrafeedback_binarized dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0388 |
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- Rewards/chosen: 0.0266 |
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- Rewards/rejected: -0.0126 |
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- Rewards/accuracies: 0.6070 |
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- Rewards/margins: 0.0392 |
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- Logps/rejected: -379.8497 |
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- Logps/chosen: -369.7509 |
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- Logits/rejected: -0.9196 |
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- Logits/chosen: -0.9539 |
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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: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 1 |
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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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| 0.098 | 0.06 | 100 | 0.0533 | -0.0029 | -0.0036 | 0.4980 | 0.0007 | -370.8433 | -399.2503 | -0.7225 | -0.8171 | |
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| 0.094 | 0.13 | 200 | 0.0491 | -0.0390 | -0.0525 | 0.5525 | 0.0135 | -419.6949 | -435.2693 | -1.0754 | -1.1388 | |
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| 0.0898 | 0.19 | 300 | 0.0452 | -0.0184 | -0.0403 | 0.5780 | 0.0218 | -407.5088 | -414.7480 | -1.0291 | -1.0858 | |
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| 0.0731 | 0.26 | 400 | 0.0430 | -0.0069 | -0.0331 | 0.5970 | 0.0262 | -400.2979 | -403.1916 | -0.9864 | -1.0412 | |
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| 0.0787 | 0.32 | 500 | 0.0422 | -0.0122 | -0.0473 | 0.6070 | 0.0351 | -414.4887 | -408.4566 | -1.0587 | -1.0975 | |
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| 0.0742 | 0.38 | 600 | 0.0406 | 0.0135 | -0.0175 | 0.6085 | 0.0309 | -384.7105 | -382.8363 | -0.9872 | -1.0246 | |
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| 0.0635 | 0.45 | 700 | 0.0401 | 0.0166 | -0.0188 | 0.6095 | 0.0354 | -386.0258 | -379.6696 | -0.9903 | -1.0225 | |
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| 0.0881 | 0.51 | 800 | 0.0395 | 0.0250 | -0.0102 | 0.6085 | 0.0352 | -377.4323 | -371.2672 | -0.9658 | -0.9975 | |
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| 0.0753 | 0.58 | 900 | 0.0393 | 0.0304 | -0.0046 | 0.5990 | 0.0350 | -371.7872 | -365.8699 | -0.9026 | -0.9456 | |
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| 0.0922 | 0.64 | 1000 | 0.0390 | 0.0286 | -0.0075 | 0.5990 | 0.0361 | -374.7669 | -367.7319 | -0.8801 | -0.9184 | |
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| 0.0703 | 0.7 | 1100 | 0.0389 | 0.0227 | -0.0161 | 0.6000 | 0.0387 | -383.3026 | -373.6226 | -0.9300 | -0.9602 | |
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| 0.0746 | 0.77 | 1200 | 0.0388 | 0.0226 | -0.0179 | 0.6050 | 0.0405 | -385.1601 | -373.7153 | -0.8944 | -0.9306 | |
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| 0.0925 | 0.83 | 1300 | 0.0387 | 0.0263 | -0.0131 | 0.6030 | 0.0393 | -380.3072 | -370.0340 | -0.9171 | -0.9494 | |
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| 0.0863 | 0.9 | 1400 | 0.0387 | 0.0269 | -0.0123 | 0.6055 | 0.0392 | -379.5608 | -369.4450 | -0.9121 | -0.9447 | |
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| 0.0904 | 0.96 | 1500 | 0.0386 | 0.0268 | -0.0124 | 0.6045 | 0.0392 | -379.6000 | -369.4944 | -0.9203 | -0.9536 | |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.2 |
